Dr Marine Jary
email: marine.jary@univ-fcomte.fr

Dewi Vernerey

Thierry Lecomte

Erion Dobi

François Ghiringhelli

Franck Monnien

Yann Godet

Stefano Kim

Olivier Bouché

Serge Fratte

Anthony Goncalves

Julie Leger

Lise Queiroz

Olivier Adotevi

Franck Bonnetain

Christophe Borg

Prognostic value of angiopoietin-2 for death risk stratification in patients with metastatic colorectal carcinoma

Keywords: CEBP Section: Biomarkers, -Omics & Systems Biology angiopoietin-2, angiogenesis, metastatic colorectal cancer, overall survival and prognostic biomarker

Impact: Ang-2 ability to predict OS at diagnosis could be of interest in the selection of patients eligible to intermittent or sequential therapeutic strategies dedicated to the optimization of patient's quality of life and chemotherapy cost-effectiveness.

INTRODUCTION

Remarkable improvement of colorectal cancer patient's survival was reported in last years, mainly due to the increasing indications of metastatic surgery and the availability of a growing number of chemotherapies and biotherapies during the course of the disease. Formation of new blood vessels is a major process allowing cancer progression and tumor spread. Several evidence showed that angiogenic molecular regulation is linked to the multistep oncogenesis leading to activation of an increasing number of angiogenic-related growth factors during the course of the disease. [START_REF] Rak | Reciprocal paracrine interactions between tumour cells and endothelial cells: the 'angiogenesis progression' hypothesis[END_REF] The impact of bevacizumab, a Vascular Endothelial Growth Factor neutralizing monoclonal antibody (anti-VEGFA), on mCRC patient's survival, confirmed the role of VEGF-dependent neoangiogenesis in this disease. In addition, the bevacizumab lower efficacy in advanced disease (beyond the second line of therapy) pointed out that the regulation of advanced mCRC angiogenesis might involve other angiogenic growth factors.

Several investigations were performed to determine the role of cancer-related angiogenesis in mCRC prognosis. Over the last decade, many seric potential prognostic factors were investigated in mCRC patients without any positive association with OS at baseline. [START_REF] Jubb | Impact of vascular endothelial growth factor-A expression, thrombospondin-2 expression, and microvessel density on the treatment effect of bevacizumab in metastatic colorectal cancer[END_REF][START_REF] Kopetz | Phase II trial of infusional fluorouracil, irinotecan, and bevacizumab for metastatic colorectal cancer: efficacy and circulating angiogenic biomarkers associated with therapeutic resistance[END_REF] The presence of alternative angiogenesis pathways promoting cancer progression was firstly suggested by the lack of efficacy of VEGF blockade in some tumor models in mice. [START_REF] Millauer | Dominantnegative inhibition of Flk-1 suppresses the growth of many tumor types in vivo[END_REF] Further studies demonstrated that Angiopoietin-2 (Ang-2), a ligand of Tie2 receptor [START_REF] Maisonpierre | Angiopoietin-2, a natural antagonist for Tie2 that disrupts in vivo angiogenesis[END_REF] was able to induce an anarchical blood vessel organization during cancer progression. [START_REF] Reiss | Switching of vascular phenotypes within a murine breast cancer model induced by angiopoietin-2[END_REF][START_REF] Falcon | Contrasting actions of selective inhibitors of angiopoietin-1 and angiopoietin-2 on the normalization of tumor blood vessels[END_REF] Preclinical studies confirmed that Other exploratory studies pointed out its potential prognostic value by the description of an association between Ang-2 and OS or PFS, in small cohorts of patients. [START_REF] Bauerschlag | Evaluation of potentially predictive markers for anti-angiogenic therapy with sunitinib in recurrent ovarian cancer patients[END_REF][START_REF] Schulz | Angiopoietin-2 drives lymphatic metastasis of pancreatic cancer[END_REF] However, the independent and additional Ang-

MATERIALS AND METHODS

Population

Individual patient data were collected from two previous prospective cohorts, both containing bevacizumab treatment in first-line mCRC patients.

The "cohort set- In both studies all patients gave written informed consent, and were followed until death.Data were anonymized. Population selection during the study is summarized in the flow chart (Figure 1).

Data extraction:

The following data were collected for each patient in the two cohorts: center and patient identification, age, sex, performance status, primary tumor site (colon, rectum), site of metastases (liver-limited, liver and other, other), time of metastasis diagnostic (synchronous, metachronous), primary tumor resection, LDH level at baseline (normal value was considered if LDH were below 350 UI/L), lymphocyte and leucocyte counts, survival status, and date of last news or death. Of note, ALP level was not available in "cohort set-2".

Ang-2 plasma sample measurement procedure is precisely described in supplementary materials (Supp methods).

Statistical analysis

We provided the mean (SD) values and frequency (percentages) for the description of continuous and categorical variables, respectively. The means and the proportion were compared using Student's t test and the chi-squared test (or Fisher's exact test, if appropriate), respectively. Due to the skewed Angiopoietin-2 distribution we used for its description the median, and the interquartile range for the dispersion measurement, as recommended.( 26) Wilcoxon rank-sum test was performed for Ang-2 distribution comparison among the cohort set.

OS was calculated from the date of study enrolment to the date of death from any cause. Alive patients were censored at the last follow-up. OS was estimated using Kaplan Meier method and described using median or rate at specific time points with 95%CI. Follow-up was calculated using reverse Kaplan-Meier estimation.( 27 Internal validity of the model was assessed by a bootstrap sample procedure.

Several approaches have been proposed to assess the performance in samples of the same population (internal validation).( 29)

Sensitivity analyses were performed for univariate and multivariate Cox models with a stratified approach on the cohort set parameter that allowed to consider the two cohort heterogeneities in the Cox modelisation.

We further focused on the improvement of one reference prognostic model for OS (GERCOR model) performance after the inclusion of Ang-2 measurement, comparing two sets of predictions for death: one based on a Cox proportional hazard model without Ang-2 parameter and one based on a model with Ang-2 parameter.

The discrimination ability and incremental value of Ang-2 level to the GERCOR score was evaluated with the use of C statistic. This analysis was repeated 1,000 times using bootstrap samples to derive 95%CIs for the difference in the C statistic between models. We also used net continuous reclassification improvement (NRI)

and integrated discrimination improvement (IDI) to quantify the performance and the net benefit of the addition of Ang-2 to the reference model for the prediction of 48 months death probability. [START_REF] Pencina | Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation[END_REF][START_REF] Pencina | Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond[END_REF][START_REF] Pencina | Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers[END_REF] Continuous NRI has several limitations but would give a consistent message and is therefore a descriptive marker. [START_REF] Leening | Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide[END_REF] Of note, cNRI does not consider the magnitude of the change, but only the direction. This is done by the IDI. When significantly greater than 0, IDI and cNRI are in favor of a net benefit of the addition of the marker of interest to the reference model considered.

We finally investigated the possibility to provide a simple implementation of Ang-2 monitoring in clinical practice with the determination of a cut-off value by contrasting Ang-2 level distributions in boxplot among healthy volunteers and mCRC patients.

The analyses were conducted using SAS 9.2 (Statistical Analysis System, Cary, NC, USA) and R 3.0.2 (R foundation for Statistical Computing). All statistical tests were 2-sided, and probability values <0.05 were regarded as significant.

RESULTS

Population:

The characteristics of the 177 eligible patients are summarized in table 1. Fifty-one samples were available from "cohort set-1", and 126 from "cohort set-2". Patient's characteristics of these two cohorts are similar, except for sex and liver metastatic involvement. Ang-2 values were also different in the two cohorts. Of note, the statistical unbalance in metastatic sites between the two cohorts was awaited since liver metastasis was an inclusion criteria in the "cohort set-2". The rate of PS 2 was 2% in the "cohort set-1", instead of 5% in the "cohort set-2", we consequently differentiated the patients PS 0 from the patients PS > 0. There were 23 (45%), and 45 (36%) patients with liver exclusive metastatic sites. Surgery of the primary tumor was performed in 28 (57%) and 82 (65%) patients, in the cohorts set-1 and set-2, respectively. LDH values at baseline were available in 33 and 82 patients and increased upper limit normal values in 13 (39%) and 46 (56%) patients of cohort set-1 and set-2, respectively.

Ang-2 plasma level biomarker and prediction of OS

Internal validation of the final model

With the replicated datasets (n=1000) derived from the bootstrap sample procedure, uncertainties around hazard ratio estimates can be measured.

Bootstrapping results for the internal validation reflect the robustness of the final model (HR 95%CI percentile for LDH: 1.039 to 2.628 and HR 95%CI percentile for Ang-2 (log value): 1.161 to 2.263).

Sensitivity analysis

Considering the differences observed between the two cohorts (table 1), we performed a sensitivity analysis to validate the robustness of our final model with a stratified approach in the Cox modelisation.

With this approach similar results were obtained for the univariate analysis except for timing to metastases parameters, which were reported to be nonstatistically significant (Supp data table 1). The multivariate analysis confirmed that Ang-2 and LDH were two independent predictors for OS (HR=1.800; 95%IC: 1.081-2.998; p=0.0239 and HR=1.548; 95%IC: 1.028-2.331; p=0.0365, respectively).

Added value of the Ang-2 parameter for predicting Overall Survival

Then, we decided to investigate the performance improvement in OS discrimination after the inclusion of Ang-2 measurement in a reference prognostic model.

Currently, in our situation two main staging systems are available: Kohne and GERCOR scores. In our population the Kohne model cannot be calculated. However, GERCOR score displayed a better discriminative ability (C=0.64) than Kohne model (C=0.55). [START_REF] Chibaudel | Simplified prognostic model in patients with oxaliplatin-based or irinotecan-based first-line chemotherapy for metastatic colorectal cancer: a GERCOR study[END_REF] We then used the GERCOR model as the reference score.

Ang-2 was first identified as a factor independently associated with OS (HR=1.568 95%CI=1.111-2.213; p=0.0104). Then, the inclusion of the Ang-2 parameter in the GERCOR reference model significantly improved its discriminative ability since the C-statistic increased significantly from 0.61 to 0.63 (bootstrap mean difference=0.07, 95% CI: 0.069-0.077).

The integrated discrimination improvement was 0.03 (p=0.07). Similarly, the addition of Ang-2 measurement to the reference model showed a favourable trend to adequately reclassified patients at lower risk for death and those at higher risk, as reported by a continuous net reclassification improvement of 0.26 (95%CI: -0.23 to 0.75). Indeed for patients without death at 48 months Ang-2 measurement moved risk prediction in the correct (downward) direction in 11/18=61%. Conversely, patients with death indicate a correct, upward, change in risk assessment when using the Ang-2 measurement (47/91=52%). The risk reclassification analysis (IDI and NRI) results are greater than 0 and statistically border line reflecting a favourable tendency for Ang-2 parameter.

Proposal for an implementation of Ang-2 monitoring in clinical practice

After the statistical investigation and determination of the baseline Ang-2 added value for predicting OS among the conventional factors, we investigated the possibility to provide a simple implementation of Ang-2 monitoring in clinical practice.

Cut-off value of Ang-2 fixed at 5 ng/mL

Simple implementation of Ang-2 monitoring in clinical practice is first guided by the determination for a relevant cut-off in order to categorize patients into groups with low and high Ang-2 level at baseline.

A preliminary set of experiments was done by dosing the Ang-2 value in 41 healthy volunteers, blood donors in the Etablissement Français du Sang (EFS, Bourgogne Franche-Comté). These volunteers were major (more than 55 years old), having signed an informed consent and were randomly chosen. We hypothesized that this population have no active angiogenesis. Among these healthy donors, 40 (98%) had mean values lower than 5 ng/mL. This observation was in agreement with the results previously published by Goede et al where levels of Ang-2 were inferior to 5 ng/mL in the 33 healthy volunteers.

In our study population, the median value of Ang-2 was 4.045 ng/mL for the 109 patients included in the final analysis. Of note, similar results were observed when the median value was used as a cut-off. A value of 5 ng/mL was then chosen considering results provided by previous publications [START_REF] Goede | Identification of serum angiopoietin-2 as a biomarker for clinical outcome of colorectal cancer patients treated with bevacizumab-containing therapy[END_REF][START_REF] Schulz | Angiopoietin-2 drives lymphatic metastasis of pancreatic cancer[END_REF] regarding that 98% of healthy volunteers have baseline plasmatic levels of Ang-2 below 5ng/mL. In consequence, in order to investigate a cut-off value for clinical use, a level of 5 ng/mL seemed to be a relevant choice (Figure 2).

Phenotypic characterisation of patients with high and low Ang-2 levels

The determination of 5 ng/mL as a cut-off value allowed us to classify patients into two groups: low (<5 ng/mL) and high (>=5 ng/mL) Ang-2 level group at baseline.

Among the 109 patients involved in the final analysis, 40 (36.7%) had levels of Ang-2 above 5 ng/mL. The clinico-biological characteristics according to Ang-2 value are summarized in table 3. Increased Ang-2 level was associated with enhanced LDH levels (77%, p< 0.0001), metastatic sites (63% liver and other, p=0.04515), and synchronous metastases (92%, p< 0.0001). Patients with high Ang-2 levels exhibited more surgery of the primary tumor, increased leucocytes count and lymphopenia.

As expected, we noted a significant difference for the GERCOR prognostic score distribution among groups of patients with low and high Ang-2 levels (42%-38%-20% vs 7%-48%-45%; p=0.0002 for low, intermediate and high risk GERCOR group, respectively).

Kaplan-Meier curves for OS by combining Ang-2 binary information and conventional score

Considering the added value of Ang-2 measurement for OS risk stratification among conventional factors, previously described, we investigated the interest for a combination of Ang-2 simple binary information and GERCOR prognostic score in clinical practice.

In our population, 30 (59%) and 79 (63%) patients are eligible for the GERCOR score calculation among the 51 and 126 patients in the cohort set-1 and 2, respectively (Fisher-exact test p= 0.7332). In total, data were available to assess the GERCOR prognostic score in 109 patients. This risk model identified 32 patients at "low-risk", 45 patients at "intermediate-risk", and 32 patients at "high-risk" level. In our study, the median OS was 27.

DISCUSSION:

The present results, consistently with previous studies, confirm that Angiopoietin-2 is a biomarker of interest for OS prediction in non-previously treated mCRC patients. [START_REF] Goede | Identification of serum angiopoietin-2 as a biomarker for clinical outcome of colorectal cancer patients treated with bevacizumab-containing therapy[END_REF][START_REF] Kim | Bifractionated CPT-11 with LV5FU2 infusion (FOLFIRI-3) in combination with bevacizumab: clinical outcomes in first-line metastatic colorectal cancers according to plasma angiopoietin-2 levels[END_REF] The main interest of Ang-2 as a biomarker is its ability to predict OS Population selection during the study is summarized in the flow chart.

In the left panel the population selection during the study is described.

In the right panel the derived analysis and their synthetic results are summarized. In our study population, the median value of Ang-2 was 4.045 ng/mL for the 109 patients included in the final analysis. In consequence, in order to investigate a cutoff value for clinical use, a level of 5 ng/mL seemed to be a relevant choice. Panel D shows that the determination of Ang-2 in the intermediate risk patients with GERCOR's group (n=45) reclassified these patients in two groups with similar profiles than low and high risk GERCOR's groups.

( 1 )

 1 Several medical options are currently available to treat metastatic colorectal cancer patients (mCRC) in the first-line setting ranging from chemotherapy intensification using FOLFOXIRI±biotherapies (2-3) and step-4-7) Therefore, the identification of biomarkers at diagnosis contributing to the prediction of individual mCRC patient's prognosis will be a critical step to better individualize and stratify mCRC treatments.

  VEGFR and Tie2 signalling were two independent mechanisms promoting tumor angiogenesis and cancer progression.[START_REF] Siemeister | Two independent mechanisms essential for tumor angiogenesis: inhibition of human melanoma xenograft growth by interfering with either the vascular endothelial growth factor receptor pathway or the Tie-2 pathway[END_REF] Moreover, VEGF and Ang-2 were independent biomarkers at baseline to predict survival in advanced hepatocarcinoma patients treated by sorafenib in the SHARP study.[START_REF] Llovet | Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma[END_REF] In first-line mCRC, Goede V. and colleagues proposed Ang-2 as a possible prognostic biomarker for OS at diagnosis, based on a pioneering study performed in 34 patients treated with bevacizumab and chemotherapy.[START_REF] Goede | Identification of serum angiopoietin-2 as a biomarker for clinical outcome of colorectal cancer patients treated with bevacizumab-containing therapy[END_REF] In a cohort of 51 mCRC patients treated by FOLFIRI-3 and bevacizumab we have also recently observed an association between baseline Ang-2 plasmatic levels, OS and PFS.[START_REF] Kim | Bifractionated CPT-11 with LV5FU2 infusion (FOLFIRI-3) in combination with bevacizumab: clinical outcomes in first-line metastatic colorectal cancers according to plasma angiopoietin-2 levels[END_REF]

  ) A Cox proportional hazard model was performed to estimate the hazard ratio (HR) and 95% confidence intervals for the factors associated with OS. The association of parameters at enrolment with OS was first assessed using univariate Cox analysis and then included (for those with p<0.05) in a final multivariate Cox regression model with stepwise backward elimination. When used in continuous in the Cox modelisation, Ang-2 variable had to be normalized by logarithmic transformation, considering its skewed distribution. Hazard proportionality was checked by plotting log-minus-log survival curves. Accuracy of the model was checked regarding two parameters: discrimination and calibration.(28) The predictive value and the discrimination ability of the model was evaluated with Harrell's Concordance (C)-index. One thousand random samples of the population were used to derive 95%CI for the Harrell's Concordance statistic.Calibration and goodness of fit of the model were assessed by using the extension of Hosmer-Lemeshow test for survival analysis and p-value greater than 0.1 was considered as an indicator for acceptable agreement.

  disease, as suggested by Abajo et al.[START_REF] Abajo | Identification of colorectal cancer metastasis markers by an angiogenesis-related cytokine-antibody array[END_REF] In line with this hypothesis, the production of Ang-2 within the tumor microenvironment could depend on VEGF.(44) Moreover, Ang-2 expression was shown to be correlated with colorectal cancer stages and progression.[START_REF] Abajo | Identification of colorectal cancer metastasis markers by an angiogenesis-related cytokine-antibody array[END_REF][START_REF] Volkova | Association of angiopoietin-2, C-reactive protein and markers of obesity and insulin resistance with survival outcome in colorectal cancer[END_REF] Nevertheless the precise role of Ang-2 cancer progression need to be further clarified.In conclusion, the assessment of the Ang-2 value at baseline could guide clinicians in stratifying risk for death in first-line mCRC patients and in providing a basis for early and adapted therapeutic interventions. The determination of Ang-2 at baseline should allow death risk stratification that could also be useful in the design optimization for future clinical trials.

Figure 1 :

 1 Figure 1: Flow chart and Analysis plan of the study.

Figure 2 :

 2 Figure 2: Distribution of Ang-2 measurement value among Healthy Volunteers

Figure 3 :

 3 Figure 3: Kaplan-Meier Curves for Overall Survival

  The association of clinical, biological, and radiological parameters with risk of death in univariate and multivariate Cox regression analysis with stepwise backward elimination was performed and results are shown in table 2. We identified six

	95%CI: 1.035-1.984; p=0.0304), synchronous metastases (HR=1.46; 95%CI: 1.035-
	2.054; p=0.0308), high LDH level (HR=2.03; 95%CI: 1.373-2.988; p=0.0004) and
	leucocyte count (HR=1.12; 95%CI: 1.056-1.188; p=0.0002) (table 2).
	Two independent predictors of OS were identified by the multivariate analysis:
	LDH high level (HR=1.60; 95%CI: 1.04-2.45; p=0.03) and log Ang-2 high level
	(HR=1.59; 95%CI: 1.14-2.21; p=0.0065) (table 2).
	Final Multivariate model performance assessment
	Accuracy of the model was checked regarding two parameters: discrimination and
	calibration, which measure the ability to separate patients with different prognosis
	and to provide unbiased survival predictions in groups of similar patients,
	respectively.
	Our final multivariable Cox model exhibited good calibration (Hosmer-
	Lemeshow with deciles p=0.8) and acceptable discrimination (C-statistic 0.64;
	95%CI: 0.58-0.68).
	variables as prognostic factors for OS in the univariate analysis: Ang-2 log-value
	(HR=1.91; 95%CI: 1.492; 2.448; p< 0.0001), metastatic localisations (liver and other:
	43

HR=1.82; 95%CI: 1.305-2.527 -other: HR=0.69; 95%CI: 0.326-1.442; p=0.0003liver alone as the reference), absence of resection of the primary site (HR=1.

  1 months 95%CI: 20.2-38.4 for the low-risk, 24.3 months 95%CI: 19.3-29.5 for the intermediate-risk and 19.7 months 95%CI: 12.3-23.8 for the high-risk group (figure 3, panel A, global p-logrank=0.02).

	According to the Ang-2 value, the median OS was significantly better in
	patients with low levels of Ang-2 than in patients with high levels of Ang-2 (median
	OS=26.2 months, 95%CI: 23.2-34.5 vs 17.8 months, 95%CI: 10.1-24.1, respectively,
	HR=2.06; 95%CI: 1.37-3.09; p<0.0001) (figure 3, panel B).
	Finally, the Ang-2 binary status was combined with the GERCOR score. In low
	and high-risk patients, the Ang-2 value only triggers a trend in better discrimination of
	prognosis, without statistical significance (p=0.27 and 0.45, respectively). However,
	in the intermediate risk GERCOR group (n=45, 41%), the consideration of Ang-2
	binary status allowed us to split the population in two subsets of patients. Indeed,
	patients with low Ang-2 level had a significant better prognosis than those with high
	Ang-2 level (26.3 95%CI: 22.2-38.2 vs 17.6 months 95%CI: 7.6-29.5 p=0.02;
	HR=2.07 95%IC=1.11-3.86) (figure 3, panel C).
	Interestingly, patients with intermediate risk and Ang-2< 5 ng/mL (n=26, 58%)
	had a similar risk profile than the GERCOR low risk patients (OS=26.3 and 27.1

months, respectively). Similarly, those with Ang-2> 5 ng/mL (n=19, 42%) had a similar risk profile than the GERCOR high-risk patients (OS=17.6 and 19.7 months, respectively, figure

3

, panel D).

  The prognostic value of primary tumor resection still remains a matter of debates. It appears to be significantly associated with OS in our univariate analysis, but not in the multivariate one. Many clinical and biological investigations support a potential role of the presence of the primary carcinoma in

	were treated with bevacizumab and chemotherapy, excluding the possibility to
	analyse the predictive value of Ang-2. This question was recently addressed by
	Llovet et al in the SHARP study.(16) Ang-2 was monitored in 491 hepatocellular
	when measured at diagnosis, before treatment initiation. Low levels of Ang-2 identify a significant population (n=69; 63%) displaying an encouraging median OS of 26.2 months (HR=2.06; 95%CI: 1.37-3.09; p<0.0001). Identification of patients with a favourable prognosis at baseline might be of clinical interest to better individualize cancer therapies. Such prognostic biomarkers might enable the appropriate selection of patients eligible to intermittent chemotherapy or sequential therapeutic strategies dedicated to the optimization of patient's quality of life and chemotherapy cost-effectiveness.(5-6, 34) In our cohort, the GERCOR model allows the identification of 32 (29%) patients of good prognosis, with a median OS of 27.1 months. When considering the population exhibiting both low Ang-2 level and low GERCOR risk, no difference was established in OS, as well as for elevation of Ang-2 level and high GERCOR risk. However, considering the intermediate GERCOR subgroup risk (n=45, 41% of patients in the present study) the binary Ang-2 status succeeded in reclassifying the prognostic risk into a low (n=26, 58%) and a high risk (n=19, 42%), corresponding to those of GERCOR model. In this case, the soluble Ang-2 value appears to be of particular interest since i) the intermediate subgroup represents a consequent number of patients, and ii) the relevance of an intermediate group in clinical practice is not clear leading to considerable confusion for their management. There are some limitations in our study. First, all patients of these cohorts carcinoma patients treated by sorafenib or placebo. Baseline plasma Ang-2 was correlated to OS both in sorafenib and placebo groups, ruling out a predictive value of Ang-2 in the context of this anti-angiogenic therapy. More recently the predictive impact of Ang-2 has also been assessed in pancreatic cancer. In that study, three seric factors have been proposed as predictive for efficacy in 328 patients treated by gemcitabine with bevacizumab or placebo, including Ang-2, Stomal-cell Derived Factor-1 (SDF1) and VEGF-D.(35) Another limitation of our study is the absence of PAL recording in the "cohort set-2". Nevertheless, we did not observe any association between PAL and Ang-2 in the "cohort set-1" Moreover, the GERCOR analysis, performed on 803 mCRC patients treated by FOLFOX or FOLFIRI in the first-line setting demonstrated that LDH, number of metastatic sites and PS were the only independent prognostic factors.(24) From a statistical point of view, the assessment of models performance measures such as discrimination, calibration, and internal validation strengthen the present investigation. While the model developed here has good calibration, discrimination and robust internal validation (reproducibility), these results have to be replicated and confirmed in a prospectively recruited validation cohort, in order to ensure wider transportability and generalizability of our results. One scheming observation is the association between Ang-2 level and presence of the primary tumor. Surgery of the primary tumor was linked to a decreased probability to observe plasmatic Ang-2 in mCRC patients (76% Ang-2< 5 CAIRO II, and a recent meta-analysis suggested a prognostic association of primary tumor removal with OS in mCRC patients.(38, 40-42) The potential association of primary colorectal cancer on Ang-2 production might account for its potential detrimental effect on prognosis. Prospective studies should monitor Ang-2 production before and following primary tumor resection. The biological basis underlying the adverse role of Ang-2 also deserves further investigations. There are some evidences that VEGF is early expressed during ng/mL vs 54% Ang-2> 5 ng/mL). mCRC prognosis.(36-39) Data reported in two phase III clinical trials CAIRO and cancer progression (43) Ang-2 could be overexpressed in latter stages of the

Table 1 : Baseline Characteristics of Patients, according to the cohort set

 1

	Characteristics		Overall population (N =177)		Cohort set-1 (N =51)	Cohort set-2 (N =126)	P †
		N		N		N
	Age -years ‡			51		126
	<= 65	177	93 (53%)		27 (53%)		66 (52%)
	>65		84 (47%)		24 (47%)		60 (48%)	p = 0.9461
	Patient male sex -no. (%) ‡ 177	74 (42%)	51	28 (55%)	126	46 (37%)	p = 0.0246
	Performance status OMS ‡ 167
	0-no. (%)		91 (55%)	48	27 (56%)	119	64 (54%)
	>0-no. (%)		76 (45%)		21 (44%)		55 (46%)	p = 0.7719
	Primary Tumor site ‡	177		51		126
	Colon -no. (%)		120 (68%)		32 (63%)		88 (70%)
	Rectum -no. (%)		57 (32%)		19 (37%)		38 (30%)	p = 0.3602
	Metastases localisation ‡	177		51		126
	Liver and other -no. (%)		98 (55%)		17 (33%)		81 (64%)

Acknowledgements:

We thank Jeremy Balland for providing us data for Healthy Volonteers.

Conflict of interest:

T. Lecomte


D Racoceanu
email: daniel.racoceanu@upmc.fr

F Capron

Towards Semantic-Driven High-Content Image Analysis. An Operational Instantiation for Mitosis Detection in Digital Histopathology

Keywords: cognitive microscopy, virtual microscopy, medical ontologies, cancer grading, breast cancer, semantic reasoning, high-content image exploration

Towards semantic-driven high-content image analysis: An operational instantiation for mitosis detection in digital histopathology

Daniel Racoceanu, F Capron

Introduction

Cancer is a deadly disease and major cause of death worldwide. Every year, 7.4 million people (about 13% of the people dying worldwide [START_REF]National cancer institute[END_REF]) die because of cancer.

Breast cancer is a very common type of cancer, especially for woman, for whom it comprises [START_REF]W3C OWL web ontology language reference[END_REF].9% of all cancers [START_REF] Gonzalez-Angulo | High risk of recurrence for patients with breast cancer who have human epidermal growth factor receptor 2positive, node-negative tumors 1 cm or smaller[END_REF]. Breast cancer is also a very deadly disease. In 2013, 234 580 new cases and 40 030 deaths from breast cancer are estimated in the United States [START_REF]National cancer institute[END_REF]. The estimated new cases of breast cancer in the United States in 2013 were about 232 340 cases in female and 2 240 in male. The mortality was about 39 620 cases for female and 410 for male, with 1 woman out of 5,7 sick dying from breast cancer [START_REF]National cancer institute[END_REF]. About 1 in 8 U.S. women (almost 12%) developed invasive breast cancer over the course of her lifetime2 [START_REF]National cancer institute[END_REF]. In Europe, breast cancer is the most current form of cancer for women, with 42 000 new cases a year [START_REF] Le Naour | Un explorateur visuel cognitif (MIcroscope COgnitif-MICO) pour l'histopathologie. Application au diagnostic et à la graduation du cancer du sein[END_REF].

The last few decades, digital technologies have been emerging on the market, like high resolution histopathological slide scanners or efficient software viewers for large-scale histopathological images. Similarly, pattern recognition image processing algorithms have been shown to be quite effective and mature for medical applications [START_REF] Bamford | Unsupervised cell nucleus segmentation with active contours[END_REF]. However, these technologies are still not being intensively used in pathology departments, and will not be adopted without being properly adapted to the medical domain and practices.

Cautious in creating and respecting new protocols in digital pathology, this study proposes a novel approach in cognitive vision, combining imaging, reasoning and ontologies. The work has been done in the framework of the Cognitive Microscopy project (MICO 1 ), dealing with a cognition-driven visual explorer for breast cancer grading. The traceability of a second opinion being a key issue, the proposed system is so able to explicitly consider the visual (and contextual) medical knowledge, by indicating at each step, the rules and the ontologies used to generate the quantitative diagnosis support.

After presenting the challenge of the cognitive virtual microscopy, we analyse the problem of knowledge representation in digital histopathology, in order to further describe the effective use of reasoning, ontologies and imaging tools, in the context of breast cancer grading.

Cognitive Virtual Microscopy

Histopathology became widely accepted as a powerful exam for diagnosis and prognosis in mainstream diseases such as breast cancer. Currently, analysis of medical images in histopathology largely remains the work of human experts. For pathologists, this consists of hundreds of slides examined daily3 , a tedious repetitive work which will greatly benefit from reliable and traceable digital second opinion. Beside, the work of a pathologist is mostly based on an intensive visual micro-semiology training (at least a decade to reach a real expertise in most of specialties) and know-how, embedded in a very complex medical contextual knowledge.

In this sense, considering the cognitive part of the pathologist's work is essential when we envisage a reliable second opinion support. The Cognitive Microscope (MICO 1 ) initiative aims at developing a cognition-driven visual explorer for histopathology, focusing on the use case of mitotic count in breast cancer grading. MICO proposes a radical change in medical practice by proposing a cognition-driven medical imaging environment, enabling safer decision support in histopathology.

The system integrates image visualisation and exploration support, as the use of specific visual medical knowledge, in order to set up the bases of a cognition-driven medical imaging, able to address pervasive adaptation and physical-virtual confluence challenges for histopathology. The analysis capabilities and results are made available to the pathologist through a platform combining virtual microscopy and cognitive reasoning. This allows medical staff to interact with the platform at the appropriate level of abstraction. The platform combines multi-scale whole slide image (WSI) exploration and analysis, as medical knowledge representation and inference using ontologies.

Among the existing cognitive vision approaches [START_REF] Vernon | Cognitive vision: The case for embodied perception[END_REF], we chose to go for the symbolic one, enabling a better interaction with the users (the pathologists -in our case), a traceability for verification and quality control, as an effective maintenance capability, by using referenced on-line medical thesauri (Snomed-CT 4 , ADICAP5 etc.).

Knowledge Representation in Histopathology

Histopathology is the study of signs of disease using the microscopic examination of a biopsy or surgical specimen processed and fixed onto glass slide [START_REF] Gurcan | Histopathological image analysis: A review[END_REF]. Histopathologists often are experts able to analyse tissue samples on glass slide using light microscope to grade cancers [START_REF] Le Naour | Un explorateur visuel cognitif (MIcroscope COgnitif-MICO) pour l'histopathologie. Application au diagnostic et à la graduation du cancer du sein[END_REF]. Sample observation is important because it allows the expert to make a judgement on the state of the disease. Therefore, it helps to target the appropriate cure. Histology involves invasive procedures and is the most accurate way of grading cancers so far. The histopathological diagnosis plays also a legal role in most of advanced countries.

Accurate grading of invasive breast cancer requires good fixation, processing, section cutting, staining and careful application of grading criteria 6 .

A pathologist may have to analyse different kind of biological, cytological or histological extractions. Since the cytology is the analysis of cells and no information is kept about their spatial organisation, the histology represents the study of a tissue. There are different techniques for tissue extraction. In the case of breast cancer, biopsy, tumorectomy, mastectomy or axillary curage may be operated, depending mostly on the nodule size. After its extraction, the tissue is analysed using a microscope. Figure 1 shows an example of lesion induced by breast cancer [START_REF] Genestie | Mammary pathology[END_REF].

Breast cancer grading

In case of solid tumour, cancer diagnosis is morphological and made using human tissue samples [START_REF] Le Naour | Un explorateur visuel cognitif (MIcroscope COgnitif-MICO) pour l'histopathologie. Application au diagnostic et à la graduation du cancer du sein[END_REF]. Histological slides are extracted from these samples. Histopathologists exper-HES staining (Safran is used in some hospitals for the collagen fibers enhancement)

HE staining -classic international standard staining tise enables medical diagnosis by the observation of these histopathological slides. Figure 1 shows an example of what histopathologists may see using light microscope. Histological grading helps to categorise the breast cancer, and therefore to treat it better. Breast cancer grading is now based on international standards. For a long period of time, the most widespread grading system has been the Scarff-Bloom-Richardson (SBR) system. Recently, the Nottingham Grading System (NGS) [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with longterm follow-up[END_REF], a more reproducible variation of it [START_REF] Genestie | Mammary pathology[END_REF], has become increasingly popular, being recommended -as mentioned-by the World Health Organisation.

The NGS consists in the study on three criteria, by establishing a cancer state evaluation protocol using of the respective scores. The three analysed criteria are tubules formation, nuclear pleomorphism and mitotic count [START_REF] Going | Breast cancer grading -Nottingham criteria[END_REF]. A grade of 1 depicts a low level of cancer for the patient, while a grade of 3 means an advanced state of cancer. In the UK, in 2005, for example, about 20% of symptomatic breast cancers were grade 1, 30% grade 2, and 50% grade 3 6 . Histopathological slide analysis is a complex task requiring advanced expertise. There are some drawbacks to the current histological grading process. Some grading tasks like counting mitosis can be very tedious and time consuming, inducing delay before diagnosis. Moreover, in most of the hospitals there are fewer and fewer experts, since increasingly important quantities of histopathological slides are produced every day 3 . Furthermore, one must admit that the criteria ruling histopathological analysis or grading are not fully formally described. Actually, there is currently no way to formally double-check judgements from histopathology experts about biological objects (for instance, it is very difficult to formalise how looks a mitosis and why a particular pattern corresponds to a mitosis). This lack of formalisation might be one reason of the long training and expertise time needed for the histopathologist to become a recognised pathologist expert in his medical area. Beside, even after reaching this expertise, one pathologist need to remain polyvalent, in order to insure the reactivity of the whole Pathology service/department if needed (extemporal Reproducibility of diagnosis is also an issue either between different pathologists (cross expertise in tele-pathology), post-operation exam, or longitudinal follow-up. In all these situations, a minimum of formalism supporting the pathological demarcate will be of great use, together with a flexible use of pattern recognition tools available on the market.

Medical technologies progress

Lastly, new medical technologies have been released on the market, such as high resolution slide scanners, or large medical image software viewers. These technologies are not currently being used -as a part of the routine protocol -in hospitals. However, they might help to improve the cancer grading process.

High resolution slide scanners. may be used to scan the histopathological slides (Figure 2). Some scanners are able to digitize several slides at a time. The images produced by a slide scanner are known as Whole Slide Images (WSI). A single image is generally about 140 000 by 60 000 pixels RGB (3 bytes per pixel), that is about 8 gigapixel. Each image is usually about 1 to 2GB when compressed, and 15 to 25GB when uncompressed. Such huge images require special softwares to be manipulated. However, WSI represents an efficient way to store the slides, protecting their critical information from degradation. Slide visualisation softwares. Whereas histopathologists are using light microscope to observe the slides, slide visualisation softwares offer interesting capabilities to virtual microscopy. First, computerized view allows displaying different parts of the WSI next to each other, in order to compare them, which can be very useful (for example) during nuclear pleomorphism analysis. This live reference-structure is impossible with a microscope, were the histopathologist has to look at one part of the WSI, memorise it and then move to another part of the image, basing his/her judgement on his/her (somehow volatile) visual memory. Second, visualisation tools allow to precisely measure the size of the observed biological objects. Again, this might be useful in the case of nuclear pleomorphism analysis or mitosis candidates pre-selection. Third, the viewers are very promising for collaborative work. For instance, they allow the pathologist to add cross-annotations on the whole slide image (Figure 3), for a better collaboration and discussion between experts. Also, most of the WSI viewers platforms 7 are able to share WSI and the information associated to collab-Figure 3: Example of WSI annotations using Calopix orating pathologist, via internet. This is an important breakthrough in this field since, still in the current clinical practice, the histopathologists are sending slides to each other (for diagnosis confirmation) by postal mail. Postal mail is slow, and worse, the slide is broken in transit from time to time. Fourth, viewers are also great tools to display information from different origins. Unified information storage infrastructure, or PACS (Picture Archiving and Communication Systems)8 store general information (age, sex, medical antecedents) and other information like screening (scanner, MRI, etc.) coming from different parts of the hospital. Allowing the histopathologists to access all the information about a patient (including the WSI) from the same interface, would help them to take into account relevant contextual information while grading the WSI. Finally, software viewers may also be considered as user interfaces for image processing macros or algorithms.

Due to these new technologies, histopathology is about to significantly evolve. Some procedures will probably need to be modified or completely revised to comply with the new tools, and it is hoped that new methods will also emerge (i.e. routine telepathology [START_REF] Le Naour | Un explorateur visuel cognitif (MIcroscope COgnitif-MICO) pour l'histopathologie. Application au diagnostic et à la graduation du cancer du sein[END_REF]), in the interest of both histopathologists and patients as, after all, in the interest of the quality of healthcare. However, the right tools still need to be built, and the pathologists need to learn how to use them in clinical routine.

Proposed Digital Pathology Protocol

At the initiative of the pathology department of La Pitié-Salpêtrière Hospital, we introduced a new digital pathology protocol for pattern collection and annotation. This Whole Slide Image (WSI) computer-assisted analysis protocol uses a simple and efficient structured magnification scheme: the explored territory is sampled in 10X frames, embedding 4 frames 20X each, each of them embedding again 4 frames at 40X (See Figure 4). This will constitute a standard digital pathology protocol we are extending at the moment to a larger French consortium in telepathology (FlexMIm9 FUI project) and we are planning to extend as a standard protocol, at a larger scale, in the future, for the benefit of a faster and wide clinical adoption of digital pathology and telepathology.

Knowledge representation and reasoning 3.4.1. Semantic Web technologies

As discussed, pathology is a highly (visual) cognitive medical speciality. However, most of information is currently available in a weakly structured form. Therefore, from the knowledge management perspective, the current information systems are not fully efficient for searching, extracting, maintaining and mining information. Indeed, the keyword-based search reaches its limits, and tedious human intervention is often required to extract relevant information from documents [START_REF] Antoniou | A semantic web primer[END_REF].

Semantic Web technologies enable designing operational knowledge management systems, presenting the advantage of a large community adoption and a dynamic evolution. By organising knowledge in conceptual spaces according to its meaning, keyword based search is replaced by query answering, automated tools being so able to check inconsistencies, to extract new knowledge, and to present it in human-friendly way. Therefore, it becomes possible to extract existing knowledge or to make inferences over several documents [START_REF] Berners-Lee | Scientific publishing on the semantic web[END_REF].

A matter of meta-data

Currently, most of the information targets human readers, rather than targeting programs. For instance, most of the services, technical documentation or web pages are written to be understandable by human only. In addition to containing formatted information aimed at human, they could also contain information about their content, or meta-data. Such representation is far more easily processable by machines. Indeed, the functionalities outlined in the previous paragraph will not happen using text-based manipulation of information, but by taking advantage of machine-processable meta-data [START_REF] Antoniou | A semantic web primer[END_REF]. The term meta-data refers to such information: data about data. Meta-data capture part of the meaning of data, thus the term semantic in Semantic Web.

Ontologies

There are various definitions of an ontology. Merriam Webster's dictionary [START_REF]Dictionary and thesaurus -merriam-webster online[END_REF] defines Ontology as a branch of philosophy which studies what exists in all areas of reality. In Artificial Intelligence (AI), the meaning of ontology (with a lower-case "o") can be slightly different. A suitable definition is given by Studer [START_REF] Studer | Situation and perspective of knowledge engineering[END_REF] : an ontology is a formal, explicit specification of a shared conceptualization. A 'conceptualization' refers to an abstract model of some phenomenon in the world by identifying the relevant concepts of that phenomenon. 'Explicit' means that the type of concepts used and the constraints on their use are explicitly defined. 'Formal' refers to the fact that the ontology should be machine understandable, which excludes natural language. 'Shared' reflects the notion that an ontology captures consensual knowledge, that is, it is not private to some individual, but accepted by a group.

Concretely speaking, an ontology consists in a finite list of concepts and relationships linking those concepts, formally describing a domain. The concepts may be classes or instances, and the ontology may be viewed as a graph. Typically, the relationships include hierarchies of classes.

Ontological analysis. clarifies the structure of knowledge [START_REF]Branici)[END_REF]. A domain ontology is the ineluctable core of any system of knowledge representation of the same domain. Without it, or similar conceptualization, there cannot be a vocabulary for representing knowledge. As ontologies help to increase the efficiency and consistency of describing resources, they allow for more sophisticated functionalities in knowledge management and information retrieval for application development [START_REF]Branici)[END_REF]. Basically, the role of ontologies in the knowledge engineering process is to facilitate the construction of a domain model, by providing a vocabulary of terms and relations with which a domain can be modelled [START_REF] Studer | Situation and perspective of knowledge engineering[END_REF].

Ontology as a theory. Ontologies are content theories about the type of concepts and relations between concepts that are possible in a specified domain of knowledge [START_REF]Branici)[END_REF]. In its strongest form, an ontology tries to capture universally valid knowledge, independent of its use, a view closely related to its philosophical origin. However, AI researchers quickly gave up this view, because it turned out that specific use of knowledge influenced its modelling and representation [START_REF] Studer | Knowledge engineering: principles and methods[END_REF].

Within a given domain, an ontology is not just a representation -in a computer -of that domain but also claims to reflect a certain rate of consensus about the knowledge in that particular domain.

As all representations are surrogates for reality, and therefore distinct from it, they must be imperfect approximations of reality. Moreover, creating an ontology means selecting representations, in other words making a set of ontological commitments. These commitments of blurring or emphasising some aspects of the described objects are unavoidable (they are also useful because they allow to focus on the aspects relevant or useful to the domain application). As a consequence, a knowledge representation is a set of ontological commitments [START_REF] Davis | What is a knowledge representation?[END_REF]. Thus, it may exist several concurrent ontologies describing the same objects. It doesn't mean one is better than the others, but maybe it is indeed better for a specific application domain.

Ontology levels. Depending on their generality degree, different kind of ontologies may be identified. Among others, we may distinguish the following types [START_REF]Branici)[END_REF]:

• Upper level ontologies provide notions to which all concepts in existing ontologies are necessarily related. As a consequence, they are valid across several domains [START_REF] Studer | Situation and perspective of knowledge engineering[END_REF].

• General ontologies represent knowledge at an intermediate level of detail, independently of a specific task.

• Domain ontologies capture the knowledge valid for a particular domain. Within domain ontologies, the ontologies designed for specific tasks are called application ontologies.

Ontology design. While designing ontologies, it is a good practice to separately define assertions and terminological knowledge. Indeed, description logics traditionally split concepts and their relationships from the instances and their attributes and roles, expressed as fact assertions [START_REF] Bergman | The fundamental importance of keeping an ABox and TBox split[END_REF]. The Resource Description Framework (RDF) is a language for representing information in the Web. Its purpose is to represent meta-data about Web resources, being designed to allow information to be processed by applications, instead of viewed / analysed by people [START_REF]Resource description framework (RDF): Concepts and abstract syntax[END_REF]. RDF provides a common framework able to express this information in order to be shared between applications by keeping its original meaning. Being a common framework, application designers can leverage the availability of common RDF parsers and processing tools. The ability to exchange information between different applications means that the information may be made available to applications other than those for which it was originally created [START_REF]Resource description framework (RDF): Concepts and abstract syntax[END_REF]. This characteristic represents a fondamental property of our system, initially designed for digital pathology, but usable for a large spectrum of high-content image/spaces analysis contextes.

RDF is based on the idea of identifying things using Web identifiers (called Uniform Resource Identifiers, or URIs), and describing resources in terms of simple properties and property values. This enables RDF to represent simple statements about resources as a graph of nodes and arcs representing the resources, and their properties and values.

RDF model encodes data in the form of triples [START_REF] Bizer | Linked data-the story so far[END_REF]. Actually, the majority of data processed by machine may be described using triples [START_REF] Berners-Lee | Scientific publishing on the semantic web[END_REF]. It also enable us to translate human language into a model that machines can interpret. All sentences are sliced in triples, made of the following three entities [START_REF] Verborgh | Semantic web reasoning with eye[END_REF]: Subject: what or who we are talking about ; Predicate: what property of the subject we are discussing ; Object: what we are saying about the property of the subject. The subject and object of a triple are both URIs that each identify a resource, or a URI and a literal string respectively. The predicate specifies how the subject and object are related, and is also represented by a URI [START_REF] Bizer | Linked data-the story so far[END_REF] [START_REF] Berners-Lee | Scientific publishing on the semantic web[END_REF].

Resource Description Framework Schema

RDF properties may be thought of as attributes of resources and in this sense correspond to traditional attribute-value pairs. RDF properties also represent relationships between resources. RDF however, provides no mechanism for describing these properties, nor the relationships between these properties and other resources. That is the role of the RDF vocabulary description language, RDF Schema (RDF/S). RDF Schema is extending RDF vocabulary to define classes and properties that may be used to describe classes, properties and other resources. Such extension is required to allow the description of taxonomies of classes and properties, or to set the domain and range of properties. RDF Schema may be defined as a semantic extension of RDF [START_REF]W3C RDF vocabulary description language 1.0: RDF schema[END_REF].

RDF/S is often expressed using RDF/XML syntax. This syntax is often used to serialise a RDF graph as a XML document.

OWL

Web Ontology Language OWL is a semantic markup language designed to publishing and share ontologies on the World Wide Web [START_REF]W3C OWL web ontology language reference[END_REF]. OWL is developed as a vocabulary extension of RDF/S. OWL language provides two specific subsets. OWL Lite is designed for easy implementation and to provide a functional basic subset. OWL DL (where DL stands for "Description Logic") is designed to support the existing Description Logic business segment and to provide a language subset that has desirable computational properties for reasoning systems. The complete OWL language (called OWL Full to distinguish it from the subsets) relaxes some of the constraints on OWL DL so as to make available features which may be of use to many database and knowledge representation systems, but which violate the constraints of Description Logic reasoners [START_REF]W3C OWL web ontology language reference[END_REF].

Notation 3 & N3 Logic

RDF/ XML syntax is not very easily readable by humans. It is much more convenient to use the equivalent language Notation 3 (N3). N3 is a compact and easily readable alternative to RDF as XML syntax [START_REF] Berners-Lee | Notation3 (N3): A readable RDF syntax[END_REF].

It has been mentioned previously that knowledge models such as Resource Description Framework (RDF/S) may provide a unifying representation of richly structured data. Adding logic to these data implies the use of rules to make inferences, to choose courses of action and answer questions. This logic must be powerful enough to describe complex properties of objects. N3Logic is a first order based logic that allows rules to be expressed in an open environment. It extends RDF with syntax for nested graphs and quantified variables and with predicates for implication and accessing external resources. The main goal of N3Logic is to be a minimal extension to the RDF data model such that the same language can be used for logic and data [START_REF] Berners-Lee | N3logic: A logical framework for the world wide web[END_REF]. N3Logic uses N3 syntax and extends RDF with a vocabulary of predicates. N3 aims to do for logical information what RDF does for data: to provide a common data model and a common syntax, so that extensions of the language are made simply by defining new terms in an ontology [START_REF] Berners-Lee | N3logic: A logical framework for the world wide web[END_REF]. Since N3Logic uses first order logic, it is possible to do semantic inferences using this formalisation and a semantic inference engine. It exists several N3 compatible semantic reasoners, such as CWM 10 and EULER 11 .

Nevertheless, there are are some particularities induced by the openness of the used knowledge bases: first, whereas a closed system may be built based on a single knowledge base of believed facts, an open Web-based system exists in an unbounded set of interconnected information resources. This requires that an entity be aware of the information source and responsible for its disposition. A language for use in this environment typically requires the ability to express which document or message asserts what. That is why N3Logic offers the possibility to quote facts [START_REF] Berners-Lee | N3logic: A logical framework for the world wide web[END_REF]. Last but not least, to avoid paradoxes, N3Logic has been kept to limited expressive power: by excluding general first order negation [START_REF] Berners-Lee | N3logic: A logical framework for the world wide web[END_REF].

Rules. So far, knowledge base facts have been described. Another kind of knowledge is described in this section: the rules. Rules allow one to infer new knowledge from existing knowledge [START_REF] Verborgh | Semantic web reasoning with eye[END_REF]. Expressed using first order logic, a rule looks like:

∀x, P (x) ⇒ Q(x) (1)
The above reads "P implies Q", or in an instructive manner: "if the premise P is true, then the consequence Q is also true". It is possible to write such rules using N3.

Queries. Queries allow to extract targeted knowledge from the knowledge base. To be extracted, the knowledge must respect the constraints described within the query. Also, it is possible to choose the output format of the extracted individuals. Expressed using N3, queries take the same shape as rules, although their effect is quite different. The N3 queries premisses describe the constraints that should be respected in order for individuals to be extracted. The N3 queries consequences describe the format of the results returned by the query.

Goal of the study

The goal of this study is to contribute to the cognition-driven aspect of the MICO platform. Indeed, until now ontologies and semantic tools have been used to exploit image processing results in order to infer medical knowledge, crucial to the pathologist. The vision of our approach also integrates the driving of image exploration & analysis using 10 CWM (a forward chaining reasoner): http://www.w3.org/2000/10/swap/doc/cwm 11 EULER (a semibackward reasoner): http://eulersharp.sourceforge.net/ semantic tools. Providing a semantic profile for each algorithm, gathering knowledge about the WSI to be analysed and matched with medical knowledge, it would be convenient to infer an adapted analysis process in order to get better and faster results. Besides the scientific deliverables, the MICO initiative leads to a Proof of Concept of a semantically driven medical image analysis platform based on the basic principles described here, and which will be deployed and validated by the pathologists. This platform will provide the opportunity to validate research concepts within an integrated system in order to allow moving forward at the clinical application level.

Operational Ontology-Driven High-Content Image Analysis

Conceptual graph

The first step in devising a knowledge representation based system is to perform an ontological analysis of the field or domain [START_REF]Branici)[END_REF]. The following conceptual graph (Figure 5) is the result of a series of cross-knowledge meetings with the pathologists, involving researchers and computer scientists. It represents the premises of the structure used for the MICO semantic platform.

On the Figure 5, green and yellow colours represent concepts from histopathology, while grey and blue concepts may belong to imaging domain. Because there are overlaps between domains, the colour legend is approximative. The design of such conceptual graph shows the difficulty of merging views of experts coming from different domains within the same theory. This finding is responsible for some of the motivations going towards a split of MICO ontology into several ontological domains (Cf. Section 4.4.2).

Processes definition

Nottingham criteria evaluation on an histopathological slide image is a tedious and time consuming task for histopathologists. We address these issues and proposes a new approach for Nottingham grading, aiming at making it faster and more reliable, thanks to expert knowledge augmented computations. This section deals with proposals for Nottingham criteria automation. Nottingham criteria automated evaluation requires heavy image processing computations. However, it is not possible to compute the needed algorithms on a whole slide image within reasonable time. Strategies are proposed to overcome the processing power limitations. As the histopathology expert doesn't need to watch every part of the whole slide image in order to grade it, MICO aims to understand and reproduce this expertise during automated grading.

Mitosis analysis strategy

Mitotic score is one of the three Nottingham criteria. This score is quite easy to compute once the highest mitosis density within the histopathological slide is known. This section describes the digital strategy proposed for highest mitosis density evaluation without exhaustive analysis of the whole slide image (Fig. 6).

The digital protocol starts by a territory corresponding to the invasive area to be graded. This relevant territory is split into 40X frames (see Section 3.3). In the tumour, for the mitotic score, we are looking for the area having the highest concentration of mitotic nuclei. To perform this search faster, we operate a sampling on the set of frames covering the tumour (see Fig. 6): the frames are grouped into blocks of 3×3 frames. In each 3×3 block, only the top left frame is analysed for mitosis detection. This sampling of one frame analysed out of nine gives us a broad picture of the concentration of mitosis in each area of the tumour. To select the area having highest number of mitosis, we now consider blocks of 4×4 frames such that the frames at each corner of a block have already been analysed for mitosis detection. We compute the sum of mitosis detected so far in each 4×4 block, and we select the block (or the blocks) having highest number of mitosis for further analysis.

In this block of 16 frames, the four frames at the corners of the block have already been analysed. We now proceed to detection of mitosis on the 12 remaining frames of the block. So all the 16 frames of the block are now analysed. According to Nottingham Grading System, mitotic count is given for 10 adjacent frames. To comply with this definition, we select from our block of 16 frames the 10 frames with highest number of mitosis. The resulting mitotic count for the tumour territory will be equal to the sum of mitosis detected in these 10 frames. Of course, in case we had analysed more than one 4×4 block, the final mitotic count will be the highest one among all the 4×4 blocks so analysed. This strategy gives an overview of what should be done to achieve a clinically acceptable mitosis score approximation. However, more details are needed for its realisation, like the different steps of the strategy, and the kind of data exchanged to go from one step to another. Therefore, mitotic scoring flow has to be split in modular algorithms, allowing a precise description in terms of inputs and outputs, in a more accurate manner.

Algorithms definition

Several modular algorithms are designed from the mitotic count strategy as a first step towards a cognitive platform realisation. In order to insure a broad usability of these modules, a particular attention is paid to the definition of the algorithms interfaces. Because its convenience for a semantic platform (Cf. section 4.4.3) and because its self-described caracteristics, easy to read and to maintain [START_REF] Bray | Extensible markup language (XML)[END_REF], Extensible Markup Language (XML) is chosen as the modality for information exchange with the modular algorithms. In addition of input output oriented design and well formed XML data samples, Document Type Declarations (DTD) are provided as definition of the grammar driving exchanges with the algorithms.

Seven algorithms are needed in the use case of the mitotic score analysis of an histopathological whole slide image:

TerritoryExtractor Extracts a labelled polygon from Calopix WFML file.

FrameGenerator Cuts a polygon into frames.

FrameSampler3x3 Outputs a sample of a given frame matrix (stereology) (Fig. 7).

FrameSelector4x4 Selects a 4x4 frames area from a given frame matrix according to the frames characteristics.

FrameSelector10 Selects ten frames from the 4x4 frame matrix according to the frames characteristics.

MitosisExtractor Extracts the mitosis of a given frame. This module is the most complex imaging module in the given example. The algorithm used here is issued from a recent quasi-exhaustive state of the art of our team [START_REF] Irshad | Methods for nuclei detection, segmentation, and classification in digital histopathology: A review ? current status and future potential[END_REF] as the PhD work of Humayun Irshad [START_REF] Irshad | Automated Mitosis Detection in Color and Multi-spectral High-Content Images in Histopathology: Application to Breast Cancer Grading in Digital Pathology[END_REF] (Fig. 8).

ScoreCalculator Provides a mitotic score by aggregation of the mitotic count from the selected ten adjacent frames. A semantic approach MICO aims at supporting histopathologists during the cancer grading process, by taking benefit of the last medical and image processing technologies, while relying on histopathology experts' knowledge. Image processing algorithms allow fast image analysis and are expected to provide reliable and reproducible results. Hence, the use of such algorithms should help to make the grading process faster. MICO platform is also expected to reuse the best expert strategies in order to improve the grading process. NGS is to be implemented into the platform, as the non formally defined expert knowledge required to efficiently operate the same criteria analysis. Histopathological knowledge obviously plays a predominant role in our platform. Therefore, particular attention is given to the use of proper knowledge modelling and inferences technologies. Moreover, the issue of the impact of the knowledge on the platform behaviour is also addressed. How to ensure the knowledge has the position it deserve within the platform? This is the idea behind the proposed semantic approach: "Ontology at the helm". Indeed, MICO platform is built to be driven by the knowledge: each of MICO platform action should be induced by a decision, and each decision should be made within a formal knowledge based world. Such platform architecture offers several benefits like the improved traceability of decisions, the ability to be understood by the user, the ability to support him, a good flexibility, an improved technology acceptance and smarter use of image processing algorithms.

Traceability Medical tools must obey to strict regulations: because MICO platform is aimed to help histopathologists to take decisions by providing statements about medical cases, its decisions should obviously have some kind of traceability. Since semantic reasoning takes place in a formal world, each inference is proven: as a consequence, each decision is proven, and the proof trace may be viewed if necessary.

System understanding & Decision support MICO is definitely not aimed to replace the histopathologists, but to support them while taking important decisions, as to help them doing tedious and time consuming tasks. Thus, after the execution of low level tasks and inferences, the system should communicate relevant results to the expert, using suitable modality and formalization. These result should be human understandable and should be expressed with concepts from histopathology experts theory of histopathology, or histopathology domain ontology. Indeed, putting the user in the loop requires him to have a good understanding of what the system is doing. This may be archived by making sure expert and system use the same language, i.e. the expert activity language. MICO should support histopathologists by providing relevant information at the right time and using the right form, in order to minimize the effort needed to interpret the message and make higher level decisions.

Flexibility With a full semantic approach, all the facts and processes are expressed in an open manner. They are also fully described and therefore easily understood. A good understanding of the system knowledge is a first step toward its modification.

Since the histopathologist user is not a computer scientist, semantic technologies only may allow him to modify the system knowledge. Compared to "hard coded" imaging systems, semantic systems are more flexible and easier to maintain.

Technology acceptance As mentioned previously, the use of semantic web technologies may help the user to understand what the system truly does, and therefore increase its perceived ease of use. In order for the system to take benefit of expert knowledge, it must be build in close collaboration with them. Communication with the user helps to understands his real needs, and to involve him in the project. By increasing the system perceived ease of use and its perceived usefulness, this approach will probably help the user to accept and use the technology.

Operational image processing Image processing algorithms might be effective, but they may also need quite a lot of processing power. Therefore, it may not be possible to compute a whole histopathological slide image (up to 25GB uncompressed) within reasonable time. A smarter approach is obviously needed, in order to grade the image without having to analyse every part of it. After all, the expert doesn't need to have a close look at every part of the slide in order to grade it. As a matter of fact, the histopathologists only use the 40X detailed view on very small parts of the slides. These analysis spots are selected using slide overview magnifications. Similarly, expert knowledge could be used to guide image processing algorithms, to target interesting spots in order to spare as much processing power as possible, and to make the overall grading faster.

Overview

MICO platform is composed of two main components: a semantic core, and a set of image processing algorithms. The core is made of an ontology interacting with a semantic inference engine: it may be considered as a glass box. On the contrary, image processing algorithms are binaries, and may be considered as black boxes.

Semantic core

MICO semantic core is the place where all the decisions are taken, and also the place where all the platform knowledge is held. It is made of two main components: a semantic reasoning engine (EULER) and an ontology. While EULER is in charge of the decisions, the ontology contains several layers of structured information about both image processing and histopathology domains. MICO semantic core is maybe the most important part of the platform, at least in regards to the semantic aspects of the project. It always works the same way, following a simple three steps life cycle, as illustrated in Figure 10.

First step During the knowledge acquisition step, external information is aggregated into the core ontology. It usually comes from MICO modules, and may be the result of image processing programs execution. If MICO semantic core is being launched for the first time, then a whole knowledge base is generated from seed files.

Second step Thanks to the inference engine, new knowledge may be discovered from existing facts, and queries are launched to select the knowledge relevant to further processing or user feedback.

Third step

The last step of the MICO semantic core life cycle consists in the execution of the orders revealed in the previous step using queries, and the orders execution acknowledgement. Typically, an order is the parametrized launch of an image processing algorithm. A notable particularity of MICO semantic core is the way it manages knowledge. Indeed, some knowledge management systems or expert systems work with a frozen state knowledge base: once they are designed and developed, their knowledge base stays more or less the same. MICO semantic core approach differs in the sense its knowledge base is made to evolve with time: each time new knowledge is discovered thanks to inferences or programs outputs, it is incorporated into the knowledge base. As a result, the same inference never has to be made more than one time. Also, the concepts defining a knowledge state may be easier to identify, access, share, and link with external ontologies. Knowledge relevant to the user may be easier to monitor. Finally, it is a first step towards rules generation. However, there are drawbacks to being so exhaustive. Since the knowledge base grows with its use, it might become to large for the system to process it in reasonable time. In such a case, either the "useless" items should be removed, or an adapted database system should Ontologies. The ontology is the place where all the platform knowledge is stored. MICO ontology is actually made of several ontologies describing different topics. Each ontology is made of three kind of components: the structure, the individuals, and the rules. The individuals may be initialization knowledge, acquired or inferred knowledge: they compose the ontology A-Box. On the contrary, structure definition or rules belong to the ontology T-Box. MICO ontology is written using Notation3 and is paired with EULER semantic inferences engine. The current section describes the ontology structure, its logical rules and queries.

Overall structure Several kind of information are described into MICO ontology. For instance, a description for the initialization context is needed to start the processing of a new whole slide image. Similarly, a description of the information outputted by the image processing algorithms is needed to be able to interpret it when it comes up. Also, histopathologists vocabulary should be described to have the ability to provide results of the same language. Thus, the ontology should contains concepts from image processing domain to communicate with the image processing algorithms, and concepts from medical domain to exchange with histopathology experts during the grading process.

During the early steps of the project, it has been empirically found out it can be tough to merge or to unify concepts coming from different domains within the same ontology. Because such concepts may not perfectly overlap, and because people representation of their domain is tightly linked with the same domain traditional denominations, such compromise is complex (and maybe not reasonable ?) to achieve. On this matter, MICO approach is to consider every expert conceptualization as legitimate: experts using the platform should have the right to keep their habits wile defining and using their expertise. That is why it is preferred to allow theories coming from different domains to coexist, even if they describe the same object. In such a case, translation rules should be written to give the system the ability to convert a knowledge from one ontology to another (Figure 11). There is another reason to argue in favour of "splitting" the knowledge into several ontologies: it is about the difficulties induced by the evolution of image processing algorithms interfaces. Indeed, the input and output of these algorithms have been evolving and will probably continue to evolve. It would be interesting to minimize as much as possible the impact of this evolution on the platform, in particular on the analysis rules validity. To protect the reasoning rules from the algorithms format evolution, a distinction is made between the algorithms returning raw data and the higher knowledge level induced by it. This distinction implies conversion rules, to convert raw data to higher level knowledge of the same domain. Therefore, changes of an algorithm output format would only impact the description of its output and the conversion rules to convert the same output to higher level knowledge. The reasoning or conversion rules operating on higher level knowledge would not be impacted, because the used high level ontology structure would not be altered after modifications of algorithms output format. Thus, the platform would not be strongly affected by image algorithms input and output format evolutions.

To sum up about the two kind of separations between ontologies: as illustrated on the Figure 11, ontologies coming from different domains are all legitimate, and their existence is allowed in MICO platform. This is the case for the medical domain and the image processing domain. Within the same domain, the use of ontologies having different levels of abstraction is recommended, in order to make a proper distinction between changing data and the meaning of these data. This separation is indeed one of the good practices of AGFA Healthcare R&D. According to AGFA: high level ontologies are called Domain Ontologies (DO) and low level ontologies are called Data Definition Ontologies (DDO).

The rules may have different purposes in the platform. Some rules aim to infer knowledge from measurements while others aim to convert knowledge from one theory to another, or to infer new knowledge from existing knowledge. According to the AGFA terminology, the rules allowing to translate knowledge from one ontology to another are called Conversion Rules, while the rules allowing to populate an ontology using knowledge from the same ontology are called Analysis Rules. The Listing 1 is a rule example from the developed platform prototype. This particular rule aims to detect when it is possible to launch a territory extraction analysis on an histopathological whole slide image WFML file. If the platform context fit the rule requirements, then an order is published in the ontology. Each order contains information about both the image processing algorithm to be launched (in this specific case, a territory extractor algorithm), and the parameters to be provided as algorithm input. These parameters are generated from ontology content. The queries take the same shape as the rules, even if their purpose is quite different. They are used to extract relevant information from the knowledge base. The extracted relevant individuals may be results to be displayed to the user or orders aiming to launch image processing algorithms. In the developed platform, the queries are typically used to isolate algorithm launch orders. The Listing 2 is a query example from the developed platform prototype. This particular query aims to detect when it is opportune to launch a territory extraction analysis on an histopathological whole slide image WFML file. In this example, it is chosen to actually launch the algorithm when the known information allows its launch and it has not already been launched with the same context.

Modules

MICO platform is made of both a semantic core and image processing algorithms. Since the image processing algorithms are not aware of any conceptualization domain, they are not able to directly communicate with the semantic core. And because they are generic tools and could be applied on different contexts, they should not be tight to a specific domain. Therefore, they should not interfere with non generic high level concepts. That is why a conversion layer is needed to allow communication between the generic image processing algorithms and the formal world describing the application domain, or MICO ontology. The combination of an image processing algorithm and its conversion layers is called a MICO module (Figure 12). Each MICO module conversion layer is actually made of two layers. The first layer (next to the image processing algorithm) is an XML interface. Such XML layer allows a first description of the algorithms input and outputs. The second layer is made of both a XML ↔ Notation3 converter and a semantic profile. For instance, when an algorithm produces data, it is first expressed using XML. Then, an XSL semantic profile is used by an XSLT engine to convert the XML to Notation3. The N3 is finally incorporated into MICO knowledge base. Such conversion from raw data to low level knowledge can be achieved because the algorithms outputs are highly described, highly contextualized. Because a data without context has no meaning, each algorithm measurement (output) contains various meta information like the measurement location, date, time, the kind of measurement, the method used, etc. Indeed, these meta information are crucial to make a proper N3 modelling. Thanks to the modules conversion layers, communication is possible between black boxes (the image processing algorithms) and white boxes (the semantic core).

Conclusion

The researches presented in this paper represent a considerable step towards a clinically viable digital pathology. The objective of our study is to make histopathological WSI A working prototype has been proposed for the use case of a WSI, focusing on cancer grading, and in particular, to the mitotic count. The implemented analysis is compliant with the Nottingham Grading System, supported by the World Health Organisation.

The prototype realised is composed of two main components: a semantic core, and a set of image processing algorithms. Generic image processing algorithms have been designed for the our MICO platform. They have been developed by different members of our team. A semantic description of each algorithm has been written, and they have been incorporated into the platform. MICO semantic core has also been designed and developed. Two main components have been implemented inside the core: a semantic reasoning engine and an ontology. While the semantic reasoning engine is in charge of the platform decisions, the ontology contains several layers of knowledge about both image processing and histopathology domains.

Being a very ambitious challenge, leading to a potential breakthrough in imaging, this work has also the meaning of its genericity. Indeed, the approach is applicable for number of additional challenges, related, for example, to cytology, biology, molecular imaging and, in general, to a large set of high-content high-throughput automatic screening approaches.

Figure 1 :

 1 Figure 1: Examples of histological images of breast lesion.

Figure 2 :

 2 Figure 2: Slide digitisation process

Figure 4 :

 4 Figure 4: Collection and WSI exploration model for digital histopathology

  The concept split is known as the T-Box (terminological knowledge) and represents the schema or taxonomy of the domain at hand. The T-Box is the structural and intentional component of conceptual relationships. The second split of instances is known as the A-Box (assertions) and describes the attributes of instances, the roles between instances, and other assertions about instances regarding their class membership with the T-Box concepts. The T-Box is where the reasoning work occurs and the A-Box is where assertions occurs: While T-Box operations are based more on inferencing or verifying class memberships in the hierarchy, A-Box operations are more rule-based and govern fact checking.

Figure 5 :

 5 Figure 5: MICO conceptual graph {illustrative simplified example}

Figure 6 :

 6 Figure 6: Mitosis count flow

Figure 7 :Figure 8 : MitosisExtractor 4 . 4 .

 7844 Figure 7: FrameSampler3x3 -module-actor of the setreology process

Figure 9 :

 9 Figure 9: MICO semantic platform overview

Figure 10 :

 10 Figure 10: MICO semantic core life cycle

  Figure 11: MICO ontologies

Figure 12 :

 12 Figure 12: A MICO Module

www.breastcancer.org

For exemple, at the Pitié-Salpêtrière Hospital, Paris, about 1500 slides need to be examined daily by the Pathology Department.

SNOMED Clinical Terms (Snomed-CT): ihtsdo.org/snomed-ct

French Association for the Development of the Informatics in Cytology and Anatomo-Patholog (ADI-CAP): adicap.asso.fr

[START_REF] Gurcan | Histopathological image analysis: A review[END_REF] cancerscreening.nhs.uk.

CaloPix is an example of visualisation and data structure platform and Teleslide, a server application allowing sharing expertise and collaborating annotations on WSI : http://tribvn.fr/

i.e. AGFA Healthcare' PACS http://www.agfahealthcare.com/global/en/main/

FlexMIm project: https://www.comp.nus.edu.sg/~danielr/projects.htm

Acknowledgment

This work was performed with the support of the project MICO COgnitive MIcroscope: a cognition-driven visual explorer for histopathology for application to breast cancer grading, a project supported by ANR the French National Research Agency, program TecSan 2010 ANR-10-TECS-015.

We would like to acknowledge the whole MICO consortium for its creative dynamics and in particular Jos De Roo from AFGA Healthcare Semantic Web R&D department in Ghent, Belgium, as the fellows from the Image & Pervasive Access Lab, Singapore, in particular M. Olivier Morere and Dr. Ludovic Roux.