Yogesh Karpate

Olivier Commowick

Christian Barillot

PROBABILISTIC ONE CLASS LEARNING FOR AUTOMATIC DETECTION OF MULTIPLE SCLEROSIS LESIONS

This paper presents an automatic algorithm for the detection of multiple sclerosis lesions (MSL) from multi-sequence magnetic resonance imaging (MRI). We build a probabilistic classifier that can recognize MSL as a novel class, trained only on Normal Appearing Brain Tissues (NABT). Patch based intensity information of MRI images is used to train a classifier at the voxel level. The classifier is in turn used to compute a probability characterizing the likelihood of each voxel to be a lesion. This probability is then used to identify a lesion voxel based on simple Otsu thresholding. The proposed framework is evaluated on 16 patients and our analysis reveals that our approach is well suited for MSL detection and outperforms other benchmark approaches.

INTRODUCTION

Multiple Sclerosis (MS) is an acquired inflammatory, demyelinating disease of the central nervous system. MS is a major cause of disability in young adults prevalent in the northern hemisphere. Automatic detection and segmentation of MS lesions (MSL) from brain magnetic resonance images (MRI) can help to assess the progression of the disease and to evaluate the effectiveness of drug therapy. Automatic MSL detection and recognition is a topic of great importance and still challenging. Although manual lesion detection by experts is the Gold Standard, the objective evaluation of lesion becomes difficult for the radiologist when the number and the resolution of MRI sequences get larger. Consequently, several studies investigated the automatic/semi-automatic segmentation of MSL using multi-channel MR images [START_REF] Lladó | Segmentation of multiple sclerosis lesions in brain mri: A review of automated approaches[END_REF]. One breed of MSL segmentation includes Gaussian Mixture Modeling (GMM) on multispectral MRI, where each multivariate Gaussian probability density function represents a tissue, e.g. cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM). The GMM enables characterization of the image intensities with a reduced number of parameters, which are estimated by a maximum likelihood estimator (MLE). For example, some authors assessed lesions as outliers [START_REF] Van Leemput | Automated segmentation of multiple sclerosis lesions by model outlier detection[END_REF][START_REF] García-Lorenzo | Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis[END_REF]. Spatial decision forests for MSL segmentation were also investigated [START_REF] Geremia | Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images[END_REF]. A probabilistic framework for segmentation of gadolinium-enhancing lesions was developed using conditional random fields [START_REF] Karimaghaloo | Automatic detection of gadolinium-enhancing ms lesions in brain MRI using crf[END_REF]. The performance of automatic segmentation is constrained by partial volume effects and noise artifacts. Manifold based learning methods were used to characterize pathological deviations from Normal Appearing Brain Tissues (NABT) [START_REF] Kadoury | Manifold-constrained embeddings for the detection of white matter lesions in brain MRI[END_REF][START_REF] Batmanghelich | Multiparametric tissue abnormality characterization using manifold regularization[END_REF]. However, manifolds have huge computational complexity and do not scale to large data easily. We propose to use simple intensity features extracted from multi-parametric MRI for lesion detection. The contributions of our work are two-fold:(1) to build an automatic, probabilistic framework to discriminate NABTs and MSLs based upon simple image representation i.e. bag of words features. (2) A probability map is generated from the classifier and used to guide detection based on Otsu thresholding. Note that our framework is closely related to binary classification between two classes (NABT and MSL) of observations. There exists subtle differences, though: binary classification is a symmetric setting for discrimination between two sets whereas we are interested in addressing the asymmetric problem of finding novel instances in one set relative to another. We focus on the form of the problem in which training samples (NABT) without anomalies (MSL) are provided, and we calculate lesion score (anomaly scores) for test data. The paper is organized as follows. In section 2, the framework for MSL detection is presented. Section 3 gives the details of classifier, development of probability score and detection based on probability score. Results and conclusion of the proposed methods are presented in sections 5 and 6.

METHODOLOGY

Framework

We introduce a novel framework to identify MS lesions, as illustrated in Figure 1. It is based on two main stages. The first stage consists of two parts: (1) extraction of feature vector from multi-channel MRI and its dimensionality reduction using PCA; (2) training a classifier as described in current and next sections. The second stage consists of testing patches from the patient image by applying the learned model. The feature designing from the patient patches is the same as mentioned above. Testing is performed by doing full search over an image by placing a patch at every voxel.

Probabilistic Classification

We consider training data formed at each voxel by stacking local intensities from multiple modalities inside a surrounding patch of N voxels. To reduce the dimensionality of each vector, PCA is performed on these vectors, leading to a train-

ing set D = (x i , y i )|x i ∈ d , y i ∈ (-1, 1) N i=1
where y i is either -1 or +1, indicating the class to which the point x i belongs. Each x i is a d-dimensional real vector. The objective of probabilistic classification is to learn the class-posterior probability p(y|x) of the training samples D. Based on the class-posterior probability, classification of a new sample x can be carried out y := arg max y∈{-1,+1} p(y|x) with confidence p( y|x). One approach to tackle this problem is Least Squares Probabilistic Classification (LPSC) [START_REF] Sugiyama | Superfast-trainable multi-class probabilistic classifier by least-squares posterior fitting[END_REF]. It employs the linear combination of kernel functions and its parameters are learned by regularized least-squares fitting of the true class-posterior probability. For y ∈ {-1, +1}, p(y|x) can be estimated as follows

p(y|x) = q(y|x, θ) j∈{-1,+1} q(y|x, θ j ) (1)
Where p(y|x) is modeled with q(y|x; θ y ) = Further θ y can be estimated analytically as

θ y = Φ T Φ + ρI B -1 Φ T π y (2)
where I B denotes B dimensional identity matrix. In Equation 2, the effect of increasing the size of parameter ρ is both to regularize and to decrease the sensitivity to outliers. In order to estimate lesion probability of a test patch, we do not need to do any extra parameter estimation. Φ = (φ(x 1 ), ..., φ(x N )) T ∈ N ×B is the design matrix π y is N dimensional class indicator variable defined as π y,n = 1 if y n = y and zero otherwise. Finally, a posterior is obtained by normalizing over all classes as shown in Equation 1. LPSC is a consistent estimator and is very fast to compute in practice, finding a global optimum for θ in a single step.

LESION DETECTION MODEL

The framework explained in Section 2 was extended to one class learning in [START_REF] Quinn | A least-squares approach to anomaly detection in static and sequential data[END_REF]. The basic premise holds an assumption that MSL occupy low-density regions of the data space and that a kernel model can be used to characterize the highdensity regions of NABT. We consider the case where MSL are not present in training data but present in the test data. Let y = {-1, +1} be the NABT and MSL class respectively. The task of MSL detection is to assign a value to the estimate p(y = +1|x) for test data x given training data.

The conditional probability of MSL p(y = +1|x, θ) with

q(y = +1|x, θ +1 ) = 1 -θ T -1 φ(x) is computed as q(y = +1|x, θ +1 ) = 1 -q(y = -1|x, θ -1 ) (3)
This can be estimated as discussed in Section 2. The parameters learned to model the NABT can therefore be used for MSL detection.

Aggregate Probability Score

Since the probability map generated by the classification output scores of LPSC is noisy, we adopted the technique from [START_REF] Reddy | Confidence guided enhancing brain tumor segmentation in multi-parametric MRI[END_REF] to rectify the probability score obtained from the classifier by smoothing it using a 3D Gaussian. For each voxel, its probability score is propagated to the neighborhood by using isotropic Gaussian kernel. It produces a weighted average of each voxel's neighborhood, with the average weighted more towards the value of the central voxels. The Gaussian kernel for a voxel has a zero mean and standard deviation defined by the probability score of that particular voxel.

Thresholding Guided Detection

Automatic thresholding is used in final lesion detection. The basic idea is to automatically select an optimal probability threshold value for separating MSLs from the NABT based on their probability distribution. A common thresholding technique, the Otsu method [START_REF] Otsu | A threshold selection method from gray-level histograms[END_REF], provides satisfactory results for thresholding an image with a histogram of bimodal distribution. This method, however, fails if the histogram is unimodal or close to unimodal. We used revised Otsu method [START_REF] Ng | Automatic thresholding for defect detection[END_REF] for selecting optimal threshold values for both unimodal and bimodal distributions. Figure 2 shows the pictorial representation of the proposed framework.

EXPERIMENTS

Whole-brain MR images were acquired on 16 MS patients and 20 controls. T1-w MPRAGE, T2-w and FLAIR modalities were chosen for the experiment. Expert annotations of lesions were carried out by an expert radiologist on all MS patients. The volume size for T1-w MPRAGE and FLAIR is 256 × 256 × 160 and voxel size is 1 × 1 × 1 mm 3 . For T2-w, the volume size is 256 × 256 × 44 and voxel size is 1 × 1 × 3 mm 3 . All images were acquired on a 3T Siemens Verio (VB17) scanner with a 32-channel head coil. MR images from each patient are de-noised [START_REF] Coupé | An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images[END_REF], bias field corrected [START_REF] Nicholas | N4ITK: improved N3 bias correction[END_REF] and registered with respect to T1-MPRAGE volume. All images are processed to extract the intra-cranial region. We built a geometrically unbiased atlas for each sequence from the controls [START_REF] Guimond | Average brain models: A convergence study[END_REF][START_REF] Commowick | Automated diffeomorphic registration of anatomical structures with rigid parts: Application to dynamic cervical MRI[END_REF]. The atlas is considered as the reference image to which all controls and patients images were aligned using intensity normalization [START_REF] Karpate | Longitudinal intensity normalization in multiple sclerosis patients[END_REF].

Experimental Setup

NABT patches were collected from 20 healthy volunteers. The trained classifiers were tested on MS subjects. The features are intensity values extracted from 3 3 patches. The concatenated feature vector was formed using voxel values by extracting patches from FLAIR, T2-w and T1-w MPRAGE. The dimensionality reduction of feature vector was done using PCA. The number of components were decided by keeping 90% of the total variance. In this experiment, we searched regularization parameter ρ and Gaussian kernel width σ over a range of 10 [-3:1 :2] and {m/10, m/5, m/2, m, 3m/2} respectively, where m := median { x i -x j } N i,j=1 . We chose the best ρ according to the validation set. We compared results with other benchmark methods:One Class SVM (OSVM) [START_REF] Schölkopf | Estimating the support of a highdimensional distribution[END_REF] and the Minimum Covariance Determinant estimator (MCD) [START_REF] Rousseeuw | A fast algorithm for the minimum covariance determinant estimator[END_REF]. OSVM is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set. The MCD estimator is a robust, high-breakdown point estimator of covariance. Assuming that the NABT are Gaussian distributed, it will estimate the inlier location and covariance in a robust way. The Mahalanobis distances obtained from this estimate are used to derive a measure of MSL. For OSVM, the Gaussian width is set to the median distance between samples, which has been shown to be a useful heuristic [START_REF] Schölkopf | Estimating the support of a highdimensional distribution[END_REF]. We report the results for ν = 0.2 for OSVM. For MCD, the amount of contamination of the data set is assumed to be 0.30.

RESULTS AND DISCUSSION

We report the quantitative improvement for identification of lesions: Table 1 presents the precision (Positive Predicted Value) and recall (Sensitivity) of lesion detection averaged across the 16 patients for various overlap thresholds. The lesion is said to be detected if Rc∩R GT R GT ≥ ϕ where R c , R GT and ϕ are respectively the candidate region in the image, the ground truth and a threshold. Table 1 reports values of precision and recall for various thresholds. As from the figures, our approach outperforms other methods. We have a very high recall of 0.94 at ϕ = 0.2 and even 0.72 at ϕ = 0.6.

Both benchmark methods have a lower performance. A potential reason for OSVM to perform slightly worse is because it is not easily scalable. Besides, the NABT class has big intra class variance. MCD also results in lower performance as it may fail to capture the multi-modal distribution of data. Our framework alleviates these problems by including a large training dataset estimating correct data distribution. Figure 3 shows the lesion detection results for T2-w. The row represents the image and each column depicts the anatomical slice of patient MRI, corresponding ground truth, lesion detection results with OSVM, MCD and proposed framework. The white labels show the true lesions detected while red ones indicate the brain tissues which are detected as lesion but actually are not part of the lesions. This figure demonstrates visually the ability of our approach to detect lesions. As seen from the last two columns, there is considerable improvement of lesion detection, thanks to the proposed framework.

CONCLUSION

We have introduced a novel method for MSL detection based on Least Squares Probabilistic Classifier. The efficacy of our method was evaluated though rigorous evaluation on MS patients data. We have demonstrated that our method achieves better performance compared to benchmark methods: OSVM and MCD. Our methodology is more suitable for MS lesion analysis because of its ability to capture NABT distribution correctly. This performance suggests that it can provide valuable assistance in detecting the MS lesions in clinical routine with high reliability. The proposed framework is generic in nature and can be extended beyond MSL detection e.g. strokes, tumor. The framework described here allows for exploration of additional MR sequences with or without contrast agents. For example, one can consider infusing T1-w Gadolinium and DTI.

Fig. 1 :

 1 Fig. 1: Workflow for the proposed MSL detection

B

  b=1 θ y,b φ b (x) = θ T y φ(x) and B denotes the number of parameters, θ y = (θ y,1 , ..., θ y,B ) T ∈ B is the basis function vector. In practice, we use a kernel model by setting B = N and φ b (x) = K (x, x b ), where K (x, x ) are the kernel functions.

  63±0.01 0.65±0.02 0.59±0.04 0.61±0.02 0.56±0.03 0.60±0.03 0.46±0.03 0.52±0.03 Proposed 0.79±0.02 0.94±0.02 0.74±0.03 0.86±0.02 0.7±0.02 0.83±0.01 0.63±0.03 0.72±0.02 MCD 0.55±0.02 0.65±0.03 0.51±0.06 0.53±0.04 0.40±0.03 0.52±0.05 0.35±0.03 0.43±0.05

Fig. 2 :

 2 Fig. 2: From top to bottom and left to right: Slice of FLAIR, aggregated probability map, ground truth and obtained lesion mask after Otsu thresholding.

Fig. 3 :

 3 Fig. 3: Lesion detection example.


Renaud Hédouin

Olivier Commowick

Maxime Taquet

Elise Bannier

Benoit Scherrer

Simon K Warfield

Christian Barillot

SYMMETRIC BLOCK-MATCHING REGISTRATION FOR THE DISTORTION CORRECTION OF ECHO-PLANAR IMAGES

We introduce a new approach to correct geometric and intensity distortion of Echo Planar Images (EPI) from images acquired with opposite phase encoding directions. A new symmetric block-matching registration algorithm has been developed for this purpose relying on new adapted transformations between blocks and a symmetric optimization scheme to ensure an opposite symmetric transformation. We present results of our algorithm showing its ability to robustly recover EPI distortion while obtaining sharper results than the popular TOPUP algorithm.

INTRODUCTION

Single-shot Echo Planar Imaging (EPI) is a very fast imaging method. Its low acquisition time allows us to acquire a large number of images in a reasonable time for a clinical usage. Diffusion Weighted Imaging (DWI) allows, through the acquisitions of many gradient weighted images with several bvalues, to model the underlying tissue microstructure of the brain. As many images are required to estimate water diffusion models, DWI needs many acquisitions. For this reason, EPI is the most common sequence used for DWI [START_REF] Johansen-Berg | Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy[END_REF].

The major problem of EPI acquisitions resides in the large distortion that may affect the images. This distortion is caused by the magnetic field inhomogeneity at the tissue/air interfaces. Affected areas are contracted or dilated in the phase encoding direction (PED). To correct for this, several methods have been devised in the literature. One of the earliest techniques involves the acquisition of a B0 field map [START_REF] Jezzard | Correction for geometric distortion in echo planar images from B0 field variations[END_REF], which is in turn used to estimate the B0 inhomogeneities and correct for them. However, this method loses accuracy on strongly warped areas. Other techniques have considered the design of new sequences based on point spread functions [START_REF] Zaitsev | Point spread function mapping with parallel imaging techniques and high acceleration factors: Fast, robust, and flexible method for echo-planar imaging distortion correction[END_REF] to obtain fast acquisitions with less distortion. Unfortunately such techniques, although very promising, are not available and ready to use in clinical routine on all scanners.

Another class of methods is based on the acquisition of an additional EPI where the PED is reversed. From those two images, a distortion field may be computed to correct the EPI. For example, Voss et al. [START_REF] Voss | Fiber tracking in the cervical spine and inferior brain regions with reversed gradient diffusion tensor imaging[END_REF] introduced a method to estimate from two images the correction displacement field based on cumulative intensity distributions along each line in the PED. Andersson et al. [START_REF] Andersson | How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging[END_REF] followed the same kind of idea, which implementation is available in the popular FSL package (TOPUP). Other methods in this category include the one proposed by Holland et al. [START_REF] Holland | Efficient correction of inhomogeneous static magnetic fieldinduced distortion in echo planar imaging[END_REF] performing a line intensity registration on each line independently. However, this method may be unstable when high noise or large displacements are present. Assuming that the subject did not move during a series of EPI acquisitions such as DWI, this class of algorithm allows us to correct for the distortion of the entire EPI series using only one supplemental B0 image acquisition with reversed PED. Therefore, it allows for EPI distortion correction of DWI at almost no additional acquisition time cost.

We propose a new approach towards distortion correction of EPI based on a block-matching registration specifically designed to register two images acquired with opposite PEDs. To do so, we introduce a new symmetric block-matching registration algorithm, optimizing local affine transformations constrained in the PED for the expected distortions in EPI. In addition, the transformation is computed as opposite symmetric to match the distortion model in EPI [START_REF] Jezzard | Correction for geometric distortion in echo planar images from B0 field variations[END_REF]. We first present our distortion correction method in Section 2. Then, we apply it on real acquisition of five subjects where images with four different PED (anterior-posterior, posterioranterior, left-right, right-left) were acquired. We present results of our method in Section 4, demonstrating its ability to robustly recover EPI distortion while obtaining sharper results than TOPUP.

METHODS

Distortion Model

We assume that two images have been acquired: IF is the EPI acquired with a classical PED (e.g. anterior -posterior), and IB is the EPI acquired with a reversed PED (e.g. posterioranterior). The goal of EPI distortion correction is to recover a corrected image C from these two images. Based on previous literature [START_REF] Jezzard | Correction for geometric distortion in echo planar images from B0 field variations[END_REF], we assume that IF and IB are generated from C using a displacement field U parallel to the PED:

C(x) = JT + (x)IF (T+(x)) (1) C(x) = JT -(x)IB (T-(x)) (2)
where T+(x) = x + U (x), T-(x) = x -U (x), J denotes the Jacobian operator. U corresponds to the distortion displacement field, it is assumed that T+ and T-are opposite symmetric, i.e. that they share the same U up to a minus sign.

978-1-4799-2374-8/15/$31.00 ©2015 IEEE

Correction Initialization

Distortion correction involves finding very large and local displacements between the images, displacements that may be extremely difficult to recover for registration approaches. We tackle this problem using a coarse-to-fine approach to recover EPI distortion. We have therefore used a coarse initialization to estimate an initial transformation, which is then further improved with our block-matching strategy. This initialization is based on the method introduced by Voss et al. [START_REF] Voss | Fiber tracking in the cervical spine and inferior brain regions with reversed gradient diffusion tensor imaging[END_REF].

Their approach amounts to the following. For each line in the PED, for both images do:

• Compute normalized cumulated intensities. Ni(y) = 1 αi y 0 Li(x)dx for i = 1, 2 (3)
where L1 and L2 are line intensities of images IF and IB, α1 and α2 are normalization constants.

• For a large number n of values xn between 0 and 1, find by cubic interpolation y1,n and y2,n such that

N1(y1,n) = N2(y2,n) = xn
• At each position yn = (y1,n + y2,n)/2, the transformation map is computed as

U (yn) = y1,n -yn = yn -y2,n (4)
This algorithm has the advantage of being very fast and simple. However, it is highly sensitive to noise and lines registrations are purely independent which may lead to unrealistic transitions between lines. Therefore, Gaussian smoothing is performed on the obtained transformation T , which leads to a trade-off between transformation precision (small Gaussian σ) and transformation regularity (high Gaussian σ).

Block-Matching for Distortion Correction

In the distortion model, the corrected image C is unknown and can at best be estimated. We have therefore chosen a registration approach that does not rely on C. Avants et al. [START_REF] Avants | Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain[END_REF] have introduced a registration algorithm where, instead of looking for T between two images, the transformation T 1/2 is sought so that R • T -1/2 and F • T 1/2 match. We follow this idea, adapting it to a block-matching algorithm [START_REF] Ourselin | Block matching: A general framework to improve robustness of rigid registration of medical images[END_REF][START_REF] Commowick | Automated diffeomorphic registration of anatomical structures with rigid parts: application to dynamic cervical MRI[END_REF] and to match the requirements of EPI distortion correction. Taking U0(x) = 0, the following steps are repeated at each iteration l until convergence (for each pyramid level of a multi-resolution scheme):

• Resample: I F,l-1 (x) = JT +,l-1 (x)IF (T +,l-1 (x)) and I B,l-1 (x) = JT -,l-1 (x)IB (T -,l-1 (x))
• Estimate local transformations for each block on I B,l-1 : A+ ← block-matching(I B,l-1 , I F,l-1 )

• Estimate local transformations for each block on I F,l-1 : A-← block-matching(I F,l-1 , I B,l-1 )

• Extrapolate asymmetric dense stationary velocity field (SVF) corrections from A+ and A-: δS+ ← extrapolate(A+), δS-← extrapolate(A-)

• Compute symmetric SVF correction: δS = 1 4 (δS+ -δS-), and compose it with current transformations:

T +,l = T +,l-1 • exp(δS) and T -,l = T -,l-1 • exp(-δS)
• Ensure T +,l and T -,l are opposite symmetric • Regularize (elastic-like) T +,l and T -,l

In the following, we will detail the block-matching, extrapolation and composition steps of this algorithm and their specificities for distortion correction of EPI.

Block-Matching of Distorted EPI

At each iteration, we define blocks B+,i regularly placed on image I B,l-1 , and B-,i on I F,l-1 . On each of those blocks, we look for an adapted transformation best matching I B,l-1 and I F,l-1 . Such a transformation A.,i can be constrained a priori to match the expected features of distortion correction. First, A.,i is defined as being collinear to the PED. Then, it should allow local contraction or dilation (si) inside a block to account for local intensity changes in distorted images. Finally, we define skew components ki, mi to the transformation so that it accounts for different translations parallel to the PED inside a block. Assuming the PED is the y-axis, A.,i can be expressed as a 4 × 4 matrix of the following form:

A.,i =    1 0 0 0 ki si mi ti 0 0 1 0 0 0 0 1    (5)
Such a transformation has several interesting properties: 1) its Jacobian is easily computed as the scaling component si and can be used to approximate the local modification of intensities in the deformed images, 2) its matrix logarithm is analytically computed from its parameters which is important for the extrapolation step. With this transformation defined, we estimate A+,i and A-,i as the transformations (depending only on 4 parameters) optimizing a similarity measure S between I F,l-1 and I B,l-1 respectively over the blocks B+,i and B-,i:

Â+,i = arg max A +,i S JA +,i I F,l-1 • A+,i, I B,l-1 (6)
Â-,i = arg max

A -,i S I F,l-1 , JA -,i I B,l-1 • A-,i (7)
A linear relationship can be assumed locally between the intensities of the blocks after correction and we therefore utilized a squared correlation coefficient as the similarity measure. This optimization is performed using the BOBYQA algorithm for bounded optimization without gradient [START_REF] Powell | The BOBYQA algorithm for bound constrained optimization without derivatives[END_REF]. This algorithm proceeds by successively computing quadratic approximations to the cost function to find its local maximum. After this matching step, a confidence weight is assigned to each transformation. It is defined from two terms: a similarity based term, the optimal value of the similarity measure Ŝ, and a directional weight w d . The first term accounts for the quality of the best match. The second accounts for the image structure of the reference block. If the block image structure is parallel to the PED, registration in that direction is indeed undetermined, which is known as the aperture problem in image registration. We therefore define w d as a function of the structure tensor inside the reference block: w d (B(., i)) = c l D B(.,i) < vD B(.,i) , g >, where < ., . > denotes the scalar product, D B(.,i) the average structure tensor of block B(., i), c l is the linear coefficient [START_REF] Westin | Processing and visualization for diffusion tensor MRI[END_REF], vD B(.,i)

is the principal eigenvector of D B(.,i) , and g is the PED. This weight will be 0 if the structure tensor is perpendicular to the PED (image structure parallel to g) or spherical, giving a structure based confidence to the matches. In the end, the weight for the match of block B(., i) is defined as w B(.,i) = ŜB(.,i) w d (B(., i)).

Transformation Extrapolation and Composition

We have constituted two sets of block pairings (one for the IF and one for IB): A+ = (A+,1, . . . , A+,m) and A-= (A-,1, . . . , A-,n), each pairing being positioned at the center of its corresponding block, and given a weight w B(.,i) . We then use the method proposed in [START_REF] Commowick | Automated diffeomorphic registration of anatomical structures with rigid parts: application to dynamic cervical MRI[END_REF] to extrapolate two update SVFs from the sparse weighted log-transformations: δS+ = extrapolate(A+) and δS-= extrapolate(A-). A symmetric δS is computed as a quarter of the subtraction of asymmetric incremental updates δS+ and δS-: δS = 1 4 (δS+ -δS-), taking into account symmetry and the fact that only half of the SVF is required to make the images move towards the image C. This symmetrization is necessary to ensure equivalent roles to IF and IB in the registration process. Then, the transformation corrections δT+ and δT-are computed as the exponential of these SVFs [START_REF] Arsigny | A fast and logeuclidean polyaffine framework for locally linear registration[END_REF].

Such an extrapolation has the advantage of incorporating an outlier rejection framework, able to remove from the update SVF log-transformations that are erroneous due to noise or artifacts in the images. In addition, it guarantees that the extrapolated transformations are diffeomorphic and therefore invertible. Once δT+ and δT-are computed, they are composed with the current transformations: T +,l = T +,l-1 • δT+ and T -,l = T -,l-1 • δT-. Such an operation however does not guarantee that T +,l and T -,l still have opposite roles, as required in the EPI distortion model, i.e. that T +,l (x) = x + U l (x), T -,l (x) = x -U l (x). We therefore compute U at the l -th iteration as U l (x) = 1 2 (T +,l (x) -T -,l (x)), thereby ensuring an opposite symmetric transformation.

EXPERIMENTAL DESIGN

We have scanned 5 healthy volunteers to evaluate our distortion correction algorithm. For each volunteer, two pairs of B0 EPI with opposite PED were acquired: AP/PA and LR/RL. Each image has a size of 128×128×60 and an isotropic resolution of 2×2×2 mm 3 . We then corrected the AP/PA and the LR/RL images separately using one of the algorithms to evaluate: initial transformation as described in Section 2.2 (Initial), our algorithm initialized by the previous transformation (BM), and the TOPUP algorithm [START_REF] Andersson | How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging[END_REF]. The two pairs of images AP/PA and LR/RL are subject to different distortion but are images of the same anatomy. We therefore measure the quality of an algorithm as a comparison between the two images after distortion correction.

To compare images after correction, we define two complementary measures. First, we specify a global measure Sim on images as an average of the local correlation coefficients between the images. However, intensities out of the brain are pure noise. We therefore masked those background areas by first computing a mask of the relevant areas for each corrected image. Then, a global mask was computed as the union of all these individual masks to ensure that quality metrics are comparable between the methods. Sim is then defined as:

Sim(I, J) = 1 Card({I, J} N ) p∈{I,J} N C(IN p , JN p ) ( 8
)
where C is the local correlation of IN p , JN p (neighborhoods of p in I and J), {I, J} N the image domain. Sim characterizes well if the images match after correction, however it is sensitive to image smoothing which may make a method appear better than it really is. In fact, the resulting correction also needs to keep the structural information: the sharpness of the images. This is evaluated by a sharpness index as follows:

Sharpness(I) = 1 Card(I N ) p∈I M V (IN p ) E 2 (IN p ) ( 9
)
where V is the variance and E the expectation. It computes the average of ratios between the local variances and means.

In other words, it considers that structures are sharply defined if the variance inside a block is high compared to its mean. Again, we compute this measure on the same mask used for correlation.

RESULTS

We present in Fig. 1 a representative example of a distortion correction result for LR/RL images. This figure demonstrates visually that our algorithm clearly improves results over the initialization method. The eyes are better defined after applying BM, and structures in the temporal lobes are also better defined and more in phase with the anatomical T2 reference. TOPUP also achieves a good result, however it is more blurry than the other two methods. To quantitatively validate our observations, we have computed similarity and sharpness metrics to our datasets. Correlation similarity results are presented in Table 1. This table shows that BM increases systematically the similarity index as compared to the initialization. However, these results also show that TOPUP seems to be performing better than our method that could be due to a visually smoother result.

Table 1. Correlation results (Sim measure) between AP/PA and LR/RL images. Columns from left to right: no correction, initial transformation, BM and TOPUP.

To better understand the behavior of TOPUP, we therefore computed the sharpness on all images, corrected or not, on the 5 subjects. Results are presented in Table 2 with images and correction of the pair AP/PA, (results for the pair LR/RL are similar). The first three columns (original images, corrected by initial transformation and by BM) show a stable sharpness index. However, the sharpness index of TOPUP decreases significantly, confirming the visual impression that TOPUP produces smoother images after correction. As a final comparison, we then tested the incidence of smoothness on the correlation score. We present in Table 3 the correlation results for one representative correction result obtained by BM, simply adding Gaussian smoothing to the result images with different σ before computing the local correlations. Interestingly, correlation and sharpness are strongly linked: when increasing smoothness, the correlation score reaches better values. Moreover, for a specific σ value (0.6), we even obtain the same sharpness and similarity values as for TOPUP. This advocates two conclusions : 1) our method obtains results of similar quality to TOPUP but with a higher sharpness of structures, which is desirable to avoid partial volume effects; 2) validation of distortion correction methods is a complex field that requires several metrics to be considered for evaluating all desired aspects.

CONCLUSION

Fig. 1 .

 1 Fig. 1. Distortion correction results. First line: EPI with opposite PED (LR/RL) and a T2 resampled image. Second line: correction results with the three compared methods.

Table 2 .

 2 Sharpness results of corrected images. From left to right: no correction, initial transformation, BM and TOPUP.

	Sharpness Untouched Initial BM TOPUP
	Subject 1	0.34	0.33	0.36	0.23
	Subject 2	0.38	0.36	0.39	0.26
	Subject 3	0.35	0.33	0.36	0.25
	Subject 4	0.32	0.31	0.33	0.23
	Subject 5	0.34	0.33	0.34	0.24

Table 3 .

 3 Illustration of the link between image smoothness and correlation results. First line: σ values for Gaussian smoothing, second and third line: sharpness and correlation of BM corrected images after smoothing.

	Sigma	0.2	0.3	0.4	0.5	0.6	0.7
	Sharpness 0.36 0.35 0.32 0.27	0.23 0.21
	Sim	0.65 0.65 0.67 0.72	0.76 0.79

We have developed a new algorithm for the correction of EPI distortion using two EPI with opposite PED. To do so, we have developed new linear transformations adapted to local distortions. We then integrated these transformations into a symmetric block-matching algorithm, ensuring a robust computation of an opposite symmetric transformation. We have evaluated this method on 5 subjects with 2 pairs of EPI. We have shown how the block-matching approach improves quantitative and visual results. It also provides results comparable with TOPUP but with a much improved sharpness, close to the one of the original images. Computationally, we have run the two algorithms on a Xeon 2.5 Ghz computer on 20 cores: our method took 150 s and TOPUP 750 s.


Damien Bergeat

Doctor Laurent Sulpice
email: laurent.sulpice@chu-rennes.fr-karim.boudjema@chu-rennes.fr

Michel Rayar

Aude Merdignac

MD B Meunier

MD Eveline Boucher

Professor Karim Boudjema

Extended liver resections for intrahepatic cholangiocarcinoma: Friend or foe?

Keywords: extended Liver resection, intrahepatic cholangiocarcinoma, outcomes, survival

come

INTRODUCTION

Liver resection (LR) is the most suitable curative treatment for intrahepatic cholangiocarcinoma (ICC). ICC has become a public health issue with an incidence that has increased in all Western countries in the last two decades [START_REF] Taylor-Robinson | Increase in mortality rates from intrahepatic cholangiocarcinoma in England and Wales 1968-1998[END_REF][START_REF] Patel | Increasing incidence and mortality of primary intrahepatic cholangiocarcinoma in the United States[END_REF][START_REF] Welzel | Risk Factors for Intrahepatic and Extrahepatic Cholangiocarcinoma in the United States: A Population-Based Case-Control Study[END_REF][START_REF] Wood | Do increases in mortality from intrahepatic cholangiocarcinoma reflect a genuine increase in risk? Insights from cancer registry data in Scotland[END_REF] . Moreover, the prognosis for ICC has not or only slightly improved. Indeed, the median survival ranges between 18 and 39 months [START_REF] Farges | AJCC 7th edition of TNM staging accurately discriminates outcomes of patients with resectable intrahepatic cholangiocarcinoma[END_REF][START_REF] Ribero | Surgical approach for long-term survival of patients with intrahepatic cholangiocarcinoma: A multi-institutional analysis of 434 patients[END_REF] . This poor prognosis is first related to the high rate of recurrence, particularly during the first year following liver resection [START_REF] Sulpice | Treatment of recurrent intrahepatic cholangiocarcinoma[END_REF] , and to the low rate of resectability. Indeed, in the majority of cases, ICC arises in patients without identifiable underlying liver disease and is thus frequently diagnosed at a late stage with large tumors [START_REF] Lang | Extended hepatectomy for intrahepatic cholangiocellular carcinoma (ICC): when Clean version 26/10/14 is it worthwhile? Single center experience with 27 resections in 50 patients over a 5-year period[END_REF][START_REF] Yamanaka | Clinicopathologic spectrum of resected extraductal mass-forming intrahepatic cholangiocarcinoma[END_REF][START_REF] Suzuki | Comparative clinicopathological study of resected intrahepatic cholangiocarcinoma in northeast Thailand and Japan[END_REF] . In these conditions, the only available curative resection options are extended liver resections (ELR) or orthotopic liver transplantation (OLT). Unfortunately, the first studies that reported on the primary results of OLT in ICC revealed five-year overall survival (OS) rate of only 25% [START_REF] Hu | Retrospective analysis of prognostic factors after liver transplantation for intrahepatic cholangiocarcinoma in China: a single-center experience[END_REF] . These poor results are inconceivable in periods of liver graft scarcity. In contrast, with the recent progress in surgical techniques, perioperative management and postoperative care, ELR has become increasingly common, and the risks associated with these major procedures have decreased. For example, portal vein occlusion via ligation or embolization allows for the possibility of increasing the remaining liver and thus reducing the risk of liver failure following ELR.

In recent years, our experiences have revealed an increase in the indications for ELR in ICC from 17% to one-third of cases (Figure 1.). However, this surgical aggressiveness has not yet been evaluated. Although some reports have analyzed the effects of vascular reconstruction on survival and outcome [START_REF] Ali | Role of Major Vascular Resection in Patients with Intrahepatic Cholangiocarcinoma[END_REF] , none have specifically compared ELR and LR in terms of outcomes and survival.

The aim of the present study was to analyze the outcomes and survival following LR with curative intent for ICC with a special focus on ELR.

METHODS

Patients

The study population included all with curative-intent LRs performed for mass forming type ICCs as defined by the liver Cancer Study Group of Japan at a single tertiary referral center between the 1st of January 1997 and the 1st of September 2013.

Hepatocellular cholangiocarcinoma, gallbladder carcinoma, hilar cholangiocarcinoma and periductal infiltrating types were excluded from the analyses. The clinical data were retrospectively collected from an LR database and analyzed after institutional review board approval was obtained.

Data were collected regarding demographics (i.e., age, sex and body mass index (BMI)), surgical variables, length of hospital stay, morbidity, postoperative chemotherapy and follow-up including disease recurrence and death.

Pathological data, such as the tumor size, satellite nodes, lymph node involvement, UICC 7 th edition TNM staging, perineural invasion and microvascular involvement, were collected.

Surgery

Prior to surgery, each patient was evaluated by thoracoabdominal computed tomography scans with intravenous contrast. Portal vein embolization was indicated when the Liver Resection Volume/total liver volume ratio was < 35%. Extended liver resection (ELR) was defined by the resection of five or more Couinaud liver segments. Right or left hepatectomy extended to segment 1 and right or left trisectionectomy were considered to be ELRs. All LRs were performed with curative intent by senior surgeons. Intraoperative ultrasounds of the liver were performed to ensure the extension and the resectability of the tumor. Vascular or biliary reconstructions (partial or complete) were performed when necessary. Vascular reconstructions were classified into the following three categories: portal vein reconstruction, inferior vena cava reconstruction, and hepatic artery reconstruction. In one case, LR was performed ex situ. The absence of bulky lymph nodes in the celiac or paraaortic area, the absence of tumor residue in the remnant liver and the absence of peritoneal extension defined curative hepatectomies. The resection Clean version 26/10/14 margin was classified as microscopically negative (R0) or positive (R1). When macroscopic tumoral tissue was left in situ, the resection was R2.

Postoperative morbidity

Postoperative morbidity was defined by any complication that occurred within 90 days after the LR and was categorized according to the Dindo and Clavien classification [START_REF] Dindo | Classification of Surgical Complications: A New Proposal With Evaluation in a Cohort of 6336 Patients and Results of a Survey[END_REF] . Major complications (MC) were categorized as grades III to V in the Dindo and Clavien classification. Postoperative liver failure was defined by the "50-50" criteria on postoperative day 5 as described by Balzan et al. [START_REF] Balzan | The ???50-50 Criteria??? on Postoperative Day 5: An Accurate Predictor of Liver Failure and Death After Hepatectomy[END_REF] . Postoperative mortality was defined by the occurrence of death within 90 postoperative days.

Survival Analyses

The end of the follow-up was set to be between the 1 st of December 2013 and the 1 st of January 2014 or the time of death. Overall survival was calculated from the date of the intervention to the time of death (regardless of cause) or to the end of follow-up.

Disease-free survival (DFS) was calculated from the date of the intervention to the time of recurrence or the time of death. To identify the prognostic factors for overall survival, the following data were analyzed: age, gender, ASA score ≥ 2, BMI ranking in categories (<25, 25-30 and >30), known cirrhosis, neoadjuvant chemotherapy, portal embolization, ELR, vascular resection, bile duct resection, lymph node dissection, resectability, perioperative blood transfusion, major complications, tumor size (≥ 5 cm), ≥ 1 node, macrovascular invasion, perineural invasion, positive lymph nodes, and adjuvant chemotherapy.

Statistical Analyses

Comparisons were made between the patients who underwent ELR and those who underwent other LRs. Qualitative variables are expressed as numbers with percentages and were compared with chi square tests or Fisher's tests when necessary. Quantitative variables are expressed as the mean ± the standard deviation (SD) or the median and were compared using Student's t tests or Mann-Whitney U tests as appropriate. Univariate analyses were conducted on the variables that are known to have roles in postoperative major complications. All variables with p values <0.1 in the univariate analyses were entered into the multivariate analysis that utilized a multiple logistic regression model. The most suited model was then selected using a stepwise method based on the Akaike criteron.

The cumulative survival rate was estimated by the Kaplan-Meier method, and the survival curves were compared with the log rank test. The univariate analyses were conducted between the relevant clinicopathologic variables and the cumulative survival rates using the log rank test. The Cox proportional hazard model was utilized with all variables with p values < 0.1 in the univariate analyses and the variables that are known to influence survival. Stepwise method was also used to select the most suited model. P<0.05 was considered statistically significant in all comparisons. The analyses were performed with R statistical software (http://www.r-project.org/).

Clean version 26/10/14

RESULTS

Demographic and operative data

During the study period, 107 patients underwent LR with curative intent for ICC (82 men and 25 women). ELR was performed in 27 cases (25%). The preoperative data were compared and are shown in the Table 1. The mean ages were 61.4 ± 10.2 years and 65.6 ± 8.9 years in the ELR and other LR groups, respectively (p=0.07).

The patients' BMIs were greater than 30 in three cases (11.11%) in the ELR group and in 21 cases in the other group (26.25%; p=0.2). No differences were found in either portal vein embolization or neoadjuvant chemotherapy rates between the two groups. The tumor size was significantly larger in the ELR than in the other LR group (7.8 ± 2.46 cm vs. 6.14 ± 3.14, respectively, p=0.003). ELRs were associated with more complex reconstructions. Indeed, vascular reconstructions were required in eight cases (29.63%) in the ELR group vs. three (3%) in the other LR group (p<0.001), and included portal vein reconstructions in six vs. one (p<0.001), inferior vena cava reconstructions in four vs. two (p=0.03) and hepatic artery reconstructions and in three vs. zero (p=0.01) of the cases in each respective group. Bile duct resections were required for eight patients (29.63%) in the ELR and two (2.5%) patients in the other LR group (p<0.001). R0 resection rate was similar in both group (74% vs 79% in ELR and other LR group respectively, p=0.81).

The major complication and surgical revision rates were significantly higher in the ELR group than in the other LR group (59.26 vs. 15% (p=0.01) and 29.63 vs. 7.5% (p=0.06), respectively). The liver dysfunction rates were not statistically different The 90-day mortality rate was 9.2% (10 patients) across the entire population, and there was no difference between the two groups (p=1). We have summarized all causes of death in the Supplementary Table 1. Two patients (7.4%) in the ELR group died; the first death was linked to portal vein and arterial thromboses following an extended right hepatectomy, and the second was linked to major sepsis.

Risk Factors for Major Complications

Twenty-eight patients developed major complications (26%). Among the 14 factors assessed by univariate analyses (Table 2.), ELR (p<0.001), vascular reconstruction (p<0.001), bile duct resection (p=0.02) and extended lymphadenectomy (p=0.01) were significantly associated with increased risks of major complication. In the multivariate analysis, ELR (OR=6.2, (2.11-19.62), p<0.001) was the only remaining independent risk factor for major complications.

Survival and recurrence analyses

All patients were included in the overall and disease-free survival analyses. The median overall survival of the entire cohort was 32.8 months, and the one-, threeand five-year actuarial survival rates were 79.8, 49.4 and 34.6%, respectively.

Recurrence was observed in 16 ELR patients (59.26%) and 44 other LR patients (55%; p=0.81). Fifty-five percent of the recurrences were at least intrahepatic. The median disease-free survival of the entire cohort was 10.73 months, and the one-, three-and five-year actuarial survival rates were 49.68, 25.7 and 17.4%, respectively. The overall survival rates and disease-free survival rates were not significantly different between the ELR and the other LR groups, p=0.881 and p=0.228, respectively; Figure 2.

Univariate and Multivariate Analysis of Variables Affecting Overall Survival

The variables that were thought to have effects on overall survival are reported in Table 3. Extended lymphadenectomy (p=0.05), perioperative allogenic blood transfusion (p<0.001), tumor size ≥ 5 cm (p=0.005), multinodular tumor (p<0.001) and positive lymph node involvement (p=0.02) were associated with poor overall survival. Neither vascular reconstruction nor extended resection significantly affected overall survival. The multivariate analysis revealed that an age ≥ 65 years (OR 1.72, (1.03-2.86), p=0.036), a perioperative blood transfusion (OR 2.51, (1.49 to 4.23), p<0.001) and the presence of more than one nodule (OR 3.17, (1.67 to 5.97), p<0.001) were independent risk factors for overall survival (Figure 3).

DISCUSSION

Liver resection with curative intent remains the best therapeutic option for ICC patients [START_REF] Farges | Influence of surgical margins on outcome in patients with intrahepatic cholangiocarcinoma: a multicenter study by the AFC-IHCC-2009 study group[END_REF] . Advances in anesthesiology, liver surgery and postoperative care have enabled more extensive liver resections. The present study analyzed the outcomes of 27 ELRs that were performed with the intent to cure ICC patients. Our results are the first to shed light on the surgical aggressiveness of the use of ELR for ICC compared with the use of less aggressive LRs. Indeed, we demonstrated that, despite an increase in the 90-day postoperative major morbidity, ELR resulted OSs and DFSs that were similar to those of non-extended LRs that were performed for smaller tumors. These results are strengthened by the absence of patients lost to follow up in our cohort.

The multivariate analysis of the factors associated with major complications revealed that ELR was the only independent risk factor. In this series, the higher rate of MC in the ELR group was probably linked to the complexity of surgeries, which included more vascular or biliary resections and reconstructions. The most frequent complication was biliary leakage, and this finding accords with a finding of our previous report that showed that major LR was an independent risk factor for the occurrence of biliary leakage [START_REF] Guillaud | Incidence and predictive factors of clinically relevant bile leakage in the modern era of liver resections[END_REF] . These results have recently been confirmed by Zimmity et al. in a large retrospective study of approximately 2628 LRs that showed that increased LR complexity is correlated with the rate of biliary leakage but not with the rates of hemorrhage or perihepatic abscesses [START_REF] Zimmitti | Greater Complexity of Liver Surgery is Not Associated with an Increased Incidence of Liver-Related Complications Except for Bile Leak: An Experience with 2,628 Consecutive Resections[END_REF] . In the present series, the overall 90-day mortality was higher than the recent report of Ali et al. [START_REF] Ali | Role of Major Vascular Resection in Patients with Intrahepatic Cholangiocarcinoma[END_REF] . This difference could be partially explained by the lack of selection criterion such as the age or underlying liver disease.

Although portal vein embolization (PVE) was considered preoperatively in all cases, two patients (7.4%) in the ELR group who did not have PVEs developed postoperative liver dysfunction. These results are in line with those of a previously published study [START_REF] Ohtsuka | Results of surgical treatment for intrahepatic cholangiocarcinoma and clinicopathological factors influencing survival[END_REF][START_REF] Vauthey | Is Extended Hepatectomy for Hepatobiliary Malignancy Justified?[END_REF] . However, these findings might indicate that the operative risk was underestimated in the ICC patients, and therefore inadequate preoperative and operative management was applied. This underestimation is likely based on the false belief that ICCs arise in normal underlying liver parenchyma. Indeed, one might consider that the increased incidence of ICC in Western countries might be related to the rise of metabolic syndromes and insulin resistance [START_REF] Welzel | Risk Factors for Intrahepatic and Extrahepatic Cholangiocarcinoma in the United States: A Population-Based Case-Control Study[END_REF][START_REF] Jing | Diabetes mellitus and increased risk of cholangiocarcinoma: a meta-analysis[END_REF] , which would lead us to believe that ICCs often occur in liver parenchyma with steatosis or steatohepatitis and might partially explain the rate of postoperative liver failure [START_REF] Reddy | Prevalence of nonalcoholic steatohepatitis among patients with resectable intrahepatic cholangiocarcinoma[END_REF] .

Similarly, Cauchy et al. recently demonstrated that LR for HCC associated with metabolic syndrome is significantly associated with an elevated risk of postoperative morbidity [START_REF] Cauchy | Surgical treatment of hepatocellular carcinoma associated with the metabolic syndrome[END_REF] . Based on these results, it is reasonable to suggest that improvements in pre-and perioperative management should be implement especially when ELR is considered for ICC.

The main result of the present study is that ELR performed for the largest ICC neither negatively influenced OS nor DFS compared with non-ELR performed for the smallest tumors. Indeed, the actuarial five-year OS of the patients who underwent ELR was 32.3%, and this result is largely similar to those reported by Cho et al. in their series of 63 LRs for which the five-year cumulative survival rate was 31.8% [START_REF] Cho | Survival Analysis of Intrahepatic Cholangiocarcinoma After Resection[END_REF] .

Beyond the fact that these results validate our aggressive policy, they allow us to further broaden the criteria for the patients who are eligible for curative treatments; specifically some patients with conditions currently considered unresectable could be included. Indeed, the challenge henceforth is to identify and validate therapeutic strategies that can reduce tumor size and enable curative resection through ELR.

Among the different possibilities, transcatheter arterial chemoembolization (TACE) using cisplatin might represent a promising approach [START_REF] Liapi | Transcatheter and ablative therapeutic approaches for solid malignancies[END_REF] . TACE allows for the delivery of increasing local concentrations of chemotherapy without damaging the healthy liver tissue and reduces systemic side effects [START_REF] Liapi | Transcatheter and ablative therapeutic approaches for solid malignancies[END_REF] . Accordingly, Kim et al. reported interesting results about down-staging ICC following TACE [START_REF] Kim | Transcatheter arterial chemoembolization or chemoinfusion for unresectable intrahepatic cholangiocarcinoma[END_REF] . Similarly, the intraarterial injection of yttrium-90-labeled (yttrium-90) is a persuasive therapeutic option that can decrease tumor and increase the remnant liver volume as has recently been shown in HCC [START_REF] Edeline | Volumetric changes after (90)y radioembolization for hepatocellular carcinoma in cirrhosis: an option to portal vein embolization in a preoperative setting?[END_REF] . We previously demonstrated the potential of yttrium-90 in ICC recurrence following primary curative resection to ensure prolonged survival [START_REF] Sulpice | Treatment of recurrent intrahepatic cholangiocarcinoma[END_REF] . Among 46 patients with ICCs that were initially considered unresectable, Mouli et al. reported that that 10.8% (n=5) were converted to resectable statuses and were successfully treated with curative resection after yttrium-90 [START_REF] Mouli | Yttrium-90 Radioembolization for Intrahepatic Cholangiocarcinoma: Safety, Response, and Survival Analysis[END_REF] . These results are reinforced by a recently published clinical case [START_REF] Servajean | One case of intrahepatic cholangiocarcinoma amenable to resection after radioembolization[END_REF] . Altogether, expert centers play a crucial role in the management of ICC and contribute to the optimization of the management of these Clean version 26/10/14 cancers especially when large cancers that are potentially unresectable and require collaboration between surgeons, oncologists and radiologists are being considered.

Finally, our multivariate analysis of the factors that affected the OS demonstrated that only an age over 65 years, the presence of satellite nodules and the requirement for a perioperative blood transfusion were closely correlated with the poorest prognoses.

In this series, blood transfusions were required in 29.63% of the ELR group, which is not different from previously reported results [START_REF] Vauthey | Is Extended Hepatectomy for Hepatobiliary Malignancy Justified?[END_REF] . Perioperative blood transfusion has been highlighted frequently as an important predictive factor for perioperative mortality and survival in liver cancer surgery for the last 20 years. A recent metaanalysis of the effects of perioperative blood transfusions in patients with hepatocellular carcinomas confirmed that perioperative blood transfusions are associated with an elevated risk of death at three and five years and an increased risk of tumor recurrence [START_REF] Liu | Perioperative Allogenenic Blood Transfusion Is Associated with Worse Clinical Outcomes for Hepatocellular[END_REF] . Several authors have argued that these effects are due to the immunosuppressive effects of blood transfusion and support the role of the immune system in the control of tumor proliferation and progression. Accordingly, it is important to reassert that perioperative allogenic blood transfusion should not be avoided when possible and to emphasize surgical techniques that minimize blood loss. These data further strengthen the need to manage these patients in expert centers to improve the overall prognoses of these tumors.

Obviously, the current study has some limitations. Even if there was no significant difference between the ELR group and the other LR group on confounding variables, there was potentials selection bias. The sample size of the ELR group was quite small even it represented a quarter of our entire population and further studies are needed to confirm the results.

In conclusion, for the first time, this study supports the notion that ELRs for large ICCs do not negatively affect oncological outcomes. These findings argue in favor of aggressive therapeutic strategies for ICC to increase the rate of resectability. In view of ELR results on survival, we can assume that the combination of a neoadjuvant therapy, such as yttrium-90, to decrease the tumor size and increase the remnant liver with ELR is an appealing strategy that should be explored in the near future. Supplementary Table 1. Summarized of 90-day mortality causes.

( 7 .

 7 41 and 10% in the ELR and other LR group, respectively).Hospital stay was significantly longer in ELR group than in other LR group( 13 ± 16.7 days [4-70] and 10 ± 13.3 days[4-91], respectively (p=0.02).

Figure 1 .

 1 Figure 1. Evolution of the frequency with which ELR were performed before

Figure 2 .

 2 Figure 2. Overall and disease-free survival rates after liver resection according

Figure 3 .

 3 Figure 3. Overall survival after liver resection according to transfusion status (continuous line: no transfusion, dotted line: transfusion) and the presence of more than one nodule (continuous line: 1 nodule, dotted line: > 1 nodule).

Figure 1 .

 1 Figure 1. Evolution of the frequency with which ELR were performed before and after

Figure 2 .

 2 Figure 2. Overall and disease-free survival after liver resection according to extended liver resection status (ELR) (continuous line: non-ELR, dotted line: ELR).

Figure 3 .

 3 Figure 3. Overall survival after liver resection according to transfusion status (continuous line: no transfusion, dotted line: transfusion) and the presence of more than one nodule (continuous line: nodule = 1, dotted line: > 1 nodule).

TABLE AND FIGURE LEGENDS Table 1 . Patient characteristics, perioperative data and outcomes according to extended resection status.Table 2 . Factors influencing the occurrence of major complications.Table 3 . Factors influencing the overall survival.

 ANDLEGENDS123

Table 1 . Patient characteristics, Perioperative and outcome data according to extended resection status Extended liver resection

 1

		Yes	No	p value
		(n=27)	(n=80)
	Age (mean±sd)	61.4±10.2	65.6±8.9	0.07
	Range	40-79	39-83
	Gender F:M	9:18	16:64	0.25
	BMI*			0.2
	≤ 25	12 (44.44)	34 (42.50)
	25-30	11 (40.74)	23 (28.75)
	≥ 30	3 (11.11)	21 (26.25)
	ASA score**			1
	<2	21 (77.78)	62 (77.50)
	≥2	6 (22.22)	16 (20.00)
	Known cirrhosis	4 (14.81)	25 (31.25) 0.14
	Portal embolization	6 (22.22)	8 (10.00) 0.18
	Neoadjuvant chemotherapy	6 (22.22)	9 (11.25) 0.19
	Blood transfusion	8 (29.63)	21 (26.25) 0.9
	Vascular resection
	Portal vein resection	6 (22.22)	1 (1.25) <0.001
	Inferior vena cava resection	4 (14.81)	2 (2.50) 0.03
	Hepatic artery resection	3 (11.11)	0 (0.00) 0.01
	Bile duct resection	8 (29.63)	2 (2.50) <0.001
	Resectability			0.81
	R0	20 (74.07)	63 (78.75)
	R1	7 (25.93)	17 (21.25)
	Tumor size (cm,mean ± sd)	7.8 ± 2.46	6.14 ± 3.14	0.003
	Major complication***	16 (59.26)	12 (15.00) <0.001
	Biliary fistulas	10 (37.04)	11 (13.75) 0.01
	Surgical revision	8 (29.63)	6 (7.50) 0.006
	Hospital stay (days, median ± sd)	13 ± 16.7	10 ± 13.3	0.02
	Range	4-70	4-91

Table 2 . Univariate and Multivariate analyses of major complications factors

 2

		Univariate analysis	Multivariate Analysis
	Factors			Major Complications
		YES (n=28)	NO (n=79)	p value	Odds ratio (95% confidence Interval)	p value
	Age			0.39	-	-
	< 65	15 (53.57) 33 (41.77)
	≥ 65	13 (46.43) 46 (58.23)
	Gender			0.12	-	-
	Male	18 (64.29) 64 (81.01)
	Female	10 (35.71) 15 (18.99)
	BMI*			0.52	-	-
	≤ 25	14 (50.00) 32 (40.51)
	25-30	7 (25.00) 27 (34.18)
	≥ 30	5 (17.86) 19 (24.05)
	ASA score**			0.52	-	-
	<2	4 (14.29) 18 (22.78)
	≥2	23 (82.14) 60 (75.95)
	Known cirrhosis			0.14	-	-
	Yes	4 (14.29) 25 (31.65)
	No	24 (85.71) 54 (68.35)
	Neo adjuvant chemotherapy			0.53	-
	Yes	5 (17.86) 10 (12.66)
	No	23 (82.14) 69 (87.34)
	Portal embolization			1	-
	Yes	3 (10.71) 11 (13.92)
	No	25 (89.29) 68 (86.08)
	Operative time			0.71	-
	≥ 180 min	13 (46.43) 32 (40.51)
	< 180 min	15 (53.57) 66 (83.54)
	Tumor size			0.037	-	-
	< 5cm	4 (14.29) 30 (37.97)
	≥ 5 cm	24 (85.71) 49 (62.03)
	Blood transfusion			0.052	2.91(0.96-8.97)	0.056
	Yes	12 (42.86) 17 (21.52)
	No	16 (57.14) 62 (78.48)

(7.41) 8 (10.00) 1

The study received no external support.