Arnaud Huaulmé
email: arnaud.huaulme@univ-rennes1.fr

Duygu Sarikaya

Kévin Le Mut

Fabien Despinoy

Yonghao Long

Qi Dou

Chin-Boon Chng

Wenjun Lin

Satoshi Kondo

Laura Bravo-Sánchez

Pablo Arbeláez

Wolfgang Reiter

Manoru Mitsuishi

Kanako Harada

Pierre Jannin
email: pierre.jannin@univ-rennes1.fr

MIcro-surgical anastomose workflow recognition challenge report

Keywords: Surgical Process Model, Workflow recognition, Multi-modality, OR of the future

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Introduction

Computer-assisted surgical (CAS) systems should ideally make use of a complete and explicit understanding of surgical procedures. To achieve this, a surgical process model (SPM) can be used. A SPM is defined as a "simplified pattern of a surgical process that reflects a predefined subset of interest of the surgical process in a formal or semi-formal representation" [START_REF] Jannin | Modeling surgical procedures for multimodal image-guided neurosurgery[END_REF]. The SPM methodology is used for various applications, such as operating room optimization and management [START_REF] Sandberg | Deliberate Perioperative Systems Design Improves Operating Room Throughput[END_REF][START_REF] Bhatia | Real-time identification of operating room state from video[END_REF], learning and expertise assessment [START_REF] Huaulmé | Sequential surgical signatures in micro-suturing task[END_REF][START_REF] Forestier | Surgical skills: Can learning curves be computed from recordings of surgical activities?[END_REF], robotic assistance [START_REF] Ko | Surgery task model for intelligent interaction between surgeon and laparoscopic assistant robot[END_REF], decision support [START_REF] Quellec | Real-Time Task Recognition in Cataract Surgery Videos Using Adaptive Spatiotemporal Polynomials[END_REF], and quality supervision [START_REF] Huaulmé | Offline identification of surgical deviations in laparoscopic rectopexy[END_REF].

According to Lalys et al. [START_REF] Lalys | Surgical process modelling: a review[END_REF], a surgical procedure can be decomposed on several levels of granularity ,e.g., phases, steps, and activities. Phases are the decomposition of a surgical procedure into the main periods of intervention (e.g., resection). Each phase is broken down into multiple steps corresponding to a surgical objective (e.g., to resect the pouch of Douglas). A step is composed of several activities that describe the physical actions (namely action verbs,e.g., cut) performed on specific targets (e.g., the pouch of Douglas) by specific surgical instruments (e.g., a scalpel). This initial definition was improved at a lower granularity level to take into account information closed to kinematic data [START_REF] Despinoy | Unsupervised trajectory segmentation for surgical gesture recognition in robotic training[END_REF]: surgemes and dexemes. A surgeme represents a surgical motion with explicit semantic meaning (e.g., grab), and a dexeme is a numerical representation of the sub-gestures necessary to perform a surgeme.

In early publications [START_REF] Sandberg | Deliberate Perioperative Systems Design Improves Operating Room Throughput[END_REF][START_REF] Bhatia | Real-time identification of operating room state from video[END_REF][START_REF] Huaulmé | Sequential surgical signatures in micro-suturing task[END_REF][START_REF] Forestier | Surgical skills: Can learning curves be computed from recordings of surgical activities?[END_REF][START_REF] Ko | Surgery task model for intelligent interaction between surgeon and laparoscopic assistant robot[END_REF][START_REF] Quellec | Real-Time Task Recognition in Cataract Surgery Videos Using Adaptive Spatiotemporal Polynomials[END_REF][START_REF] Huaulmé | Offline identification of surgical deviations in laparoscopic rectopexy[END_REF][START_REF] Despinoy | Unsupervised trajectory segmentation for surgical gesture recognition in robotic training[END_REF], SPMs were manually acquired by human observers. However, this solution has several drawbacks: It is costly concerning human resources, time-consuming, observer-dependent, and errors could be made. In [START_REF] Huaulmé | Automatic annotation of surgical activities using virtual reality environments[END_REF], the authors noted that for the annotation of a peg transfer task, the mean duration to manually annotate one minute of video was around 13 minutes, and 65 annotation errors were counted for 60 annotations although the task was less susceptible to subjective interpretation than a surgical operation. To overcome these issues, [START_REF] Huaulmé | Automatic annotation of surgical activities using virtual reality environments[END_REF] proposed an automatic annotation method based on the information extracted from a virtual reality simulator.

Even though this is a promising solution to limit human annotation, it requires information that could be complicated to obtain in surgical practice, such as the interactions between the instruments and anatomical structures. Other solutions are currently being studied to reduce the amount of manual annotation as transfer learning from simulated data to real data [START_REF] Zisimopoulos | Can surgical simulation be used to train detection and classification of neural networks?[END_REF] or from a limited amount of annotated data [START_REF] Dipietro | Automated Surgical Activity Recognition with One Labeled Sequence[END_REF].

Despite these innovative methods, automatic and online recognition of surgical workflows is mandatory to bring context-awareness CAS applications inside the operating room. Various machine learning and deep learning methods have been proposed to recognize different granularity levels such as phases [START_REF] Bhatia | Real-time identification of operating room state from video[END_REF][START_REF] Padoy | Statistical modeling and recognition of surgical workflow[END_REF][START_REF] Twinanda | EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos[END_REF] , steps [START_REF] Bouarfa | Discovery of high-level tasks in the operating room[END_REF][START_REF] James | Eye-Gaze Driven Surgical Workflow Segmentation[END_REF] ,and activities [START_REF] Ko | Surgery task model for intelligent interaction between surgeon and laparoscopic assistant robot[END_REF][START_REF] Lalys | Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures[END_REF]. According to the type of surgery, different modalities could be used for workflow recognition. For manual surgery, unless it is possible to add multiple sensors, workflow recognition is generally restricted to video-only modalities [START_REF] Bhatia | Real-time identification of operating room state from video[END_REF][START_REF] Bouarfa | Discovery of high-level tasks in the operating room[END_REF][START_REF] Lalys | Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures[END_REF]. In the case of robot-assisted surgery (RAS), kinematic information is easily available. It is expected that multi-modal data will lead to easier automatic recognition methods, as is the case for the combination of video and eye gaze information [START_REF] James | Eye-Gaze Driven Surgical Workflow Segmentation[END_REF] or the combination of video and kinematic information based on RAS data [START_REF] Zappella | Surgical gesture classification from video and kinematic data[END_REF]. However, some methods based on RAS data sets propose video-only methods [START_REF] Sarikaya | Surgical Gesture Recognition with Optical Flow only[END_REF][START_REF] Funke | Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video[END_REF] or kinematic-only methods [START_REF] Despinoy | Unsupervised trajectory segmentation for surgical gesture recognition in robotic training[END_REF][START_REF] Dipietro | Automated Surgical Activity Recognition with One Labeled Sequence[END_REF].

The "MIcro-Surgical Anastomose Workflow recognition on training sessions" (MISAW) challenge provided a unique data set for online automatic recognition of multi-granularity surgical workflows using kinematic and stereoscopic video information on a micro-anastomosis training task. The participants were challenged to develop uni-granularity (with phases, steps, or activities) and/ or multi-granularity workflow recognition models.

Methods: Reporting of Challenge Design

This section describes the challenge design through an explanation of the organization, the mission, the data set, and the assessment method of the challenge.

Challenge organization

The MISAW challenge was a one-time event organized as part of EndoVis Participation in the challenge was subject to the following policies: Participants had to submit a fully automatic method using kinematic and/or video data.

The data that could be used for the training were restricted to the data provided by the organizers and publicly available data sets, including pre-trained networks.

The publicly available data sets only covered data that were available to everyone when the MISAW data set was released. The results of all participating teams were announced publicly on the challenge day. Challenge organizers and people from the organizing institutions could participate but were not eligible for the competition.

The participating teams had to provide the following elements: the method's outputs, a write-up, and a Docker image allowing the organizers to verify the outputs provided. Due to the COVID-19 crisis, a pre-recorded talk was also mandatory to limit technical issues during the challenge day (online event). All technical information (how to create a Docker image, the output format, etc.) was provided to the participants during the challenge on the challenge platform.

The participants could submit multiple results and Docker images. However, only the last submission was officially counted to compute the challenge results. No leader-board or evaluation results were provided before the end of the challenge.

The challenge schedule was as follows:

The training and the test data sets were released on June 1st and August 24th 2020 respectively. Submissions were accepted until September 23rd (23:59 PST). The results were announced October 4th during the online MICCAI2020. The complete data set was released with this paper at: www.synapse.org/MISAW The organizers' evaluation scripts were publicly available on the challenge platform. Participating teams were encouraged (but not required) to provide their code as open access.

Mission of the challenge

The objective of the challenge was to automatically recognize the workflow of an anastomose performed during training sessions using video and kinematic data. The challenge was composed of four different tasks according to the granularity level recognized. Three of these tasks were uni-granularity surgical workflow recognition, i.e., the model had to recognize one of the three available granularity levels. They were noted task 1 for phase recognition, task 2 for step recognition, and task 3 for activities recognition. The last task, noted task 4, was a multi-granularity surgical workflow recognition, i.e., recognition of the three granularities with the same model. Each task offers a different difficulty, from the easiest (task 1) to the most complex (task 4). Due to the hierarchical decomposition of SPM, phases are more distinct between them than activities.

For example, an activity could have the same action verb and surgical tool but only differ by the target, whereas two phases represent two distinct main periods of the operation. Task 4 brings together the difficulty of the three other ones.

However, due to the hierarchical structure of the workflow description, it could help the recognition of the lower granularity. For example, some activities can only be present in specific phases or steps.

The challenge data were provided by a robotic system used to realize microsurgical anastomosis on artificial blood vessels through a stereoscopic microscope. Such micro-surgical anastomosis is performed in neurosurgery and plastic surgery.

The surgical robotic technologies developed for micro-surgical anastomosis can be applied to other robotic surgeries requiring dexterous manipulation on small targets. Automatic recognition of this task is an essential step to help the realization of this task or to increase robotic autonomy from manual to shared control or full automation [START_REF] Beer | Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction[END_REF].

The final biomedical application was robotic micro-surgical suturing of the dura mater during endonasal brain tumor surgery. Both applications were similar in the use of a robotic system, the microscopic dimension of the targets, and the surgical gestures. Furthermore, the automatic workflow recognition associated with analysis methods could help the surgical skill training by providing online feedbacks. For example, on a subset of the MISAW dataset, Huaulmé et al. [START_REF] Huaulmé | Sequential surgical signatures in micro-suturing task[END_REF] have identified sequences of activities, called surgical signatures, specific to the expertise or to common mistakes.

Challenge data set

The challenge data set was composed of 27 sequences of micro-surgical anastomosis on artificial blood vessels performed by [START_REF] Bhatia | Real-time identification of operating room state from video[END_REF]

Data acquisition

The video and kinematic data were synchronously acquired at 30 Hz by a high-definition stereo-microscope (960x540 pixels) and a master-slave robotic platform [START_REF] Mitsuishi | Master-slave robotic platform and its feasibility study for micro-neurosurgery: Master-slave robotic platform for microneurosurgery[END_REF], respectively, by the Department of Mechanical Engineering of the University of Tokyo. The kinematic data were recorded by encoders mounted on the two robotic arms. The kinematic data consisted of x, y, z, α, β, γ. The homogeneous transformation matrices for each robotic instrument were calculated as in equations 1 and 2. The kinematic files also contained information about the grip and the output grip voltage. The workflow annotation was acquired manually by two non-medical observers from the MediCis team of the LTSI Laboratory from the University of Rennes.

H right = T x (x)T y (y)T z (z)R x ( 1 18 π)R y (α)R x (β - 5 9 π)R y (γ)) (1)
H lef t = T x (x)T y (y)T z (z)R x (- 1 18 π)R y (α)R x (β + 1 18 π)R y (γ)) (2)
The observers used the software "Surgery Workflow Toolbox [annotate]" provided by the IRT b<>com [START_REF] Garraud | An Ontology-based Software Suite for the Analysis of Surgical Process Model[END_REF] to annotate the phases, steps, and activities (action verb, target, and instrument) of each robotic arm according to an annotation protocol. The vocabulary contained 2 phases, 6 steps, 10 action verbs, 9 targets, and 1 surgical instrument (Table 2). The protocol described how to recognize each phase, step, and activity of each robotic arm by giving a definition, start and end point, and graphical illustration. For example, the step "suture making" was defined by "insert and pull the needle into artificial vessels." The start point was the "beginning of the needle insertion on one vessel," the stop point was "the needle completely pass through both vessels." This is illustrated in Figure 1. The complete annotation protocol is available in Supplementary Material C. Each case was annotated by both observers independently and harmonized by the following protocol (Figure 2). An automatic merging was performed when the transition difference between both observers was less than one second (b in Figure 2). Here, the transition between red and blue components was inferior to the threshold, so the automatic merging took the mean. The transition between the blue and the green components took longer than one second, so no decision was made. The merging sequence came back to each observer separately to refine uncertain transitions (c). A second automatic annotation was performed with a threshold of 0.5 seconds (d). Finally, all remaining uncertainties were harmonized by a consensus between both observers.

Data pre-processing

We pre-processed the videos and initial workflow annotations to have consistent and synchronized data for each case. In the videos, the boundary between the left image and the right image was not consistent (Figure 3, i.e., the position of the centerline was a little different within and between the trials. We removed 40 pixels from the center of the stereoscopic image to have two images of 460x540 pixels. The final video resolution was 920x540 pixels. .

The software "Surgery Workflow Toolbox [annotated]" produced a description of sequences where each element was characterized by the beginning and the end in milliseconds. We modified it to provide a discrete sequence synchronized at 30Hz with the kinematic data. When no phase, step, or activity occurred, the term "Idle" was added. For each timestamp, we provided the following information: timestamp_number, phase_value, step_value, verb_Left_Hand, tar-get_Left_Hand, instrument_Left_Hand, verb_Right_Hand, target_Right_Hand, and instrument_Right_Hand.

Sources of errors

The main source of errors was the manual workflow annotation, which was observer-dependent. We limited these errors through the double annotations and the harmonization.

The second possible source of errors came from an acquisition issue. During acquisition, some timesteps were not acquired in either the video and kinematic information. This did not affect the synchronization of the data but could create activities not present in the procedural description. The impacted cases were 2-3, 4-2, 4-4, and 5-3.

Finally, due to some system problems during acquisition, the grip data were doubtful. If the system worked correctly, 0 meant "open" and -6 meant "close," but maybe values were under -6 in some trials.

These sources of errors were communicated to the participants with the training data set. The participants did not report any other issues.

Assessment method 2.4.1. Metrics

To assess the methods proposed by participants, we used a balanced version of the application-dependent scores [START_REF] Dergachyova | Automatic data-driven real-time segmentation and recognition of surgical workflow[END_REF] of the classic metric used in the workflow recognition: accuracy, precision, recall, and F1.

Our data sets had a high class unbalance, for example, the phase "Idle" represented around 2% of the frames in both data sets (Figure 4). To give the same importance to each class, we decided to use balanced scores. Generally, frame-by-frame scores were used. This type of score assumes that the ground truth is frame perfect. However, this is not possible with manual annotation. Moreover, a clinical application does not need to be 100% accurate at a frame resolution. Application-dependent scores re-estimate classic scores using acceptable delay thresholds for a transitional window (Figure 5). When the transition on the predicted sequence occurs with a transition delay T D

inferior to an acceptable delay d centered into the real transition, all frames are considered correct. Here, this was the case for the transition between the blue and green components. If the transition was different (case between red and green components in the prediction sequence) or outside this transition delay, no modification was done. We fixed the acceptable delay d at 500 ms, which corresponded to half of the duration used for the first automatic merge (Figure 2).

. Ranking method

We used a metric-based aggregation on the balanced application-dependent accuracy (AD-Accuracy) for the ranking. For each participant, we aggregated the metric values over all test cases and aggregated overall metrics to obtain a final score. We used a metric-based aggregation according to the conclusion of [START_REF] Maier-Hein | Why rankings of biomedical image analysis competitions should be interpreted with care[END_REF], who reported this type of aggregation as one of the most robust.

For the phase and step recognition (Tasks 1 and 2), the ranking score for algorithm a i was computed as follows:

s uni (a i ) = T t=0 balance_accuracy_case_t T (3)
Activity recognition (Task 3) consisted of recognizing the action verb, target, and instrument of each robotic arm. The ranking metric was computed as follows, with each component (i.e., s verb_Lef t (a i )) computed with Equation 3:

s activity (a i ) = 1 6 * (s verb_Lef t (a i ) + s T arget_Lef t (a i ) + s Instrument_Lef t (a i )+ s verb_Right (a i ) + s T arget_Right (a i ) + s Instrument_Right (a i )) (4)
For the multi-granularity recognition (Task 4) the ranking score was the mean of each uni-granularity score:

s multi (a i ) = s phase (a i ) + s step (a i ) + s activity (a i ) 3 (5)
All multi-granularity recognition models were also ranked in each uni-granularity task to highlight the differences between the models.

In the case of missing results, we considered results as good as a total random recognition. For example, for 3-class problem, the missing result would be set to 1/3, and for a 12-class problem, it would set to 1/12.

The ranking stability was assessed by testing different ranking methods. If, the ranking was not stable according to the method chosen, a tie between the different teams was pronounced.

The ranking computation and analysis were performed with the ChallengeR package provided by [START_REF] Wiesenfarth | Methods and open-source toolkit for analyzing and visualizing challenge results[END_REF].

Results: Reporting of the Challenge Outcomes

Challenge submission

At the end of September 2020, we counted 24 individual participants registered to the MISAW challenge and 325 downloads of the 9 files available (the Synapse platform did not give statistics by file). Five competing teams and one noncompeting team completed their submissions for the challenge.

Information on participating teams and corresponding methods

In this section, we will present information on each team, the methods they used, and which tasks they participated in. The presentation is made in alphabetical order of the competing teams and not in consideration of their ranking. Table 3 provides a summary of participants methods.

MedAIR

The They extracted high-level features from video frames using an 18-layer residual convolutional network [START_REF] He | Deep residual learning for image recognition[END_REF] followed by a fully connected layer and a ReLU nonlinearity layer applied at the end, yielding a 128-dimension spatial feature vector.

To learn the temporal information of the video data, they adopted a temporal convolution network (TCN) [START_REF] Lea | Segmental spatiotemporal CNNs for fine-grained action segmentation[END_REF],i.e., an encoder-decoder module, to further capture the information across frames, generating the representative spatialtemporal visual features. For the kinematic data, they first normalized the variables into [-1, +1], and then they used the TCN and long short-term memory (LSTM) [START_REF] Dipietro | Recognizing surgical activities with recurrent neural networks[END_REF] in parallel to learn and model the complex information of the left and right arms separately, yielding spatial-temporal motion features.

After acquiring the encoded high-level features from the video stream and kinematic data of the left and right arms, they used a graph convolutional network to further learn the joint knowledge among the multi-modal data. Considering that the visual information and left/right kinematic information contained fruitful interactions and relationships, they designed a graph convolutional network (GCN) with three node entities corresponding to the video, left kinematics, and right kinematics, with all three nodes connected to each other. Initialized with these three modalities, the node features of the GCN were updated by receiving the message from neighbor connected node features and then encoding stronger information in the newly generated node features. Then, the authors max pooled the features from the three nodes and forwarded them into a fully connected layer to get the prediction results of the workflow recognition. For more details, you can refer to [START_REF] Long | Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery[END_REF].

Two different approaches were employed to further enhance the temporal consistency of the workflow recognition. The authors filtered out the frames with low prediction probabilities using a median filter and leveraged the information of the preceding 600 frames with 30 fps. They also employed an online postprocessing strategy (PKI) [START_REF] Jin | Workflow recognition from surgical videos using recurrent convolutional network[END_REF] that leveraged the workflow of the phases and steps. For example, the steps followed a specific order: "Needle holding", "Suture making", "Suture handling", "1 knot", "2 knot", and "3 knot", and it were not likely to be reversed or shuffled.

NUSControl Lab

The NUSControl Lab team was composed of Chin Boon Chng 1 , Wenjun Lin 1,2 , Jiaqi Zhang 1 , Yaxin Hu 1 , Yan Hu 1 , Liu Jiang Jimmy 2 , and Chee Kong Chui 1 . The participants noted with "1" were from the National University of Singapore (NUS), Singapore, Singapore; the participants noted with "2" were from the Southern University of Science and Technology, Shenzhen, China. This team participated in the multi-granularity task. As described in the subsection "Ranking method" (2.4.2), the model was also ranked in each uni-granularity task.

The NUSControl Lab team used both the video and kinematic data. They first extracted the features of the video frames using EfficientNet [START_REF] Tan | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks[END_REF]. Then, they employed an LSTM module to model the sequential dependencies of the video data. The authors hypothesized that the kinematic data were specifically related to the verbs and steps. With this motivation, they employed another LSTM module to model the sequential features of the left and right arm kinematic data, which was then concatenated and fed into a fully connected layer to predict the verbs (left and right) and the steps. Their network model was based upon the work of Jin et al. [START_REF] Jin | Multi-task recurrent convolutional network with correlation loss for surgical video analysis[END_REF].

The authors also employed a post-processing step that made used of the workflow observations to further improve the predictions. For example, if a knot is to be tied, a loop must first be made, followed by pulling the wire. Thus, the verb "make a loop" could be used to indicate when a new knot is being tied.

Similarly, the verb "pull" could be used to indicate when the new knot has been completed. The authors proposed to mark the verb "make a loop" as a transition signal to the next knot and "pull" as a completion signal of this knot. If the model classified the current task to be "making a loop" and the phase turned to knot tying, the knot step was incremented. This knot step was identified to start from the previous "pull" prediction and continue until the next "pull" prediction.

SK

The SK team was composed of Satoshi Kondo from Konica Minolta, Inc. This team participated in the multi-granularity task. As described in the subsection "Ranking method" (2.4.2), the model was also ranked in each uni-granularity task.

The SK team used the video data, kinematic data, and time information as the input for the model. The video frame features were extracted using a 50-layer ResNet [START_REF] He | Deep residual learning for image recognition[END_REF] pre-trained with the ImageNet data set, which led to a 2,048-dimension feature vector. While the team used only the left stereo video frame, the kinematic data features: x, y, z, α, β, γ, and grip collected from the left and right arms were used, leaving the output voltage for the grip feature out. The kinematic data were normalized with the mean and standard deviation values for each dimension and then fed to two fully connected layers.

The team also employed the frame number as a means of time information.

The frame number was divided by 10,000. The feature vector of the input image, the feature vector of the kinematic data, and the frame number were concatenated, which led to a 2,063-dimension feature vector for a single frame.

Then, the author performed multi-granularity recognition wherein the network learned the tasks,i.e., phase, step, and activity. For each activity, the verb, the target, and the tool for the left and right arms were learned, which resulted in a total of eight classes. The loss function was the summation of softmax crossentropy for these eight classes, and the team employed a Lookahead optimizer [START_REF] Zhang | Lookahead Optimizer: k steps forward, 1 step back[END_REF].

UniandesBCV

The UniandesBCV team was composed of Laura Bravo-Sánchez, Paola Ruiz The UniandesBCV team only used the video data and proposed a model that leveraged the implicit hierarchical information in the surgical workflow. The model presented by the authors was based on SlowFast [START_REF] Feichtenhofer | SlowFast Networks for Video Recognition[END_REF], a neural network that uses a slow and a fast pathway to model semantic and temporal information within videos. To accomplish this discrimination of information, each of the pathways analyzed the video at a different sampling rate. The slow pathway used a low frame rate with a large number of channels, while the fast pathway employed a high frame rate and only a fraction of the channels. To make a prediction based on the complete information (semantic and temporal), the fast pathway fused with the slow one using several lateral connections at different points of the network.

The authors first extracted the features of the video frames using ResNet-50 backbone [START_REF] He | Deep residual learning for image recognition[END_REF] and fed the features of 64-frame windows into a SlowFast model adapted for multi-task training that was pre-trained on the Kinetics data set [START_REF] Kay | The Kinetics Human Action Video Dataset[END_REF]. The authors explored different multi-task hierarchical groupings: The first model simultaneously predicted both phases and steps, the second model predicted activities, and the last model predicted all the components of the multi-granularity recognition. During training, the team also introduced an extra term to the loss function for optimizing the task added at each grouping and balanced the relevance of each task by associating each of the loss's terms to a weight. The authors reported that merging all the components of the multi-granularity recognition tasks improved the learning ability of the model and obtained more accurate predictions.

Wr0112358

Team wr0112358 was composed of Wolfgang Reiter from Wintegral GmbH.

This team participated in all proposed tasks.

Team wr0112358 only used the video data, reporting that the kinematic data did not significantly contribute to the performance of the model. The team also explored different architectures, including ResNet50 [START_REF] He | Deep residual learning for image recognition[END_REF] and multi-stream Siamese networks with temporal pooling [START_REF] Chung | A Two Stream Siamese Convolutional Neural Network For Person Re-Identification[END_REF], but reported that due to the high imbalance and the relatively small size of the data set, the complex architectures resulted in overfitting. The author also ruled out using an LSTM approach for the same reason.

The team decided to employ a multi-task convolutional neural network [START_REF] Twinanda | EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos[END_REF] and extracted the features of the video frames using a DenseNet121 CNN with data augmentation and regularization, which reduced overfitting. The author enhanced this architecture with task-wise early stopping [START_REF] Zhang | Facial landmark detection by deep multi-task learning[END_REF] and also reported that using either of the stereo video frames resulted in a similar performance.

IMPACT

The IMPACT team was the non-competing team due to the presence of challenge organizers on it. This team was composed of Arnaud Huaulmé and Then, each input modality was processed into a dedicated network branch to leverage the different types of data. While the video frames were passed to a VGG19 network [START_REF] Simonyan | Two-stream convolutional networks for action recognition in videos[END_REF], the kinematic data were passed to an adapted ResNet network [START_REF] Ismail Fawaz | Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks[END_REF]. The last convolutional layer of each modality branch was finally concatenated into a common branch before being split again into separated workflow branches containing their own activation layers (1 for the phase and step recognition, 6 for the activity recognition, and 8 for the multi-granularity recognition).

The VGG19 network was initialized with the weights of a pre-trained model on the ImageNet data set. Since the MISAW data set was acquired in phantom surgical settings, the IMPACT team retrained only the last two layers to refine the network for this task. Regarding the kinematic branch, the network was trained "from scratch" without any previous weight configuration. In the end, the training was achieved using an Adam optimizer and a starting learning rate of 0.0001. 3: Participating teams and their methods summary. On modalities, V is for video, K for Kinematic.

Workflow recognition results

Even if the participants submitted the method outputs for each test case, all of the following results were computed on organizer hardware via the provided Docker images. We did not detected any fraud attempts in the results provided by the participants.

This section only presents the results used for the ranking. Detailed results by sequence and task of each participating team are available in Supplementary Material B.

Task 1: Phase recognition

Phase recognition is a three-class task. We received 4 uni-granularity and 5 multi-granularity models for this task; the latter were identified with the addition of "_multi" at the end of the team name.

Figure 6 presents the results of all algorithms for each test sequence. The average AD-Accuracy by sequence was between 77.7% and 84.7%, which demonstrated that the recognition difficulty was similar between the sequences, except for sequence 5_6. However, we noticed that for all the test cases, 2 models had an AD-Accuracy lower than 65%. only the uni-granularity model of IMPACT had an outlier lower than 70%, while the average AD-Accuracy was 80.66%. Is it also interesting to note that the multi-granularity model of this team was slightly better than the uni-granularity one. For the phase recognition, the choice of method did not influence the ranking, except for the multi-granularity models of teams IMPACT and wr0112358, which swapped the fifth and sixth places.

Task 2: Step recognition

Step recognition is a 7-class task. We received 4 uni-granularity and 5 multigranularity models for this task; the latter were identified with the addition of "_multi" at the end of the team name.

The average AD-Accuracy by sequence was between 51.2% and 64.4% (Figure 9). Contrary to the phase recognition, there was no sequence with a significantly lower score. We noticed that for all sequences, at least one model outperformed the others. The ranking method chosen did not impact the final rank (Figure 11). multi-granularity models for this task; the latter were identified with the addition of "_multi" at the end of the team name.

The average AD-Accuracy score by sequence was between 55.1% and 63.4%

(Figure 12). As for the step recognition, all sequences had similar results.

However, for session 4_4, one model had an AD-Accuracy lower than 40%. The average AD-Accuracy by model was between 52.4% and 61.6% 13. Four models, three of which were multi-granularity ones, had results over 60%. According to the ranking method (Figure 14), the ranking was always different for the top four models. For this task, we defined a tie between the NUSControl Lab and UniandesBCV teams (IMPACT was a non-competitive team). The average AD-Accuracy score by sequences was between 59.6% and 66.4%

(Figure 15). Surprisingly, these results were slightly better than those for the activity recognition although this task also demanded recognition of phases and steps. The average AD-Accuracy by model was between 49.1% and 76.8% 16. The model of NUSControl Lab outperformed the models of the other teams, with a recognition rate 12 points higher than the second competing team (IMPACT had a better result than wr0112358 team but was not competing). The team ranking was not impacted by the ranking method (Figure 17).

Discussion

Surgical workflow recognition is an important challenge in providing automatic context-aware computer-assisted surgical systems. However, as demonstrated by the different models proposed in this challenge, there remains a lot of room for improvement. For a high level of granularity (phases and steps), the best models have a recognition rate that may be sufficient for applications such as prediction of remaining surgical time or resource management. However, for activities, the recognition rates are still insufficient to propose clinical applications.

For all tasks, the decrease between the sequence with the best recognition rate and the one with the lowest was linear. The difference between the best and the worst recognition rate was 7 points for phase recognition, 13.2 for step recognition, 8.3 for activity recognition, and 6.8 for multi-granularity recognition.

Only sequence 5_6 for phase recognition presented a recognition gap of 3 points with the penultimate sequence (Figure 6). After a review of this sequence, the major difference was a high representation of the "idle" phase (7.13% for sequence 5_6 compared to 2.49± 1.22% for the other test cases) to the detriment of the "suturing" phase (36.12% compared to 45.14±10.36%) for a similar total duration (79s vs. 99s ± 52s). However, this cannot be the only reason for this low recognition rate. In the future, it could be interesting to study the explainability of the different networks.

For the image modality, all teams proposed a model based on convolutional neural networks (CNNs) such as ResNet, and VGG, two of them also combined a recurrent neural network (RNN) as LSTM. For the kinematic modality, two teams used CNN, one used RNN, and another used a combination of both. The teams wr0112358 and UniandesBCV did not use this modality. According to the results, the use of RNNs seemed to be more relevant than that of CNNs only. However, both teams that used them also performed post-processing to improve the recognition rate, so it was difficult to evaluate the role of the RNNs and post-processing.

For the phases and step recognition tasks, the multi-granularity models had globally worse performances than the uni-granularity models, even for the teams who proposed both models on the same architecture. The only exception was for IMPACT, but the results were quite similar between the team's models. For the activity recognition, it was the opposite: 3 of the 4 top models were multigranularity ones. Two reasons could explain this fact. First, the activity and multi-granularity models had to recognize multiple components at the same time (6 and 8 components, respectively), whereas the other models only recognized 1 component. Second, the majority of activities could only appear on a specific phase or step. For example, the activity consisting of inserting a needle on the right artificial vessel with a needle holder (noted <insert, right artificial vessel, needle holder>) could only appear on the phase "suturing" and, specifically, on the step "suture making". So, the multi-granularity models could learn these relations to improve their performances for activity recognition.

One of the most surprising results of the challenge was the similar recognition rate between the video-based models and the multi-modality-based models (using both videos and kinematics). Team wr0112358 reported that the kinematic data did not significantly contribute to the performance of their model. This was confirmed by the ranking of this team (top 3 for the phase and step recognition tasks with a dedicated model and top 3 for the multi-granularity task). The UniandesBCV team also used only the video modality, and also had good ranking, especially for activity recognition, with a tie for first place between both models proposed. However, it is impossible to know whether these results come from the models used by the participants or from the lack of information provided by the kinematic data. A more robust and systematic study would clarify this by the understanding of the models and the contribution of each modality.

The first main limitation was the unbalanced distribution of cases by expertise level (11 performed by experts, 16 by engineering students) due to the different number of cases by participants (between 3 and 6 cases). We split the data set to have a similar distribution between the training and test data sets to limit the impact of this unbalanced distribution.

The second main limitation was the release of the video and kinematic data of the test cases during the challenge. This choice was dictated by the organizers' lack of knowledge of Docker images and the lack of hardware available when the challenge was proposed to EndoVis and MICCAI. So, we wanted to be able to use the results provided by the participants if necessary. Finally, all results were computed on the organizers' hardware via Docker images. With the test cases release, we first asked unnecessary works to teams; the time spent running the results could have been dedicated to the improvement of the methods. Moreover, this early release could have allowed the participants to make their own manual annotations and use them for the training. Even if these annotations were different than those by the organizer, it opened a breach for undetectable fraud.

In addition to confirming the superiority of RNNs compared to CNNs with same post-processing method and studying the impact of each modality, future work could explore more complex networks such as hierarchical models. Indeed, the granularity description is hierarchic (a step belongs to a phase; some activities only appear on specific steps), so this type of model could improve the recognition.

Enlarging the data set with more sessions, more modalities, and more sources of data (other systems, virtual reality simulators, real surgeries, etc.) is also being considered for a second version of the MISAW challenge.

  for MICCAI2020 online. It was organized by five people from three different institutions: Arnaud Huaulmé, Kévin Le Mut, and Pierre Jannin from the University of Rennes (France), Duygu Sarikaya from Gazi University (Turkey), and Kanako Harada from the University of Tokyo (Japan). The challenge was partially funded by the ImPACT Program of the Council for Science, Technology and Innovation, Cabinet Office, Government of Japan. All challenge information was made available to the participants through the Synapse platform: www.synapse.org/MISAW .

Figure 1 :

 1 Figure 1: Representation of the beginning (left) and the end (right) of the "suture making" step.

Figure 2 :

 2 Figure 2: Harmonization protocol used to merge the annotations made by two observers.

Figure 3 :

 3 Figure 3: Comparison between the initial video (3a) and pre-processed video (3b)

Figure 4 :

 4 Figure 4: Phase distribution in training and testing data sets

Figure 5 :

 5 Figure 5: Definition of acceptable and transitional delay used to compute the average-dependent scores

  MedAIR team was composed of Yonghao Long and Qi Dou from the Department of Computer Science and Engineering at the Chinese University of Hong Kong. They participated in the phase and step recognition tasks. The MedAIR team used both the video frames and the kinematic data of the left and right robotic arms, treating them separately because different arms may conduct different actions to jointly complete a task.

  Puentes, Natalia Valderrama, Isabela Hernández, Cristina González, and Pablo Arbeláez. All members were from the Center for Research and Formation in Artificial Intelligence and the Biomedical Computer Vision Group at the Universidad de los Andes, Colombia. This team participated in all proposed tasks.

Fabien

  Despinoy both from Rennes University, INSERM, LTSI -UMR 1099 and Duygu Sarikaya from Gazi University, Faculty of Engineering, Department of Computer Engineering. The team participated in all proposed tasks.The IMPACT team used both the right video frames and kinematic data and proposed a multi-modal architecture. The authors applied a pre-processing step to the input data. While the right video images were rescaled from 460x540 to 224x224 and the pixel values were normalized by subtracting the mean of each channel over the training set and scale between [0,1], the authors applied a z-normalization to center the kinematic data to 0 with a standard deviation equal to 1. To make the training step faster, the data were downsampled to 5Hz.

Figure 6 :

 6 Figure 6: Phase recognition AD-Accuracy for each sequence. Each dot represents the AD-Accuracy for one model.

Figure 7

 7 Figure 7 presents the average AD-Accuracy for each model.The MedAIR team got an average AD-Accuracy of 96.53%. The multi-granularity models of the UniandesBCV and SK teams presented results lower than 65%. Overall,

Figure 7 :

 7 Figure 7: Average phase recognition AD-Accuracy for each model. Each dot represents the AD-Accuracy for one sequence.

Figure 8

 8 Figure 8 presents the different rankings according to the method chosen.

Figure 8 :

 8 Figure 8: Phase recognition ranking stability through different ranking methods.

Figure 9 :

 9 Figure 9: Step recognition AD-Accuracy for each sequence. Each dot represents the AD-Accuracy for one model.

Figure 10 :

 10 Figure 10: Average step recognition AD-Accuracy for each model. Each dot represents the AD-Accuracy for one sequence.

  458

Figure 11 :

 11 Figure 11: Step recognition ranking stability through different ranking methods.

Figure 12 :

 12 Figure 12: Activity recognition AD-Accuracy for each sequence. Each dot represents the AD-Accuracy for one model.

Figure 13 :

 13 Figure 13: Average activity recognition AD-Accuracy for each model. Each dot represents the AD-Accuracy for one sequence.

Figure 14 :

 14 Figure 14: Phase recognition ranking stability through different ranking methods.

Figure 15 :

 15 Figure 15: Multi-granularity recognition AD-Accuracy for each sequence. Each dot represents the AD-Accuracy for one model.

Figure 16 :

 16 Figure 16: Average multi-granularity recognition AD-Accuracy for each model. Each dot represents the AD-Accuracy for one sequence.

Figure 17 :

 17 Figure 17: Multi-granularity recognition ranking stability through different ranking methods.

Table 1 :

 1 Training and test case splitting

	surgeons and 3 engineering

Table 2 :

 2 MISAW vocabulary.

	Phases	Steps	Verb	Activities Target	Instrument
	Suturing	Needle holding	Catch	Needle	Needle holder
	Knot Tying	Suture making	Give slack	Wire
		Suture handling	Hold	Both artificial vessel
		1 • knot	Insert	Left artificial vessel
		2 • knot	Loosen completely Right artificial vessel
		3 • knot	Loosen partially	Long wire strand
			Make a loop	Short wire strand
			Pass through	Wire loop
			Position	Knot
			Pull

Table

	Team name	Tasks Modalities	Networks	Post processing
	MedAIR	1, 2	V + K	CNN, TCN, RNN	PKI
	NUSControl Lab	4	V+K	CNN, RNN	Workflow
	SK	4	V+K	CNN
	UniandesBCV	All	V	SlowFast, CNN
	Wr0112358	All	V	CNN
	IMPACT	All	V+K	CNN

Table 4

 4 summarizes the results of each model on all tasks.

	487

Table 4 :

 4 Average AD-Accuracy of each model for all tasks. On model, uni is for models dedicated to uni-granularity recognition, multi for models able to recognize all modalities. Best results are highlighted in bold for each task.

Acknowledgements

This work was partially by ImPACT Program of Council for Science, Technology and Innovation, Cabinet Office, Government of Japan.

Authors thanks the IRT b<>com for the provision of the software "Surgery Workflow Toolbox [annotate]" , used for this work.

Statements of ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This articles does not contain patient data.

Conflict of interest statement

The authors declare that they have no conflict of interest.


Corentin Louis

Cédric Coulouarn
email: cedric.coulouarn@inserm.fr

One stone, two birds: circACTN4, a nexus for a coordinated activation of Hippo and Wnt/β-catenin pathways in cholangiocarcinoma

Keywords: circACTN4, Circular RNA, Cholangiocarcinoma, Signaling pathways

Cholangiocarcinomas (CCA) gather a group of tumors arising along the hepatobiliary tree. CCA is a rare cancer, but its incidence has been increasing over the last decade. CCA is associated with a poor prognosis and limited effective therapeutic options [START_REF] Banales | Cholangiocarcinoma 2020: the next horizon in mechanisms and management[END_REF]. Innovative strategies are therefore urgently needed to identify novel therapeutic targets and clinically relevant biomarkers to ultimately improve the survival of patients with CCA [START_REF] Banales | Cholangiocarcinoma 2020: the next horizon in mechanisms and management[END_REF]. Circular RNAs (circRNAs) are splicing products forming a continuous loop by joining the 3' extremity of a downstream exon with the 5' extremity of an upstream exon. Although circRNAs are mainly outlined for their ability to sponge microRNAs (miRNAs) and RNA binding proteins (RBPs), their modes of action remain largely unknown [START_REF] Kristensen | The biogenesis, biology and characterization of circular RNAs[END_REF]. Latterly, circRNAs emerged as new regulatory molecules contributing to human carcinogenesis with a promising potential as biomarkers [START_REF] Kristensen | The biogenesis, biology and characterization of circular RNAs[END_REF]. However, very few studies reported the role of circRNAs deregulated in CCA so far [START_REF] Li | Circular RNA CDR1as Exerts Oncogenic Properties Partially through Regulating MicroRNA 641 in Cholangiocarcinoma[END_REF][START_REF] Merdrignac | A novel transforming growth factor beta-induced long noncoding RNA promotes an inflammatory microenvironment in human intrahepatic cholangiocarcinoma[END_REF][START_REF] Merdrignac | Long Noncoding RNAs in Cholangiocarcinoma[END_REF][START_REF] Xu | Circ-LAMP1 contributes to the growth and metastasis of cholangiocarcinoma via miR-556-5p and miR-567 mediated YY1 activation[END_REF][START_REF] Xu | A Circular RNA, Cholangiocarcinoma-Associated Circular RNA 1, Contributes to Cholangiocarcinoma Progression, Induces Angiogenesis, and Disrupts Vascular Endothelial Barriers[END_REF]. Notably, an increased expression of circ-CCAC1 has been identified by circRNA expression profiling in tumor tissues and bile-resident extracellular vesicles (EVs) from patients with CCA [START_REF] Xu | A Circular RNA, Cholangiocarcinoma-Associated Circular RNA 1, Contributes to Cholangiocarcinoma Progression, Induces Angiogenesis, and Disrupts Vascular Endothelial Barriers[END_REF]. CCA-delivered exosomal circ-CCAC1 was reported to be transferred to endothelial cells to disrupt endothelial barriers and induce angiogenesis [START_REF] Xu | A Circular RNA, Cholangiocarcinoma-Associated Circular RNA 1, Contributes to Cholangiocarcinoma Progression, Induces Angiogenesis, and Disrupts Vascular Endothelial Barriers[END_REF]. Mechanistically, circ-CCAC1 increased cell permeability by sequestering enhancer of zeste homolog 2 (EZH2) in the cytoplasm, thereby increasing SH3GL2 expression to suppress intercellular junction proteins [START_REF] Xu | A Circular RNA, Cholangiocarcinoma-Associated Circular RNA 1, Contributes to Cholangiocarcinoma Progression, Induces Angiogenesis, and Disrupts Vascular Endothelial Barriers[END_REF]. Besides this well-designed functional study, most of the published reports highlighting circRNAs in CCA solely focused on the clinical relevance of their expression. Thus, a comprehensive analysis of their functional role in the gene regulatory networks contributing to CCA onset and progression has been barely addressed so far. In addition, 80-90% of reports focused on the sponging capacity of circRNAs as a mechanism of action. We would like to point out that circRNAs are more than simple sponges for miRNAs. Indeed, emerging data from the literature demonstrate that circRNAs take active part of complex transcriptional and post-transcriptional regulatory systems.

In this issue of Journal of Hepatology, Chen and colleagues illustrate this concept by investigating the function and the clinical relevance of circular RNA ACTN4 (circACTN4) in J o u r n a l P r e -p r o o f intrahepatic CCA (iCCA). They demonstrate that circACTN4 is induced in iCCA and promotes proliferation and invasion of tumor cells in vitro and in vivo. Importantly, the study implies circACTN4 as a signaling nexus allowing a coordinated activation of Hippo and Wnt/β-catenin pathways, notably by acting on different targets in the cytoplasm and the nucleus (Figure 1). Indeed, circACTN4 is reported i) in the cytoplasm, to sponge miR-424-5p, which could target the oncogenic transcriptional coactivator Yes-associated protein 1 (YAP1) from the Hippo pathway and ii) in the nucleus, to recruit Y-Box Binding Protein 1 (YBX1) transcription factor to initiate the transcription of Frizzled-7 (FZD7), a positive regulator of the Wnt/β-catenin signaling pathway [START_REF] Chen | Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription[END_REF]. CircACTN4 is also shown to favor the interaction between YAP1 and β-catenin and their nuclear accumulation, which ultimately leads to CCA cell growth and metastasis (Figure 1).

Thus, this study exemplifies circRNAs as sponges for miRNAs and as scaffolds to locate transcription factors to the nucleus. The data suggest that the specific location of circRNAs within the cells could play a significant role in controlling gene expression. Overall, the study by Chen and colleagues unraveled circRNAs as intracellular nodes acting simultaneously in the cytoplasm and in the nucleus through specific regulatory processes. However, this study raises unanswered questions. Notably, the dynamic regulation of circRNAs shuttling between the nucleus and the cytoplasm, in relationship to their mode of action, is an important feature which remains to be elucidated. In addition, whether or not circACTN4 acts as a scaffold by interacting with DNA and/or other co-factors to recruit YBP1 to the promoter of FZD7 remains to be determined.

The mechanisms regulating the spatial localization of circRNAs are yet poorly understood, although this parameter is probably of special importance in determining their activity. The reason why intron-containing circRNAs are restrained to the nucleus to act as transcriptional regulators while exonic circRNAs are enriched in the cytoplasm to operate as post-transcriptional regulators by sponging miRNAs and/or RBPs is unclear [START_REF] Chen | The expanding regulatory mechanisms and cellular functions of circular RNAs[END_REF]. Several reports explored possible mechanisms whereby circRNAs could shuttle from the nucleus to the cytoplasm. One of them J o u r n a l P r e -p r o o f described an evolutionarily conserved pathway that controls the nuclear export of circRNAs depending on the length of the transcripts. Hence, long (> 800 nt) or short circRNAs could be translocated to the cytoplasm with the assistance of UAP56 or URH49, respectively [START_REF] Huang | A length-dependent evolutionarily conserved pathway controls nuclear export of circular RNAs[END_REF]. Wang and colleagues described a nuclear export process specific for a circular repeat containing intron. They identified a NXF1:NXT1-related active process regulating the level of intronic circRNAs in the cytoplasm [START_REF] Wang | Nuclear export and translation of circular repeat-containing intronic RNA in C9ORF72-ALS/FTD[END_REF]. Another study pinpointed N 6 -methyladenosine (m6A) epitranscriptomic modifications favoring the export of circNSUN2 to the cytoplasm [START_REF] Chen | N(6)-methyladenosine modification of circNSUN2 facilitates cytoplasmic export and stabilizes HMGA2 to promote colorectal liver metastasis[END_REF]. These studies highlight the importance of better characterizing the mechanisms regulating circRNA spatial localization to better understand the underlying molecular mechanisms involved in CCA progression. In the future, the modulation of such transport mechanisms could be considered as an option to modulate circRNA availability and activity.

When it comes to circRNAs, most studies describe a cytoplasmic circRNA sequestrating a couple of miRNAs, which are therefore not available to inhibit their predicted target genes. However, it is becoming obvious from emerging functional studies that investigating the molecular mode of action of circRNAs should not be considered through a unique axis. Indeed, several reports, including the one from Chen and colleagues in this issue of Journal of Hepatology [START_REF] Chen | Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription[END_REF], established that circRNAs could operate through a multi-layered coordinated fashion, by acting at different levels on several gene regulatory networks. The ability of a single circRNA to concomitantly sponge miRNAs in the cytoplasm and to recruit RBPs in the nucleus to mediate RNA Pol II transcription has been also reported in gastric cancer [START_REF] Fang | A novel circular RNA, circFAT1(e2), inhibits gastric cancer progression by targeting miR-548g in the cytoplasm and interacting with YBX1 in the nucleus[END_REF]. Hence, circFAT1 exerts tumor suppressive actions by regulating the miR-548g/RUNX1 axis in the cytoplasm and by targeting YBX1 in the nucleus to inhibit its activity [START_REF] Fang | A novel circular RNA, circFAT1(e2), inhibits gastric cancer progression by targeting miR-548g in the cytoplasm and interacting with YBX1 in the nucleus[END_REF]. However, the process regulating circFAT1 spatial localization is unknown [START_REF] Fang | A novel circular RNA, circFAT1(e2), inhibits gastric cancer progression by targeting miR-548g in the cytoplasm and interacting with YBX1 in the nucleus[END_REF]. Factors regulating RNA circularization, such as QKI5 RBP, could also act at a post-transcriptional level to competitively regulate the level of canonical and back-splicing. That competition could be explained by a shared spliceosome machinery for intron and exon definition and back-splicing [START_REF] Conn | The RNA binding protein quaking regulates formation of circRNAs[END_REF][START_REF] Li | A unified mechanism for intron and exon definition and back-splicing[END_REF]. Although initially viewed as J o u r n a l P r e -p r o o f 6 non-coding RNAs, growing evidence indicate that circRNAs could also be translated, notably by cap-independent-mediated mechanisms that rely on the internal ribosome entry site and m6A modifications [START_REF] Timoteo | Modulation of circRNA Metabolism by m(6)A Modification[END_REF]. Characterizing the function of circRNAs-derived peptides is still at its infancy, but several reports described a role as protein baits [START_REF] Zhao | Protein Bait Hypothesis: circRNA-Encoded Proteins Competitively Inhibit Cognate Functional Isoforms[END_REF]. An original mode of action of circβcatenin, a circRNA derived from β-catenin has been recently reported [START_REF] Liang | Translation of the circular RNA circbeta-catenin promotes liver cancer cell growth through activation of the Wnt pathway[END_REF]. Circβ-catenin differs from its cognate mRNA by a new stop codon generated by the circularization junction. Thus, circβ-catenin is translated into a 370-amino acid β-catenin isoform which acts as a decoy.

Indeed, this shorter isoform can stabilize full-length β-catenin by antagonizing GSK3β-induced βcatenin phosphorylation and degradation, leading to Wnt/β-catenin pathway activation and promotion of tumor cell growth [START_REF] Liang | Translation of the circular RNA circbeta-catenin promotes liver cancer cell growth through activation of the Wnt pathway[END_REF]. In breast cancer, circSMARCA5 was shown to combine several of the functions described above. Indeed, circSMARCA5 binds its parental gene locus forming a R-loop with DNA and regulates concomitantly the splicing machinery and the transcriptional pausing, leading to the production of a nonfunctional truncated SMARCA5 protein [START_REF] Xu | CircRNA inhibits DNA damage repair by interacting with host gene[END_REF]. Eventually, growing evidence suggest that circRNAs act as important cellular regulators with various modes of action. However, how many functions a single circRNA can fulfill needs to be determined. This is a fundamental point to address before thinking of circRNAs for a clinical translation as therapeutic targets, as suggested by Chen and colleagues for circACTN4.

Chen and colleagues reported in this issue of Journal of Hepatology that a high expression of circACTN4 in iCCA tissues was associated with a worse survival [START_REF] Chen | Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription[END_REF]. Although numerous circRNAs are already described for their potential as prognostic biomarkers, none have been proved to be worth for a clinical use. Nonetheless, circRNAs attracted a substantial interest by their presence in body fluids (e.g. serum, plasma, saliva) circulating either freely or encapsulated into EVs, particularly in exosomes (100-200nm EVs) [START_REF] Li | Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis[END_REF]. Moreover, circRNAs display an expanded half-life compared to linear RNAs, suggesting they could challenge the sensitivity and specificity of standard biomarkers. Thus, measuring the level of circulating circRNAs in liquid J o u r n a l P r e -p r o o f biopsies represents an appealing alternative to tissues biopsies to improve the management of patients with CCA [START_REF] Li | Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis[END_REF].

Taken collectively, the study by Chen and colleagues provides an elegant example on how circRNAs could contribute to a coordinated activation of signaling pathways involved in CCA progression. With other pioneer studies in the field, it highlights the promising potential of circRNAs as targets to develop innovative therapeutic strategies and to identify clinically relevant biomarkers. Nevertheless, the field is still in its infancy, with many unanswered questions, notably as regard to the exact functions and modes of action of circRNAs in the gene regulatory network contributing to tumor progression. Integrating coding and non-coding RNA profiles could lead to a comprehensive picture of circRNA/miRNA/mRNA networks and to better understand the underlying molecular mechanisms involved in CCA tumorigenesis. Hepatology, Chen and colleagues report the role and the clinical relevance of circACTN4 in CCA [START_REF] Chen | Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription[END_REF]. The study demonstrates that circACTN4 is induced in CCA and exhibits specific but complementary functions depending on its subcellular localization. Thus, in the cytoplasm circACTN4 acts as a sponge sequestrating miR-424-5p and therefore preventing the degradation of YAP1, from the Hippo pathway. In the nucleus, circACTN4 recruits YBX1 to initiate FZD7 transcription and contributes to activate the Wnt/β-catenin pathway. Altogether, the coordinated activation of Wnt/β-catenin and Hippo signaling pathways by circACTN4 contributes to CCA cell growth and metastasis.

Figure 1 .

 1 Figure 1. Squaring the circle: emerging roles of circRNAs in CCA. In this issue of Journal of

CC is supported by Inserm, Université de Rennes 1, Ligue Contre le Cancer (CD22, CD35, CD85), INCa, and ITMO Cancer AVIESAN ((Non-coding RNA in cancerology: fundamental to translational) and Fondation ARC pour la recherche sur le cancer. CL is supported by a PhD fellowship from Université de Rennes 1 and Fondation ARC pour

Authors' contributions: All authors contributed equally to the intellectual content of the manuscript. All authors wrote the initial draft and contributed significantly to the final version.

Writing Assistance: none

Abbreviations: CCA, cholangiocarcinoma; circRNA, circular RNA; EVs, extracellular vesicles; miRNA, microRNA; RBP, RNA binding protein.

J o u r n a l P r e -p r o o f