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Background: class III obese women, are at a higher risk of cesarean section during labor, and cesarean section is responsible for increased maternal and neonatal morbidity in this population., Objective: the objective of this project was to develop a method with which to quantify cesarean section risk before labor., Methods: this is a multicentric retrospective cohort study conducted on 410 nulliparous class III obese pregnant women who attempted vaginal delivery in two French university hospitals. We developed two predictive algorithms (a logistic regression and a random forest models) and assessed performance levels and compared them., Results: the logistic regression model found that only initial weight and labor induction were significant in the prediction of unplanned cesarean section. The probability forest was able to predict cesarean section probability using only two pre-labor characteristics: initial weight and labor induction. Its performances were higher and were calculated for a cut-point of 49.5% risk and the results were (with 95% confidence intervals): area under the curve 0.70 (0.62,0.78), accuracy 0.66 (0.58, 0.73), specificity 0.87 (0.77, 0.93), and sensitivity 0.44 (0.32, 0.55)., Conclusions: this is an innovative and effective approach to predicting unplanned CS risk in this population and could play a role in the choice of a trial of labor versus planned cesarean section. Further studies are needed, especially a prospective clinical trial., Funding: French state funds "Plan Investissements d'Avenir" and Agence Nationale de la Recherche., Competing Interests: Declaration of Competing Interest The authors have no competing interests to declare., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Background: Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis., Methods: This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times., Results: Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43-0.54) and 0.51 (95% CI, 0.46-0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66-0.77) for elastic net and 0.69 (95% CI, 0.64-0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91-0.96) and 0.75 (95% CI, 0.70-0.80) for elastic net and random forest, respectively., Conclusions: This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model.

The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning., (© 2023. The Author(s).)

Background: A preoperative surgical strategy before hepatectomy for hepatocellular carcinoma is fundamental to minimize postoperative morbidity and mortality and to reach the best oncologic outcomes. Preoperative 3D reconstruction models may help to better choose the type of procedure to perform and possibly change the initially established plan based on conventional 2D imaging., Methods: A non-randomized multicenter prospective trial with 136 patients presenting with a resectable hepatocellular carcinoma who underwent open or minimally invasive liver resection. Measurement was based on the modification rate analysis between conventional 2D imaging (named "Plan A") and 3D model analysis ("Plan B"), and from Plan B to the final procedure performed (named "Plan C")., Results: The modification rate from Plan B to Plan C (18%) was less frequent than the modification from Plan A to Plan B (35%) (OR = 0.32 [0.15; 0.64]). Concerning secondary objectives, resection margins were underestimated in Plan B as compared to Plan C (-3.10 mm [-5.04; -1.15])., Conclusion: Preoperative 3D imaging is associated with a better prediction of the performed surgical procedure for liver resections in HCC, as compared to classical 2D imaging., (Copyright © 2022. Published by Elsevier Ltd.)

Background: MRI is the reference for the diagnosis of arterial cerebral ischemia, but its role in acute mesenteric ischemia (AMI) is poorly known., Purpose: To assess MRI detection of early ischemic bowel lesions in a porcine model of arterial AMI., Study Type: Prospective/cohort., Animal Model: Porcine model of arterial AMI obtained by embolization of the superior mesenteric artery (seven pigs)., Field Strength/sequence: A 5-T. T1 gradient-echo-weighted-imaging (WI), half-Fourier-acquisition-single-shot-turbo-spin-echo, T2 turbo-spin-echo, true-fast-imaging-with-steady-precession (True-FISP), diffusion-weighted-echo-planar (DWI)., Assessment: T1-WI, T2-WI, and DWI were performed before and continuously after embolization for 6 hours. The signal intensity (SI) of the ischemic bowel was assessed visually and quantitatively on all sequences. The apparent diffusion coefficient (ADC) was assessed., Statistical Tests: Paired Student's t-test or Mann-Whitney U-test, significance at P < 0.05., Results: One pig died from non-AMI-related causes. The remaining pigs underwent a median 5 h53 (range 1 h24-6 h01) of ischemia. Visually, the ischemic bowel showed signal hyperintensity on DWI-b800 after a median 85 (57-276) minutes compared to the nonischemic bowel. DWI-b800 SI significantly increased after 2 hours (+19%) and the ADC significant decrease within the first hour (-31%). The ischemic bowel was hyperintense on precontrast T1-WI after a median 87 (70-171) minutes with no significant quantitative changes over time (P = 0.46-0.93). The ischemic bowel was hyperintense on T2-WI in three pigs with a significant SI increase on True-FISP after 1 and 2 hours., Data Conclusion: Changes in SI and ADC can be seen early after the onset of arterial AMI with DWI. The value of T2-WI appears to be limited., Evidence Level: 1 TECHNICAL EFFICACY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: To prove feasibility of multimodal and temporal fusion of laparoscopic images with preoperative computed tomography scans for a real-time in vivo-targeted lymph node (TLN) detection during minimally invasive pelvic lymphadenectomy and to validate and enable such guidance for safe and accurate sentinel lymph node dissection, including anatomical landmarks in an experimental model., Methods: A measurement campaign determined the most accurate tracking system (UR5-Cobot versus NDI Polaris). The subsequent interventions on two pigs consisted of an identification of artificial TLN and anatomical landmarks without and with augmented reality (AR) assistance. The AR overlay on target structures was quantitatively evaluated. The clinical relevance of our system was assessed via a questionnaire completed by experienced and trainee surgeons., Results: An AR-based robotic assistance system that performed real-time multimodal and temporal fusion of laparoscopic images with preoperative medical images was developed and tested. It enabled the detection of TLN and their surrounding anatomical structures during pelvic lymphadenectomy. Accuracy of the CT overlay was > 90%, with overflow rates < 6%. When comparing AR to direct vision, we found that scores were significatively higher in AR for all target structures. AR aided both experienced surgeons and trainees, whether it was for TLN, ureter, or vessel identification., Conclusion: This computer-assisted system was reliable, safe, and accurate, and the present achievements represent a first step toward a clinical study., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To establish a proof-of-concept study using a phantom model to allow the fusion of preoperative single-photon emission computed tomography (SPECT) combined with computed tomography (CT), also known as SPECT/CT, with intraoperative CT, enabling the application of an augmented reality (AR) surgical guidance system for pelvic sentinel lymph node (SLN) detection in endometrial cancer patients., Methods: A three-dimensional (3D) pelvic phantom model printed in a gelatin-based scaffold including a radiopaque pelvis, a vascular tree mimicking the iliac vessels, two 3D-printed fillable spheres representing the target pelvic sentinel lymph nodes, and a calibration board was developed. A planar with SPECT/CT lymphoscintigraphy and CT were performed independently on the model. We performed all the necessary steps to achieve the fusion between SPECT/CT and CT. Then, we performed a laparoscopy of the pelvic anatomy on the phantom model to assess in real time the overlay of the recording on the anatomical structures and AR guidance system performance., Results: We have successfully completed all the steps needed to fuse the two imaging procedures. This allowed us to apply, in real time, our surgical guidance system with the coverage rate of the visible surface by the augmented reality surface, respectively, on the left SLN 99.48% and on the right SLN 99.42%., Conclusion: Co-registration and real-time fusion between a preoperative SPECT/CT and intraoperative CT are feasible. The metric performance of our guidance system is excellent in relation to possible SPECT/CT and CT fusion. Based on our results, we are able to translate the technology to patients, and we initiated a clinical study to evaluate the accuracy of the AR guidance system for endometrial cancer surgery, with a correlation with indocyanine green (ICG)-based technique, representing the gold standard today in the intraoperative detection of SLN in endometrial cancers, despite various limitations., (© 2022. The Author(s).)

Competing Interests: Competing interests: None declared.

Purpose: The purpose of this study was to identify abdominal computed tomography (CT) features associated with underlying malignancy in patients with mesenteric panniculitis (MP)., Materials and Methods: This single-institution retrospective longitudinal cohort study included patients with MP and a minimum 1-year abdominopelvic CT follow-up or 2-year clinical follow-up after initial abdominopelvic CT examination. Two radiologists, blinded to patients' medical records, conjointly reviewed CT-based features of MP. Electronic medical records were reviewed for newly diagnosed malignancies with the following specific details: type (lymphoproliferative disease or solid malignancy), location (possible mesenteric drainage or distant), stage, time to diagnosis. An expert panel of three radiologists and one hemato-oncologist, who were blinded to the initial CT-based MP features, assessed the probability of association between MP and malignancy based on the malignancy characteristics., Results: From 2006 to 2016, 444 patients with MP were included. There were 272 men and 172 women, with a median age of 64 years (age range: 25-89); the median overall follow-up was 36 months (IQR: 22, 60; range: 12-170). A total of 34 (8%) patients had a diagnosis of a new malignancy; 5 (1%) were considered possibly related to the MP, all being low-grade B-cell non-Hodgkin lymphomas. CT features associated with the presence of an underlying malignancy were the presence of an MP soft-tissue nodule with a short axis >10 mm (P < 0.0001) or lymphadenopathy in another abdominopelvic region (P < 0.0001). Associating these two features resulted in high diagnostic performance (sensitivity 100%; [95% CI: 57-100]; specificity 99% [95% CI: 98-100]). All related malignancies were identified., Conclusion: Further workup to rule out an underlying malignancy is only necessary in the presence of an MP soft-tissue nodule >10 mm or associated abdominopelvic lymphadenopathy., Competing Interests: Conflict of Interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Background: Artificial intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threats, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial Intelligence framework recently promoted by the European Commission., Study Design: A modified Delphi process has been employed to achieve expert consensus., Results: The main ethical issues that arise from applying AI to surgery, described in detail here, relate to human agency, accountability for errors, technical robustness, privacy and data governance, transparency, diversity, non-discrimination, and fairness. It may be possible to address many of these ethical issues by expanding the breadth of surgical AI research to focus on implementation science. The potential for AI to disrupt surgical practice suggests that formal digital health education is becoming increasingly important for surgeons and surgical trainees., Conclusions: A multidisciplinary focus on implementation science and digital health education is desirable to balance opportunities offered by emerging AI technologies and respect for the ethical principles of a patient-centric philosophy., (Copyright © 2022 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Magnetic Resonance Thermometry Imaging (MRTI) holds great potential in laser ablation (LA) monitoring. It provides the real-time multidimensional visualization of the treatment effect inside the body, thus enabling accurate intraoperative prediction of the thermal damage induced. Despite its great potential., thermal maps obtained with MRTI may be affected by numerous artifacts. Among the sources of error producing artifacts in the images., the cavitation phenomena which could occur in the tissue during LA induces dipole-structured artifacts. In this work., an analysis of the cavitation artifacts occurring during LA in a gelatin phantom in terms of symmetry in space and symmetry of temperature values was performed. Results of 2 Wand 4 W laser power were compared finding higher symmetry for the 2 W case in terms of both dimensions of artifact-lobes and difference in temperature values extracted in specular pixels in the image. This preliminary investigation of artifact features may provide a step forward in the identification of the best strategy to correct and avoid artifact occurrence during thermal therapy monitoring. Clinical Relevance- This work presents an analysis of cavitation artifacts in MRTI from LA which must be corrected to avoid error in the prediction of thermal damage during LA monitoring.

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease., (© 2022. Asian Pacific Association for the Study of the Liver.)

Purpose: To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT., Materials and Methods: In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recall-macro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy)., Results: Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI: 0.84, 0.86] vs 0.77 [95% CI: 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI: 0.65, 0.67] vs 0.77 [95% CI: 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) and precision-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) relative to the CNN trained and applied on CT images., Conclusion: The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features. Keywords: Chronic Obstructive Pulmonary Disease, Quantification, Lung, CT Supplemental material is available for this article. See also the invited commentary by Vannier in this issue.© RSNA, 2021., Competing Interests: Disclosures of conflicts of interest: P.S. Patent application (https://patents.google.com/patent/US20190392579A1/) under review, author is coinventor. B.G. Patent application (https://patents.google.com/patent/US20190392579A1/) under review. M.R. Funding for PhD studies from McGill University (School of Computer Science) under supervision of K.S.; Arts and Science postdoctoral fellowship from the University of Toronto; author’s contribution to this work mainly from author’s time as a PhD student at McGill University. S.B. No relevant relationships. A.S. No relevant relationships. K.S. Patent application (https://patents.google.com/patent/US20190392579A1/) under review. R.F. Clinical research scholar (chercheur-boursier clinicien) supported by the Fonds de recherche en sante du Québec (FRQS) and has an operating grant jointly funded by the FRQS and the Fondation de l’Association des radiologistes du Québec (FARQ); payment or honoraria from GE Healthcare (dual-energy CT, AI); method and system of performing medical treatment outcome assessment or medical condition diagnostic, contributor (5%), patent pending US20190392579A1. C.R. Contributor to patent (https://patents.google.com/patent/US20190392579A1/). D.H.E. No relevant relationships. R.J.D. Contributor to patent (https://patents.google.com/patent/US20190392579A1/)., (2022 by the Radiological Society of North America, Inc.)

Background: Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (AI) in radiology., Aims: To investigate the contribution of radiomics on the radiological preoperative assessment of patients with EC; and to establish a simple and reproducible AI Quality Score applicable to Machine Learning and Deep Learning studies., Methods: We conducted a systematic review of current literature including original articles that studied EC through imaging-based AI techniques. Then, we developed a novel Simplified and Reproducible AI Quality score (SRQS) based on 10 items which ranged to 0 to 20 points in total which focused on clinical relevance, data collection, model design and statistical analysis. SRQS cut-off was defined at 10/20., Results: We included 17 articles which studied different radiological parameters such as deep myometrial invasion, lympho-vascular space invasion, lymph nodes involvement, etc. One article was prospective, and the others were retrospective. The predominant technique was magnetic resonance imaging. Two studies developed Deep Learning models, while the others machine learning ones. We evaluated each article with SRQS by 2 independent readers. Finally, we kept only 7 high-quality articles with clinical impact. SRQS was highly reproducible (Kappa = 0.95 IC 95% [0.907-0.988])., Conclusion: There is currently insufficient evidence on the benefit of radiomics in EC. Nevertheless, this field is promising for future clinical practice. Quality should be a priority when developing these new technologies., Competing Interests: Declaration of competing interest None., (Copyright © 2021 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.)

OBJECTIVE. The purpose of this multicenter retrospective study was to assess the MRCP features of Caroli disease (CD). MATERIALS AND METHODS. Sixty-six patients were identified from 2000 to 2019. The inclusion criteria were diagnosis of diffuse or localized CD mentioned in an imaging report, presence of intrahepatic bile duct (IHBD) dilatation, and having undergone an MRCP examination. The exclusion criteria included presence of obstructive proximal biliary stricture and having undergone hepatobiliary surgery other than cholecystectomy. Histopathology records were available for 53 of the 66 (80%) patients. Diffuse and localized diseases were compared by chi-square and t tests and Kaplan-Meier model. RESULTS. Forty-five patients had diffuse bilobar CD ((five pediatric patients [three girls and two boys] with a mean [± SD] age of 8 ± 5 years [range, 1-15 years] and 40 adult patients [26 men and 14 women] with a mean age of 35 ± 11 years [range, 20-62 years]) and 21 patients had localized disease (12 men and 9 women; mean age, 54 ± 14 years). Congenital hepatic fibrosis was found only in patients with diffuse CD (35/45 [78%]), as was a "central dot" sign (15/35 [43%]). IHBD dilatation with both saccular and fusiform features was found in 43 (96%) and the peripheral "funnel-shaped" sign in 41 (91%) of the 45 patients with diffuse CD but in none of the patients with localized disease ( p < .001). Intrahepatic biliary calculi were found in all patients with localized disease but in only 16 of the 45 (36%) patients with diffuse CD ( p < .001). Left liver atrophy was found in 18 of the 21 (86%) patients with localized disease and in none of the patients with diffuse CD ( p < .001). The overall survival rate among patients with diffuse CD was significantly lower than that among patients with localized disease ( p = .03). CONCLUSION. Diffuse IHBD dilatation with both saccular and fusiform features associated with the peripheral funnel-shaped sign can be used for the diagnosis of CD on MRCP. Localized IHBD dilatation seems to be mainly related to primary intrahepatic lithiasis.

We submit a summary of some of the activities of the IHU-Strasbourg during the initial period of the COVID-19 pandemic. These were presented as part of the coronnavation effort coordinated by Dr Adrian Park. Three initiatives are presented as follows: Protect-Est App, healthcare worker stress, and converted diving mask for ventilation. Two of the 3 projects are still ongoing, and one (Predoict-Est) has been adopted nationally.

Background: Colorectal surgery has benefited from advances in precision medicine such as total mesorectal resection, and recently, mesocolon resection, fluorescent perfusion imaging, and fluorescent node mapping. However, these advances fail to address the variable quality of mesocolon dissection and the directed extent of vascular dissection (including high ligation) or pre-resection anastomotic perfusion mapping, thereby impacting anastomotic leaks. We propose a new paradigm of precision image-directed colorectal surgery involving 3D preoperative resection modeling and intraoperative fluoroscopic and fluorescence vascular imaging which better defines optimal dissection planes and vascular vs. anatomy-based resection lines according to our hypothesis., Methods: Six pigs had preoperative CT with vascular 3D reconstruction allowing for the preoperative planning of vascular-based dissection. Laparoscopic surgery was performed in a hybrid operating room (OR). Superselective arterial catheterization was performed in branches of the superior mesenteric artery (SMA) or the inferior mesenteric artery (IMA). Intraoperative boluses of 0.1 mg/kg or a continuous infusion of indocyanine green (ICG) (0.01 mg/mL) were administered to guide fluorescent-based sigmoid and ileocecal resections. Fluorescence was assessed using proprietary software at several regions of interest (ROI) in the right and left colon., Results: The approach was feasible and safe. Selective catheterization took an average of 43 min. Both bolus and continuous perfusion clearly marked pre-identified vessels (arteries/veins) and the target colon segment, facilitating precise resections based on the visible vascular anatomy. Quantitative software analysis indicated the optimal resection margin for each ROI., Conclusion: Intra-arterial fluorescent mapping allows visualization of major vascular structures and segmental colonic perfusion. This may help to prevent any inadvertent injury to major vascular structures and to precisely determine perfusion-based resection planes and margins. This could enable tailoring of the amount of colon resected, ensure good anastomotic perfusion, and improve oncological outcomes.

Purpose: To assess the value of sentinel lymph node (SLN) sampling in high risk endometrial cancer according to the ESMO-ESGO-ESTRO classification., Methods: We performed a comprehensive search on PubMed for clinical trials evaluating SLN sampling in patients with high risk endometrial cancer: stage I endometrioid, grade 3, with at least 50% myometrial invasion, regardless of lymphovascular space invasion status; or stage II; or node-negative stage III endometrioid, no residual disease; or non-endometrioid (serous or clear cell or undifferentiated carcinoma, or carcinosarcoma). All patients underwent SLN sampling followed by pelvic with or without para-aortic lymphadenectomy., Results: We included 17 original studies concerning 1322 women. Mean detection rates were 89% for unilateral and 68% for bilateral. Pooled sensitivity was 88.5% (95%CI: 81.2-93.2%), negative predictive value was 96.0% (95%CI: 93.1-97.7%), and false negative rate was 11.5% (95%CI: 6.8; 18.8%). We noted heterogeneity in SLN techniques between studies, concerning the tracer and its detection, the injection site, the number of injections, and the surgical approach. Finally, we found a correlation between the number of patients included and the SLN sampling performances., Discussion: This meta-analysis estimated the SLN sampling performances in high risk endometrial cancer patients. Data from the literature show the feasibility, the safety, the limits, and the impact on surgical de-escalation of this technique. In conclusion, our study supports the hypothesis that SLN sampling could be a valuable technique to diagnose lymph node involvement for patients with high risk endometrial cancer in replacement of conventional lymphadenectomy. Consequently, randomized clinical trials are necessary to confirm this hypothesis.

Objective: To develop consensus definitions of image-guided surgery, computer-assisted surgery, hybrid operating room, and surgical navigation systems., Summary Background Data: The use of minimally invasive procedures has increased tremendously over the past 2 decades, but terminology related to image-guided minimally invasive procedures has not been standardized, which is a barrier to clear communication., Methods: Experts in image-guided techniques and specialized engineers were invited to engage in a systematic process to develop consensus definitions of the key terms listed above. The process was designed following review of common consensus-development methodologies and included participation in 4 online surveys and a post-surveys face-to-face panel meeting held in Strasbourg, France., Results: The experts settled on the terms computer-assisted surgery and intervention, image-guided surgery and intervention, hybrid operating room, and guidance systems and agreed-upon definitions of these terms, with rates of consensus of more than 80% for each term. The methodology used proved to be a compelling strategy to overcome the current difficulties related to data growth rates and technological convergence in this field., Conclusions: Our multidisciplinary collaborative approach resulted in consensus definitions that may improve communication, knowledge transfer, collaboration, and research in the rapidly changing field of image-guided minimally invasive techniques., Competing Interests: Disclosure: M.G.: advisor Siemens, Medtronic; B.G.: CEO of IHU; G.C.: Advisory board Olympus, Cook Medical, Grant Research Boston Scientific; L.S.: employee of IHU, being Siemens one of its partners; I.B.: Apollo Endosurgery Research Grant holder and consultant for Boston Scientific, Apollo Endosurgery and Cook Medical; C.R.: Cooperation and invited talks sponsored by Siemens; B.O.: Research Grant: Siemens Healthineers and NIH R01CA235564; R.B.: advisor Cascination, non-financial support from iSys Medizintechnik during the conduct of the study; L.S.o.: is president of Visible Patient; A.H.: IRCAD employee; A.A.: Siemens Healthineers Advanced Therapies employee; J.M.: President of IRCAD and IHU, which are partly funded by Karl Storz, Medtronic, and Siemens Healthcare. During the face-to-face panel meeting, Siemens provided unrestricted academic sponsoring and funded it partially., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc.)

Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The current unprecedented coronavirus 2019 (COVID-19) crisis has accelerated and enhanced e-learning solutions. During the so-called transition phase, efforts were made to reorganize surgical services, reschedule elective surgical procedures, surgical research, academic education, and careers to optimize results. The intention to switch to e-learning medical education is not a new concern. However, the current crisis triggered an alarm to accelerate the transition. Efforts to consider e-learning as a teaching and training method for medical education have proven to be efficient. For image-guided therapies, the challenge requires more effort since surgical skills training is combined with image interpretation training, thus the challenge is to cover quality educational content with a balanced combination of blended courses (online/onsite). Several e-resources are currently available in the surgical scenario; however, further efforts to enhance the current system are required by accelerating the creation of new learning solutions to optimize complex surgical education needs in the current disrupted environment.

Purpose: The objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders., Methods: This study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial., Results: In the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10 -7 ), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41)., Conclusion: A radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies., Trial Registration: Clinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717., Competing Interests: Competing interests: VB reports consultingor/and advisory boards for Merck Serono, Ipsen, Prestizia, Eisai, and Bayer. DrBoige has received honoraria for Merck Serono, Sanofi, Amgen, MSD and Bayer. CL reports personal grants for boardmembership for AAA, grants from Novartis and travel grants from Ipsen Pharma,Amgen and Bayer. OB reports personalgrants for consultancy for A mgen, Merck, Roche, Bayer; payment forlectures including service onspeakers bureau for Servier, Pierre Fabre ; and travel grants from Roche,Lilly. CB reports consultingfor BMS ; payment for lectures for Merck Serono et BMS ; Paymentsfor development of educational presentations for Aztra-Zenecaand BMS; travel grants from Merck Serono and Aztra-Zeneca; Advisory Boardmembership for BMS and Aztra-Zeneca. EF reports personal grants for boardmembership for Roche, Merck and Sanofi. JB reports personal grants for boardmembership for Roche, Boehringer-Ingelheim, Astra-Zeneca, Servier and BMS;payment for lectures including service on speaker bureau for Roche,Astra-Zeneca and travel grants from Roche and BMS. JT reports personal grantsfor consultancy for Abbvie, Amgen, Baxalta, Celgene, Lilly, Merck, Roche,Servier, MSD, Pierre-Fabre and Sanofi; payment for lectures including serviceon speakers bureau for ABBVIE, Amgen, Celgene, Lilly, Merck, Roche; paymentsfor development of educational presentations for Roche and travel grants fromRoche and Merck. J-MP reports personal grants for board membership for Roche,Sanofi, Lilly Merck and Amgen; consultancy for Lilly and Roche; payments fordevelopment of educational presentations for Roche, Sanofi, Lilly Merck andAmgen; grants for his institution for board membership for Roche and Merck ;payments of grants form Merck and Roche. J-FS reports personal grants forconsultancy for Celgene, Lilly, Merck, Novartis Oncology, Pfizer, Sanofi,Roche; grants from Roche; payments for development of educational presentationsfor Amgen and Lilly; travel grants from Ipsen Pharma and Merck. F-KAreports consultancy for Sanofi as Board member and travelgrants from Roche, Bayer and Ipsen. M-BA reports consulting and travel supportfrom Amgen, Bayer, Merck, Roche, Sanofi, and Ipsen. T A reports personal grants Consultancy for Pierre Fabre; Grantsfrom Roche and Amgen; Payments for development of educational presentations forNovartis Oncology, Pfizer, Sanofi, Roche; Travel grants from Ipsen Pharma,Novartis Oncology, Sanofi, Roche., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)