PURPOSE:Pancreatic ductal adenocarcinoma (PDA) is a leading cause of cancer-related deaths, with early diagnosis hampered by nonspecific symptoms and limitations of existing imaging techniques. This study aimed to develop a deep learning (DL) algorithm to automatically classify pancreas lesions on contrast-enhanced CT scans as normal, benign, or malignant, to assist radiologists in detecting early-stage pancreatic cancer. MATERIALS AND METHODS:A dataset of 1,037 portal-phase CT scans was compiled from 18 institutions. The dataset was divided into a training set (N = 732) and a test set (N = 305), which was further divided into an internal validation test set (N = 139) and an external validation test set (N = 166). After segmentation using the TotalSegmentator algorithm, the pancreas was isolated from each CT scan. A DL model combining TotalSegmentator's pre-trained encoder and nnUNet decoder layers was developed to classify pancreas lesions. Ten-fold cross-validation was applied, and model performance was assessed using precision, recall, area under the curve (AUC) and a final score (FS) representing a weighted average of the previous three metrics. The final prediction combined the outputs of ten models. RESULTS:Across all validation datasets (139 and 166 patients, respectively, in the internal and external dataset), precision and recall were 0.57 and 0.63, respectively, while AUC was 0.84. In the external validation dataset, malignant lesions were detected with an AUC of 0.97. The model achieved an FS of 0.72 in both internal and external validation datasets, indicating consistent performance across datasets. CONCLUSION:This study demonstrated the feasibility of using a DL algorithm for automated pancreas lesion classification in CT scans. The model showed strong performance, particularly in detecting malignant lesions. Further research is needed to assess the model's clinical applicability and performance in real-world settings.

Bevacizumab, an angiogenesis inhibitor, is commonly used alongside chemotherapy for metastatic colorectal cancer (mCCR). While inducing necrosis in tumours, bevacizumab may also lead to atrophy in tumour-free organs. Artificial intelligence (AI) models offer user-friendly methods for measuring organ volumes. This study explores the relationship between bevacizumab-induced atrophy using AI-assisted volume measurement in tumour-free organs and treatment efficacy. This multicenter retrospective study includes patients from the PRODIGE 9 and PRODIGE 20 trials. Organ atrophy was assessed by evaluating volume changes from diagnosis to two months after treatment initiation in patients receiving bevacizumab compared to those who did not. Statistical analyses were performed using the Wilcoxon test, with correlations between volumetric changes. Overall and progression-free survival were assessed using log-rank tests and Cox regression models. Among the 214 patients included, 192 received bevacizumab. Both liver and spleen volumes were measured using a deep learning-based AI model and manual measurements. AI-generated volume measurements showed a strong correlation with manual measurements (Pearson coefficient > 0.8). Bevacizumab-treated patients exhibited significant atrophy of non-tumoural liver volume (p = 0.0378), while no significant changes were observed in tumour or spleen volumes in either group. Survival analyses revealed that patients with a smaller decrease in non-tumoural liver volume had improved overall survival (p = 0.016), although this association became non-significant after adjusting for age, sex, and tumour volume at diagnosis (p = 0.25). Our findings support the feasibility and reliability of AI in organ volume measurement. While bevacizumab exposure was linked to non-tumoural liver atrophy, its impact on survival remains inconclusive after adjustment. These results pave the way for further research into bevacizumab-induced organ atrophy and the potential of AI in personalizing oncology treatments.

Introduction La drépanocytose est une maladie génétique à transmission autosomique récessive, très fréquente dans le monde, notamment chez les Africains. Elle touche l’enfant et l’adulte. Ses complications sont liées à l’anémie, aux phénomènes vaso-occlusifs, à l’hémolyse et aux infections par l’asplénie qu’elle engendre. Données récentes La surcharge en fer liée aux transfusions sanguines ainsi que l’hématopoïèse extramédullaire sont des manifestations qui peuvent se retrouver dans plusieurs organes, sans spécificité particulière. L’ischémie, les infarctus et les infections sont également ubiquitaires, mais peuvent comporter des spécificités en fonction de l’organe atteint. Cet article décrit et illustre la traduction, essentiellement en TDM et en IRM, des différentes complications de cette pathologie, qu’elles soient cérébrales, thoraciques, abdominales, pelviennes ou ostéoarticulaires. Conclusion Les manifestations de la drépanocytose sont variées, et le radiologue doit en avoir une bonne connaissance de manière à poser un diagnostic adapté et précoce.

Purpose: Adrenocortical carcinoma (ACC) is a rare condition with a poor and hardly predictable prognosis. This study aims to build and evaluate a preoperative computed tomography (CT)-based score (CT score) using features previously reported as biomarkers in ACC to predict overall survival (OS) in patients with ACC. Methods: A CT score based on preoperative CT examinations combining shape elongation, maximum tumour diameter, and the European Network for the Study of Adrenal Tumors (ENSAT) stage was built using a logistic regression model to predict OS duration in a development cohort of 89 patients with ACC. An optimal cut-off of the CT score was defined and the Kaplan-Meier method was used to assess OS. The CT score was then tested in an external validation cohort of 54 patients wit ACC. The C-index of the CT score for predicting OS was compared to that of ENSAT stage alone. Results: The CT score helped discriminate between patients with poor prognosis and patients with good prognosis in both the validation cohort (54 patients; mean OS, 69.4 months; 95% confidence interval [CI]: 57.4-81.4 months vs mean OS, 75.6 months; 95% CI: 62.9-88.4 months, respectively; P = .022). In the validation cohort the C-index of the CT score was significantly better than that of the ENSAT stage alone (0.62 vs 0.35; P = .002). Conclusion: A CT score combining morphological criteria, radiomics, and ENSAT stage on preoperative CT examinations allows a better prognostic stratification of patients with ACC compared to ENSAT stage alone.

BACKGROUND & AIMS:Changes in stomach size may impact eating behaviour. A recent study showed gastric dilatation in restrictive eating disorders using computed tomography scans. This study aimed to describe stomach size in the standing position in women with anorexia nervosa (AN). METHODS:Women treated for AN at our institution were retrospectively included if they had undergone upper gastrointestinal radiography (UGR) after the diagnosis of AN. Two control groups (CG1 and CG2) were included, both comprising female patients: CG1 patients were not obese and underwent UGR for digestive symptoms of other aetiologies, and CG2 comprised obese individuals who had UGR before bariatric surgery. A UGR-based Stomach Size Index (SSI), calculated as the ratio of the length of the stomach to the distance between the upper end of the stomach and the top of the iliac crests, was measured in all three groups. Gastromegaly was defined as SSI >1.00. RESULTS:45 patients suffering from AN (28 with restrictive and 17 with binge/purge subtype), 10 CG1 and 20 CG2 subjects were included in this study. Stomach Size Index was significantly higher in AN (1.27 ± 0.24) than in CG1 (0.80 ± 0.11) and CG2 (0.68 ± 0.09); p < 0.001, but was not significantly different between patients with the restrictive and binge/purge subtypes. Gastromegaly was present in 82.2% of patients with AN and not present in the control groups. In patients with AN, gastromegaly was present in 12/15 patients without digestive symptoms (80.0%) and in 25/30 patients with digestive complaints (83.3%) at time of UGR (p = 0.99). In the AN group, no significant relationship was found between SSI and body mass index. CONCLUSION:Gastromegaly is frequent in AN and could influence AN recovery. This anatomical modification could partially explain the alterations of gastric motility previously reported in AN.

Purpose The purpose of the 2023 SFR data challenge was to invite researchers to develop artificial intelligence (AI) models to identify the presence of a pancreatic mass and distinguish between benign and malignant pancreatic masses on abdominal computed tomography (CT) examinations. Materials and methods Anonymized abdominal CT examinations acquired during the portal venous phase were collected from 18 French centers. Abdominal CT examinations were divided into three groups including CT examinations with no lesion, CT examinations with benign pancreatic mass, or CT examinations with malignant pancreatic mass. Each team included at least one radiologist, one data scientist, and one engineer. Pancreatic lesions were annotated by expert radiologists. CT examinations were distributed in balanced batches via a Health Data Hosting certified platform. Data were distributed into four batches, two for training, one for internal evaluation, and one for the external evaluation. Training used 83 % of the data from 14 centers and external evaluation used data from the other four centers. The metric (i.e., final score) used to rank the participants was a weighted average of mean sensitivity, mean precision and mean area under the curve. Results A total of 1037 abdominal CT examinations were divided into two training sets (including 500 and 232 CT examinations), an internal evaluation set (including 139 CT examinations), and an external evaluation set (including 166 CT examinations). The training sets were distributed on September 7 and October 13, 2023, and evaluation sets on October 15, 2023. Ten teams with a total of 93 members participated to the data challenge, with the best final score being 0.72. Conclusion This SFR 2023 data challenge based on multicenter CT data suggests that the use of AI for pancreatic lesions detection is possible on real data, but the distinction between benign and malignant pancreatic lesions remains challenging.

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

Purpose: The purpose of this study was to evaluate the capabilities of multiparametric magnetic resonance imaging (MRI) in differentiating between lipid-poor adrenal adenoma (LPAA) and adrenocortical carcinoma (ACC). Materials and methods: Patients of two centers who underwent surgical resection of LPAA or ACC after multiparametric MRI were retrospectively included. A training cohort was used to build a diagnostic algorithm obtained through recursive partitioning based on multiparametric MRI variables, including apparent diffusion coefficient and chemical shift signal ratio (i.e., tumor signal intensity index). The diagnostic performances of the multiparametric MRI-based algorithm were evaluated using a validation cohort, alone first and then in association with adrenal tumor size using a cut-off of 4 cm. Performances of the diagnostic algorithm for the diagnosis of ACC vs. LPAA were calculated using pathology as the reference standard. Results: Fifty-four patients (27 with LPAA and 27 with ACC; 37 women; mean age, 48.5 13.3 [standard deviation (SD)] years) were used as the training cohort and 61 patients (24 with LPAA and 37 with ACC; 47 women; mean age, 49 11.7 [SD] years) were used as the validation cohort. In the validation cohort, the diagnostic algorithm yielded best accuracy for the diagnosis of ACC vs. LPAA (75%; 46/61; 95% CI: 55-88) when used without lesion size. Best sensitivity was obtained with the association of the diagnostic algorithm with tumor size (96%; 23/24; 95% CI: 80-99). Best specificity was obtained with the diagnostic algorithm used alone (76%; 28/37; 95% CI: 60-87). Conclusion: A multiparametric MRI-based diagnostic algorithm that includes apparent diffusion coefficient and tumor signal intensity index helps discriminate between ACC and LPAA with high degrees of specificity and accuracy. The association of the multiparametric MRI-based diagnostic algorithm with adrenal lesion size helps maximize the sensitivity of multiparametric MRI for the diagnosis of ACC. (c) 2024 Published by Elsevier Masson SAS on behalf of Soci & eacute;t & eacute; fran & ccedil;aise de radiologie.

Background This article summarizes the French intergroup guidelines regarding rectal adenocarcinoma (RA) management published in September 2023, available on the French Society of Gastroenterology website. Methods This work was supervised by French medical and surgical societies involved in RA management. Recommendations were rated from A to C according to the literature until September 2023. Results Based on the pretreatment work-up, RA treatment was divided into four groups. T1N0 can be treated by endoscopic or surgical excision alone if there is no risk factor for lymph node involvement. For T2N0, radical surgery with total mesorectal excision is recommended, but rectal conservation is possible for small tumors (<4cm) after complete/subcomplete response following chemoradiotherapy. For T12N+ or T3+any N, total neoadjuvant treatment (TNT) followed by radical surgery is the gold standard, but rectal conservation is possible for small tumors after complete/subcomplete response following TNT. T3N2 or T+any N are an indication for TNT followed by radical surgery. Immunotherapy shows promise for dMMR/MSI RA. For metastatic tumors, recommendations are based on less robust evidence and chemotherapy plays a major role. Conclusion These guidelines aim at providing a personalized therapeutic strategy and are constantly being optimized. Each case should be discussed by a multidisciplinary team.

Abstract Background: Adrenocortical carcinoma (ACC) is a rare condition with a poor and hardly predictable prognosis. This study aims to build and evaluate a preoperative computed tomography (CT)-based radiomic score (Radscore) using features previously reported as biomarkers in adrenocortical carcinoma (ACC) to predict overall survival (OS) in patients with ACC. Methods: In this retrospective study, a Radscore based on preoperative CT examinations combining shape elongation, tumor maximal diameter, and the European Network for the Study of Adrenal Tumors (ENSAT) stage and was built using a logistic regression model to predict OS duration in a development cohort. An optimal cut-off of the Radscore was defined and the Kaplan-Meier method was used to assess OS. The Radscore was then tested in an external validation cohort. The C-index of the Radscore for the prediction of OS was compared to that of ENSAT stage alone. Findings: The Radscore was able to discriminate between patients with poor prognosis and patients with good prognosis in both the the validation cohort (54 patients; mean OS, 69·4 months; 95% CI: 57·4–81·4 months vs. mean OS, 75·6 months; 95% CI: 62·9–88·4 months, respectively; P = 0·022). In the validation cohort the C-index of the Radscore was significantly better than that of the ENSAT stage alone (0.62 vs. 0.35; P = 0·002). Conclusion: A Radscore combining morphological criteria, radiomics, and ENSAT stage on preoperative CT examinations allow a stratification of prognosis in patients with ACC compared with ENSAT stage alone.

L’entérographie par résonance magnétique (entéro-IRM) a pour indication principale la maladie de Crohn, bien qu’elle trouve également sa place dans l’exploration et/ou la détection d’autres pathologies de l’intestin grêle, en alternative à la tomodensitométrie. Cet examen est pratiqué en routine depuis environ 20 ans. Ce travail présente comment réaliser une entéro-IRM selon les recommandations internationales publiées sur le sujet. L’ingestion de 1 à 1,5 L d’un produit hyperosmolaire permet d’obtenir une bonne distension du grêle. Pour limiter le mouvement péristaltique, un antispasmodique doit être utilisé. Le temps d’acquisition des images doit être adapté aux possibilités d’apnée du patient. Les séquences minimales comprennent, dans les plans axial et coronal, des séquences à l’état d’équilibre sans saturation de la graisse, des séquences rapides T2 spin écho, et des séquences 3D T1 en écho de gradient après injection de gadolinium et suppression de la graisse. Le compte rendu doit évaluer la qualité de l’examen et l’ensemble des éléments séméiologiques dont la terminologie a été standardisée. Une entéro-IRM inclut un examen techniquement optimisé et un compte rendu standardisé. Cet article détaille les éléments permettant d’aboutir à ces deux objectifs. Magnetic resonance enterography (MR-enterography) is mostly performed in patients with Crohn's disease, although it may also play a role as an alternative to computed tomography in the evaluation and/or the detection of other small bowel conditions. It has routinely been performed for approximately 20 years so far. The aim of this study is to explain how to perform an MR-enterography according to international recommendations. Ingestion of 1 to 1.5 litre of hyperosmolar fluid provides good distention of the small bowel. To limit peristalsis, an antispasmodic agent must be used. Acquisition time must be adapted to the patient's apnea capacities. Minimal recommended sequences include, in both the axial and coronal planes, fast spin echo T2 weighted, steady state free precession gradient echo weighted sequences and 3D contrast-enhanced fat-suppressed T1-weighted sequences. The report must assess the quality of the imaging and the semiological terms which have been standardized. An MR-enterography includes a technically optimized imaging and a standardized report. This study details the elements enabling these two goals to be achieved.

L’éventration est une complication fréquente de la chirurgie abdominale. Elle entraîne une morbidité importante, altère la qualité de vie tant sur le plan fonctionnel qu’esthétique, avec une symptomatologie allant de la simple pesanteur à des complications plus graves liées à l’étranglement ou aux adhérences au sein du sac. Les progrès thérapeutiques des dernières décennies, notamment l’utilisation de prothèses biologiques ou biosynthétiques et l’émergence des techniques chirurgicales de séparation des composantes, ont fait de la chirurgie pariétale une spécialisation à part entière. Les éventrations complexes représentent un véritable enjeu chirurgical avec la nécessité de définir la meilleure stratégie pour éviter la récidive et toutes autres complications aiguës et chroniques. La tomodensitométrie (TDM) est la technique de référence pour le bilan préopératoire. Les études menées par les chirurgiens ont relevé les insuffisances des comptes rendus radiologiques, en routine mais parfois aussi par des radiologues spécialisés en imagerie digestive, ne leur permettant pas une bonne appréhension du geste à réaliser. Cette revue didactique a pour objectifs d’expliquer les techniques chirurgicales, de détailler la radioanatomie de la paroi, de proposer un protocole en TDM et un compte rendu structuré intégrant les éléments clés utiles aux chirurgiens. Incisional hernia is a frequent complication of abdominal surgery. It causes significant morbidity and impairs the quality of life, both functionally and aesthetically, with symptoms ranging from abdominal heaviness to more serious complications related to strangulation. The therapeutic progress of the last decades, particularly the use of long-term absorbable meshes and the emergence of surgical techniques of component separation, have made parietal surgery a highly specialized surgical discipline. Complex incisional hernias represent a real surgical challenge with the need to define the best strategy to avoid recurrence and acute and long-term complications. The CT scan is the reference technique for the preoperative assessment. Studies carried out by surgeons have revealed the inadequacy of radiological reports, routinely but also sometimes by radiologists specialized in digestive imaging, which do not allow them to anticipate perfectly the procedure to be performed. This didactic review aims to explain the surgical techniques, to detail the radio-anatomy of the abdominal wall, to propose a CT protocol, and a structured report with useful key elements for surgeons.

Purpose: To assess interobserver variability and accuracy of preoperative computed tomography (CT) and magnetic resonance imaging (MRI) in pancreatic ductal adenocarcinoma (PDAC) size estimation using surgical specimens as standard of reference. Methods: Patients with PDAC who underwent preoperative CT and MRI examinations before surgery were included. PDAC largest axial dimension was measured by 2 readers on 8 MRI sequence and 2 CT imaging phases (pancreatic parenchymal and portal venous). Measurements were compared to actual tumour size at pathologic examination. Interobserver variability was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plots. Differences in tumour size (?diameter) between imaging and actual tumour size were searched using Wilcoxon rank sum test. Results: Twenty-nine patients (16 men; median age, 70 years) with surgically resected PDAC were included. Interobserver reproducibility was good to excellent for all MRI sequences and the 2 CT imaging phases with ICCs between .862 (95%CI: .692-.942) for fat-saturated in phase T1-weighted sequence and .955 (95%CI: .898-.980) for portal venous phase CT images. Best accuracy in PDAC size measurement was obtained with pancreatic parenchymal phase CT images with median ?diameters of -2 mm for both readers, mean relative differences of -9% and -6% and no significant differences with dimensions at histopathological analysis (P = .051). All MRI sequences led to significant underestimation of PDAC size (median ?diameters, -6 to -1 mm; mean relative differences, -21% to -11%). Conclusions: Most accurate measurement of PDAC size is obtained with CT images obtained during the pancreatic parenchymal phase. MRI results in significant underestimation of PDAC size.

Background: T-cell lymphomas constitute a heterogeneous group of hematological malignancies with global poor outcomes. Computed tomography (CT)-based texture analysis (CTTA) is a promising technique to predict the survival of these patients. Objectives: The present study aimed to investigate whether CTTA features on the pretreatment unenhanced CT scans of 18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) examination can predict the survival of patients with T-cell lymphomas. Patients and Methods: In this retrospective cohort study, patients with T-cell lymphomas, undergoing pretreatment 18F-FDG PET/CT scan during 2008 - 2019, were included, and their clinical and biological characteristics were collected. The mean gray value, entropy, kurtosis, skewness, and standard deviation were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales in up to five lesions per patient, indicating a high focal uptake on 18F-FDG PET scan. A Lasso-penalized Cox regression analysis was performed to identify independent predictors of overall survival (OS), progression-free survival at 24 months (PFS24), and PFS. Results: A total of 23 patients (7 females and 16 males; median age, 69 years; age range, 33 - 86 years) were included in this study. The CTTA was performed for 60 lesions. The median OS and PFS were 391 days (range, 10 - 3,463 days) and 268 days (range, 10 - 2,321 days), respectively. No CT texture parameter was associated with PFS or PFS24. The standard deviation at a coarse filter scale was independently associated with a poor OS (hazard ratio [HR] = 1.009, confidence interval [CI]: 1.0012 - 1.016, P = 0.02). A value above 143 was associated with a poor prognosis with a specificity of 81%. Conclusion: The pretreatment CTTA-derived tumor standard deviation at a coarse filter scale may be a predictive biomarker of OS in patients with T-cell lymphomas.