Multicenter External Validation of the Deep Pelvic Endometriosis Index Magnetic Resonance Imaging Score.

Importance: Preoperative mapping of deep pelvic endometriosis (DPE) is crucial as surgery can be complex and the quality of preoperative information is key. Objective: To evaluate the Deep Pelvic Endometriosis Index (dPEI) magnetic resonance imaging (MRI) score in a multicenter cohort. Design, Setting, and Participants: In this cohort study, the surgical databases of 7 French referral centers were retrospectively queried for women who underwent surgery and preoperative MRI for DPE between January 1, 2019, and December 31, 2020. Data were analyzed in October 2022. Intervention: Magnetic resonance imaging scans were reviewed using a dedicated lexicon and classified according to the dPEI score. Main outcomes and measures: Operating time, hospital stay, Clavien-Dindo-graded postoperative complications, and presence of de novo voiding dysfunction. Results: The final cohort consisted of 605 women (mean age, 33.3; 95% CI, 32.7-33.8 years). A mild dPEI score was reported in 61.2% (370) of the women, moderate in 25.8% (156), and severe in 13.1% (79). Central endometriosis was described in 93.2% (564) of the women and lateral endometriosis in 31.2% (189). Lateral endometriosis was more frequent in severe (98.7%) vs moderate (48.7%) disease and in moderate vs mild (6.7%) disease according to the dPEI (P < .001). Median operating time (211 minutes) and hospital stay (6 days) were longer in severe DPE than in moderate DPE (operating time, 150 minutes; hospital stay 4 days; P < .001), and in moderate than in mild DPE (operating time; 110 minutes; hospital stay, 3 days; P < .001). Patients with severe disease were 3.6 times more likely to experience severe complications than patients with mild or moderate disease (odds ratio [OR], 3.6; 95% CI, 1.4-8.9; P = .004). They were also more likely to experience postoperative voiding dysfunction (OR, 3.5; 95% CI, 1.6-7.6; P = .001). Interobserver agreement between senior and junior readers was good (κ = 0.76; 95% CI, 0.65-0.86). Conclusions and Relevance: The findings of this study suggest the ability of the dPEI to predict operating time, hospital stay, postoperative complications, and de novo postoperative voiding dysfunction in a multicenter cohort. The dPEI may help clinicians to better anticipate the extent of DPE and improve clinical management and patient counseling.

Deep pelvic infiltrating endometriosis: MRI consensus lexicon and compartment-based approach from the ENDOVALIRM group.

PURPOSE: The purpose of this consensus article was to develop guidelines by a focused panel of experts to elaborate a lexicon of image interpretation, and a standardized region-based reporting of deep infiltrating endometriosis (DIE) with magnetic resonance imaging (MRI). MATERIALS AND METHODS: Evidence-based data and expert opinion were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts scoring of pelvic compartment delineation and reporting template were collected; responses were analyzed and classified as "RECOMMENDED" versus "NOT RECOMMENDED" (when ≥ 80% consensus among experts) or uncertain (when < 80% consensus among experts). RESULTS: Consensus regarding pelvic compartment delineation and DIE reporting was attained using the RAND-UCLA Appropriateness Method. The pelvis was divided in nine compartments and extrapelvic lesions were assigned to an additional (tenth) compartment. A consensus was also reached for each structure attributed to a compartment and each reporting template item among the experts. No consensus was reached for a normal aspect of uterosacral ligament, but a consensus was reached for an unequivocal involvement leading to a positive diagnosis and an equivocal involvement leading to uncertain diagnosis. Tailored MRI lexicon and standardized region-based report were proposed. CONCLUSION: These consensus recommendations should be used as a guide for DIE reporting and staging with MRI. Standardized MRI compartment-based structured reporting is recommended to enable consistent accuracy and help select the best therapeutic approach.

Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation.

PURPOSE: The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4. MATERIALS AND METHODS: An ensemble of mask region-based convolutional neural networks (Mask-RCNN) combining nodule segmentation and classification were trained to explicitly localize the nodule and generate a probability of the nodule to be malignant on two-dimensional B-mode ultrasound. These probabilities were aggregated at test time to produce final results. Resulting inferences were assessed using area under the curve (AUC). RESULTS: A total of 460 ultrasound images of breast nodules classified as BI-RADS 3 or 4 were included. There were 295 benign and 165 malignant breast nodules used for training and validation, and another 137 breast nodules images used for testing. As a part of the challenge, the distribution of benign and malignant breast nodules in the test database remained unknown. The obtained AUC was 0.69 (95% CI: 0.57-0.82) on the training set and 0.67 on the test set. CONCLUSION: The proposed deep learning solution helps classify benign and malignant breast nodules based solely on two-dimensional ultrasound images initially marked as BIRADS 3 and 4.

Three artificial intelligence data challenges based on CT and ultrasound.

PURPOSE: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers. MATERIALS AND METHODS: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams. The objectives were proposed by the different organizations depending on their core areas of expertise. A dedicated platform was used to upload the medical image data, to automatically anonymize the uploaded data. RESULTS: Three challenges were proposed including classification of benign or malignant breast nodules on ultrasound examinations, detection and contouring of pathological neck lymph nodes from cervical CT examinations and classification of calcium score on coronary calcifications from thoracic CT examinations. A total of 2076 medical examinations were included in the database for the three challenges, in three months, by 18 different centers, of which 12% were excluded. The 39 participants were divided into six multidisciplinary teams among which the coronary calcification score challenge was solved with a concordance index > 95%, and the other two with scores of 67% (breast nodule classification) and 63% (neck lymph node calcifications).

Does Computed Tomography-Based Virtual Colonoscopy Improve the Accuracy of Preoperative Assessment Based on Magnetic Resonance Imaging in Women Managed for Colorectal Endometriosis?

STUDY OBJECTIVE: To evaluate whether combining computed tomography-based virtual colonoscopy (CTC) with magnetic resonance imaging (MRI) improves preoperative assessment of colorectal endometriosis. DESIGN: Retrospective study using prospectively recorded data (Canadian Task Force classification II-2). SETTING: University tertiary referral center. PATIENTS: Seventy-one women treated for colorectal endometriosis managed between June 2015 and May 2016. INTERVENTIONS: Patients included in our study underwent colorectal surgery for deep endometriosis infiltrating the rectum or the sigmoid colon and had preoperative assessment using MRI and CTC. To establish the correlation between preoperative and intraoperative findings, the concordance kappa index was used. MEASUREMENTS AND MAIN RESULTS: Preoperative data provided by MRI, CTC, and a combination of both were compared with intraoperative findings. All 71 patients had a total of 105 endometriotic intestinal lesions intraoperatively confirmed. Some 71.2% of rectal nodules and 60.0% of sigmoid nodules infiltrated the muscularis propria of the intestinal wall, with most infiltrating between 25% and 50% of the rectal circumference; 73% of rectal nodules and 96% of sigmoid nodules led to varying degrees of stenosis. The concordance between intraoperative and preoperative findings concerning the presence of rectal nodules was high, at .88 when associating CTC with MRI, whereas each imaging technique taken individually provided lower concordance coefficients. In our study 80.3% of patients underwent the procedure that had been preoperatively planned. CONCLUSION: Our study suggests that associating MRI with CTC leads to improved accuracy in preoperative assessment of colorectal endometriosis and in subsequent preoperative choice of surgical procedures on the digestive tract.