Pamela Ines Causa Andrieu

Primary amenorrhea reflects disruption along the hypothalamic-pituitary-ovarian-uterine axis; may arise from anatomic, genetic, or functional abnormalities; and may be diagnosed on the basis of US and MRI findings.

A comprehensive approach that combines imaging, histopathologic analysis, and patient counseling can be used to develop personalized fertility-sparing and fertility-preserving protocols that are safe and effective and respect patient autonomy in making decisions related to their future fertility.

The interpretation of gynecologic pelvic MRI benefits from tailored MRI protocols, radiologists' knowledge of normal female pelvic anatomy, and a step-by-step approach focusing on lesion origin, lesion tissue composition, and solid tissue morphology, with findings presented in a structured report.

Background and purpose Endometriosis affects 5–10% of women of reproductive age. Despite its prevalence, diagnosing endometriosis through imaging remains challenging. Advances in deep learning (DL) are revolutionizing the diagnosis and management of complex medical conditions. This study aims to evaluate DL tools in enhancing the accuracy of multi-sequence MRI-based detection of endometriosis. Method We gathered a patient cohort from our institutional database, composed of patients with pathologically confirmed endometriosis from 2015 to 2024. We created an age-matched control group that underwent a similar MR protocol without an endometriosis diagnosis. We used sagittal fat-saturated T1-weighted (T1W FS) pre- and post-contrast and T2-weighted (T2W) MRIs. Our dataset was split at the patient level, allocating 12.5% for testing and conducting seven-fold cross-validation on the remainder. Seven abdominal radiologists with experience in endometriosis MRI and complex surgical planning and one women’s imaging fellow with specific training in endometriosis MRI reviewed a random selection of images and documented their endometriosis detection. Results 395 and 356 patients were included in the case and control groups respectively. The final 3D-DenseNet-121 classifier model demonstrated robust performance. Our findings indicated the most accurate predictions were obtained using T2W, T1W FS pre-, and post-contrast images. Using an ensemble technique on the test set resulted in an F1 Score of 0.881, AUROCC of 0.911, sensitivity of 0.976, and specificity of 0.720. Radiologists achieved 84.48% and 87.93% sensitivity without and with AI assistance in detecting endometriosis. The agreement among radiologists in predicting labels for endometriosis was measured as a Fleiss’ kappa of 0.5718 without AI assistance and 0.6839 with AI assistance. Conclusion This study introduced the first DL model to use multi-sequence MRI on a large cohort, showing results equivalent to human detection by trained readers in identifying endometriosis.

Endometriosis, a chronic and multifocal inflammatory condition with a substantial estrogen-dependent component, is often linked to infertility. Some patients with endometriosis may require surgical intervention or assisted reproductive technologies to conceive. Although many patients who achieve pregnancy have relatively uncomplicated outcomes because of the progesterone-induced regression of endometriotic lesions, complications can still arise during pregnancy and the peripartum period. Complications include the decidualization of endometriosis implants, with site-specific implications (e.g., decidualized endometrioma mimicking ovarian cancer, decidualized deep endometriosis infiltrating the myometrium leading to uterine rupture, spontaneous hemoperitoneum), placenta previa, preterm labor and premature rupture of membranes, postpartum hemorrhage, or systemic conditions such as hypertensive or coagulation disorders. Herein, we review the background of these conditions and the expected radiologic findings. Additionally, we review essential clinical concepts about the treatment available and the information needed to make health care decisions. This review aims to equip radiologists with essential insights into the challenges faced by patients with endometriosis, from infertility diagnosis through postpartum care. By enhancing radiologists’ understanding of these aspects and relevant imaging findings, we aspire to improve maternal and fetal outcomes affected by this complex condition.

Endometriosis is a common cause of subfertility and infertility through myriad mechanisms. Imaging of endometriosis is critical for its diagnosis, characterization, and treatment. Understanding its imaging appearance, surgical management, and implications for assisted reproductive therapy is essential to providing the most clinically relevant and impactful reports in endometriosis patients. This manuscript will review imaging techniques used to diagnose and characterize endometriosis-related infertility. We will describe relevant imaging findings by anatomic site and review the impact of surgery and ART on its appearance. Graphical abstract

Endometrial cancer (EC) is projected to become the third most common cancer and the fourth leading cause of cancer-related deaths among women by 2040, with the most substantial increases observed in non-Hispanic Black, Hispanic, and Asian populations. The International Federation of Gynecology and Obstetrics (FIGO) 2023 classification introduces key updates, including a refined staging system incorporating molecular markers for improved risk stratification. MRI plays a critical role in treatment planning by evaluating the extent of the disease, determining eligibility for fertility-preserving options, guiding radiotherapy planning, and supporting follow-up care. It is also essential for distinguishing EC from cervical cancer. Notable revisions in the FIGO 2023 staging guidelines focus on assessing myometrial invasion, identifying synchronous or metastatic ovarian tumors, and evaluating peritoneal involvement. Recurrence typically occurs within the first 2 years, with common sites including the vaginal vault and lymph nodes, where MRI remains the preferred imaging modality. While CT is routinely employed to assess metastatic disease, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/CT is valuable for detecting nodal and distant metastases. Additionally, some institutions have begun using 18F-FDG PET/MRI to evaluate both local recurrence and distant metastases, offering a more integrated approach for EC follow-up.

Imaging findings of postmenopausal endometriosis reflect the low-estrogen state and show a predominance of fibrosis and adhesions and occasional cystic or glandular components, with malignant transformation of ovarian endometriomas as the most common endometriosis-related neoplasm.

The spectrum of abnormalities associated with endometriosis necessitates a pattern-based, systematic approach that includes diagnosis with dedicated US and MRI protocols to optimize treatment outcomes and effectively communicate with patients and referring physicians.