The demand for breast imaging services continues to grow, driven by expanding indications in breast cancer diagnosis and treatment. This increasing demand underscores the potential role of artificial intelligence (AI) to enhance workflow efficiency as well as to further unlock the abundant imaging data to achieve improvements along the breast cancer pathway. Although AI has made significant advancements in mammography and digital breast tomosynthesis, with commercially available computer-aided detection (CAD systems) widely used for breast cancer screening and detection, its adoption in breast MRI has been slower. This lag is primarily attributed to the inherent complexity of breast MRI examinations and also hence the more limited availability of large, well-annotated publicly available breast MRI datasets. Despite these challenges, interest in AI implementation in breast MRI remains strong, fueled by the expanding use and indications for breast MRI. This article explores the implementation of AI in breast MRI across the breast cancer care pathway, highlighting its potential to revolutionize the way we detect and manage breast cancer. By addressing current challenges and examining emerging AI applications, we aim to provide a comprehensive overview of how AI is reshaping breast MRI and improving outcomes for patients.

Breast diffusion weighted MR imaging (DWI) is increasingly used, because it is fast and easy to be added in clinical protocol without contrast agent and provides information of cellularity or tissue microstructure. This review article explores the principles of breast DWI, the standardization of acquisition techniques, and its current clinical applications. We emphasize its role in differentiating benign from malignant lesions, reducing unnecessary biopsies, and discuss the evidence supporting DWI as a potential standalone screening tool. Prognostic indicators derived from DWI parameters and its utility in monitoring treatment responses are discussed. Finally, we look to the future, discussing emerging techniques. This review provides a comprehensive overview of breast DWI’s current status and future potential.

The early detection and treatment of breast cancer is extremely important for extending patients’ outcomes. Breast MRI has high sensitivity for the detection of breast cancer and plays an important role in breast cancer diagnosis and treatment, but conventional dynamic contrast-enhanced (DCE) MRI may be too time-consuming for breast cancer screening purposes. Abbreviated MRI is a technique that can be applied within a short time, as usually only the pre-contrast and first post-contrast images from the dynamic study or additional T2-weighted imaging are used. Abbreviated MRI may thus be suitable for breast cancer screening. In addition, its diagnostic performance for differentiating benign and malignant breast lesions is superior to that of breast tomosynthesis and comparable to that of conventional DCE MRI. The usefulness of abbreviated MRI for patients with a history of breast cancer and in clinical settings has been described, but the specificity of abbreviated DCE MRI is slightly lower than that of conventional DCE MRI. Ultrafast DCE MRI is a technique that obtains kinetic information by capturing multiple time phases in a short time scan in the very early phase after the injection of contrast material. Various parameters, including the maximum slope and time to enhancement can be used to evaluate kinetic information. Based on this kinetic information, ultrafast DCE MRI can differentiate between benign and malignant breast lesions. Since background parenchymal enhancement (BPE) is weak in the very early phase after a contrast media injection, ultrafast DCE MRI is also useful for identifying lesions in patients with marked BPE on conventional DCE MRI. In addition, ultrafast DCE MRI is useful for predicting the prognostic marker status of breast cancer, assessing the effectiveness of neoadjuvant therapy, examining MRI-detected lesions before surgery, and morphological assessments.

Lobular endocervical glandular hyperplasia (LEGH) is a rare benign lesion of the uterine cervix that produces gastric-type mucin. First identified in 1999, LEGH is often misdiagnosed as other glandular lesions, including adenocarcinoma, human papillomavirus (HPV)–independent, gastric-type (GAS), due to similar histopathological features. LEGH is now recognized as a precursor to GAS, a malignancy with poor prognosis. This review explores LEGH’s pathological and immunohistochemical characteristics and related glandular lesions, focusing on diagnostic approaches using MRI. MRI has proven essential in distinguishing LEGH from other benign cervical cystic lesions and detecting precursor conditions, such as atypical LEGH, before progression to GAS. A hallmark MRI finding for LEGH is the “cosmos pattern,” featuring centrally clustered microcysts surrounded by macrocysts, achieving 95.5% specificity when combined with T1-weighted imaging. Cytology and biopsy improve diagnostic accuracy when imaging results are inconclusive, though obtaining high-quality specimens can be challenging due to lesion location. This article reviews cytological findings, the presence of gastric-type mucin, and MRI features useful for differentiating LEGH from benign non-LEGH lesions, as well as for diagnosing precancerous and malignant conditions. Recent advances in research have led to the recognition that GAS is primarily a solid rather than a cystic lesion, contributing to improved diagnostic accuracy of MRI for GAS. However, some GAS cases and atypical LEGH can still exhibit a cosmos pattern on MRI, similar to LEGH, making differentiation challenging. Therefore, we also discuss a diagnostic strategy integrating MRI findings with cytology and presence or absence of gastric-type mucin.

Placental insufficiency is a critical condition in perinatal medicine, clinically manifesting as fetal growth restriction or preeclampsia. In addition to ultrasound and Doppler velocimetry, MRI has been assessed intensively for its potential to evaluate placental function directly. Several methods investigated to date include anthropometry, visual assessments using T2-weighted images, and quantitative evaluations based on T2 values, hypoxia indicators (T2* values and blood oxygenation level-dependent imaging), and perfusion metrics (intravoxel incoherent motion and arterial spin labeling). Anthropometry and visual assessments are easily implemented clinically because they require no specific technique or post-processing. By contrast, quantitative approaches provide objective numerical indicators, making them promising imaging biomarkers. Despite their potential, translating these methods into clinical practice presents challenges, especially for quantitative techniques, because of limited availability, lack of standardization, and inadequate clinician awareness. This review was conducted to overview the clinical aspects of placental insufficiency, summarize the anthropometry, visual assessments, and quantitative methods reported, and highlight the latest advancements. It also presents discussion of related challenges and future prospects for clinical implementation.

This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.

The umbilical cord and placenta, classified as fetal appendages, play a crucial role in fetal growth and survival. While ultrasonography remains the primary modality for evaluation, MRI provides a more objective and comprehensive assessment, particularly for the placenta and fetus. Although MRI assessment of the umbilical cord is still emerging and not yet widely adopted by radiologists, MRI has the potential to support and complement ultrasound findings, thereby contributing to safer perinatal management. This review summarizes key MRI findings related to the umbilical cord and associated placental abnormalities. In cases where MRI reports are currently unavailable, ultrasound findings are included to supplement the discussion and to support inferences of potential MRI findings, encouraging future diagnostic applications. Initially, we describe the normal morphology and physiological changes of the umbilical cord and placenta, recommended MRI sequences, and normal imaging findings. Placental abnormalities associated with umbilical cord anomalies, including morphological and positional abnormalities, are reviewed. Variations in umbilical cord insertion, particularly marginal and velamentous insertion, as well as vasa previa, are also discussed. Abnormalities related to umbilical cord position and morphology, such as hypercoiling and hypocoiling, cord knots, nuchal cords, and cord presentation, are examined. Additionally, vascular abnormalities, including single umbilical artery, fused umbilical artery, supernumerary vessels, varices, and aneurysms, are explored along with their underlying mechanisms. Finally, umbilical cord cysts and umbilical cord ulcers are addressed. Advancements in imaging technology, particularly Doppler ultrasonography, have significantly improved the detection and management of umbilical cord and placental abnormalities. Understanding these abnormalities in prenatal MRI evaluation is crucial for improving diagnostic accuracy and optimizing perinatal care. MRI may prove to be a useful adjunct when ultrasound findings are inconclusive or when additional anatomical and functional information is needed. However, further studies are needed to clarify MRI’s clinical utility.

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.