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MRI plays important roles in endometrial and cervical cancer assessment, from detection to recurrent disease evaluation. Endometrial cancer (EC) is the most common malignant tumor of the female genital tract in Western countries. EC patients are divided into risk categories based on histopathological tumor type, grade, and myometrial invasion depth. EC is surgically staged using the International Federation of Gynecology and Obstetrics (FIGO) system. Since FIGO (2009) stage correlates with prognosis, preoperative staging is essential for tailored treatment. MRI reveals myometrial invasion depth, which correlates with tumor grade and lymph node metastases, and thus correlates with prognosis. Cervical cancer (CC) is the second most common cancer, and the third leading cause of cancer-related death among females in developing countries. The FIGO Gynecologic Oncology Committee recently revised its CC staging guidelines, allowing staging based on imaging and pathological findings when available. The revised FIGO (2018) staging includes node involvement and thus enables both therapy selection and evaluation, prognosis estimation, and calculation of end results. MRI can accurately assess prognostic indicators, e.g., tumor size, parametrial invasion, pelvic sidewall, and lymph node invasion. Despite these important roles of MRI, radiologists still face challenges due to the technical and interpretation pitfalls of MRI during all phases of endometrial and cervical cancer evaluation. Awareness of mimics that can simulate both cancers is critical. With careful application, functional MRI with DWI and DCE sequences can help establish a correct diagnosis, although it is sometimes necessary to perform biopsy and histopathological analysis.

In female patients, acute pelvic pain can be caused by gynaecological, gastrointestinal, and urinary tract pathologies. Due to the variety of diagnostic possibilities, the correct assessment of these patients may be challenging. The most frequent gynaecological causes of acute pelvic pain in non-pregnant women are pelvic inflammatory disease, ruptured ovarian cysts, ovarian torsion, and degeneration or torsion of uterine leiomyomas. On the other hand, spontaneous abortion, ectopic pregnancy, and placental disorders are the most frequent gynaecological entities to cause acute pelvic pain in pregnant patients. Ultrasound (US) is usually the first-line diagnostic technique because of its sensitivity across most common aetiologies and its lack of radiation exposure. Computed tomography (CT) may be performed if ultrasound findings are equivocal or if a gynaecologic disease is not initially suspected. Magnetic resonance imaging (MRI) is an extremely useful second-line technique for further characterisation after US or CT. This pictorial review aims to review the spectrum of gynaecological entities that may manifest as acute pelvic pain in the emergency department and to describe the imaging findings of these gynaecological conditions obtained with different imaging techniques.

In diagnostic imaging, distinguishing chromophobe renal cell carcinomas (chRCCs) from renal oncocytomas (ROs) is challenging, since they both present similar radiological characteristics. Radiomics has the potential to help in the differentiation between chRCCs and ROs by extracting quantitative imaging. This is a preliminary study of the role of radiomic features in the differentiation of chRCCs and ROs using machine learning models. In this retrospective work, 38 subjects were involved: 19 diagnosed with chRCCs and 19 with ROs. The CT nephrographic contrast phase was selected in each case. Three-dimensional segmentations of the lesions were performed and the radiomic features were extracted. To assess the reliability of the features, the intraclass correlation coefficient was calculated from the segmentations performed by three radiologists with different degrees of expertise. The selection of features was based on the criteria of excellent intraclass correlation coefficient (ICC), high correlation, and statistical significance. Three machine learning models were elaborated: support vector machine (SVM), random forest (RF), and logistic regression (LR). From 105 extracted features, 41 presented an excellent ICC and 6 were not highly correlated with each other. Only two features showed significant differences according to histological type and machine learning models were developed with them. LR was the better model, in particular, with an 83% precision.

Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. However, several studies are not robust, do not include validation cohorts, or lack reproducibility. On these bases, the purpose of this narrative review is to provide an overview of the most relevant studies in the literature on radiomics in gynaecological imaging. We focused on gynaecological malignancies, particularly endometrial, cervical, mesenchymal, and ovarian malignant pathologies.

Magnetic resonance imaging (MRI) is an effective technique for the diagnosis and preoperative staging of deep infiltrative endometriosis (DIE). The usefulness of MRI sequences susceptible to chronic blood degradation products, such as T2*-weighted imaging, remains uncertain. The present study aims to evaluate the diagnostic performance of these sequences in addition to the conventional protocol for DIE assessment. Forty-four MRI examinations performed for clinical and/or ultrasound DIE suspicion were evaluated by three readers with variable experience in female imaging. The inter-observer agreement between the reader who analysed only the conventional protocol and the one who also considered T2*-weighted sequences was excellent. The less experienced reader diagnosed a significantly higher number of endometriosis foci on the T2*-weighted sequences compared with the most experienced observer. T2*-weighted sequences do not seem to provide significant added value in the evaluation of DIE, especially in less experienced readers. Furthermore, artifacts caused by undesirable sources of magnetic inhomogeneity may lead to overdiagnosis.

ObjectiveThe aim of the Female Pelvic Imaging Working Group of the European Society of Urogenital Radiology (ESUR) was to develop imaging staging guidelines for vulvar cancer and to propose standardised MRI protocols and reporting.MethodsThe guidelines recommended from the ESUR in this article resulted from a questionnaire analysis regarding imaging staging of vulvar cancer that was answered by all members of the Female Pelvic Imaging Working Group. Only the answers with an agreement equal to or more than 80% were considered. Additionally, the literature was reviewed to complement and further support our conclusions.ResultsThe critical review of the literature and consensus obtained among experts allows for recommendations regarding imaging staging guidelines, patient preparation, MRI protocol, and a structured MRI report.ConclusionsStandardising image acquisition techniques and MRI interpretation reduces ambiguity and ultimately improves the contribution of radiology to the staging and management of patients with vulvar cancer. Moreover, structured reporting assists with the communication of clinically relevant information to the referring physician.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, and its prognosis is closely linked to the International Society of Urological Pathology (ISUP) grade. While histopathological evaluation remains the gold standard for grading, non-invasive methods, such as radiomics, offer potential for automated classification. This study aims to develop a radiomics-based machine learning model for the ISUP grade classification of ccRCC using nephrographic-phase CT images, with an emphasis on model interpretability through SHAP (SHapley Additive exPlanations) values. Objective: To develop and interpret a radiomics-based machine learning model for classifying ISUP grade in clear cell renal cell carcinoma (ccRCC) using nephrographic-phase CT images. Materials and Methods: This retrospective study included 109 patients with histopathologically confirmed ccRCC. Radiomic features were extracted from the nephrographic-phase CT scans. Feature robustness was evaluated via intraclass correlation coefficient (ICC), followed by redundancy reduction using Pearson correlation and minimum Redundancy Maximum Relevance (mRMR). Logistic regression, support vector machine, and random forest classifiers were trained using 8-fold cross-validation. SHAP values were computed to assess feature contribution. Results: The logistic regression model achieved the highest classification performance, with an accuracy of 82% and an AUC of 0.86. SHAP analysis identified major axis length, busyness, and large area emphasis as the most influential features. These variables represented shape and texture information, critical for distinguishing between high and low ISUP grades. Conclusions: A radiomics-based logistic regression model using nephrographic-phase CT enables accurate, non-invasive classification of ccRCC according to ISUP grade. The use of SHAP values enhances model transparency, supporting clinical interpretability and potential adoption in precision oncology.

ObjectivesTo update the 2009 ESUR endometrial cancer guidelines and propose strategies to standardize image acquisition, interpretation and reporting for endometrial cancer staging with MRI.MethodsThe published evidence-based data and the opinion of experts were combined using the RAND-UCLA Appropriateness Method and formed the basis for these consensus guidelines. The responses of the experts to 81 questions regarding the details of patient preparation, MR imaging protocol, image interpretation and reporting were collected, analysed and classified as “RECOMMENDED” versus “NOT RECOMMENDED” (if at least 80% consensus among experts) or uncertain (if less than 80% consensus among experts).ResultsConsensus regarding patient preparation, MR image acquisition, interpretation and reporting was determined using the RAND-UCLA Appropriateness Method. A tailored MR imaging protocol and a standardized report were recommended.ConclusionsThese consensus recommendations should be used as a guide for endometrial cancer staging with MRI.Key points• MRI is recommended for initial staging of endometrial cancer.• MR imaging protocol should be tailored based on the risk of lymph node metastases.• Myometrial invasion is best assessed using combined axial-oblique T2WI, DWI and contrast-enhanced imaging.• The mnemonic “Clinical and MRI Critical TEAM” summarizes key elements of the standardized report.

Since the Bosniak cysts classification is highly reader-dependent, automated tools based on radiomics could help in the diagnosis of the lesion. This study is an initial step in the search for radiomic features that may be good classifiers of benign–malignant Bosniak cysts in machine learning models. A CCR phantom was used through five CT scanners. Registration was performed with ARIA software, while Quibim Precision was used for feature extraction. R software was used for the statistical analysis. Robust radiomic features based on repeatability and reproducibility criteria were chosen. Excellent correlation criteria between different radiologists during lesion segmentation were imposed. With the selected features, their classification ability in benignity–malignity terms was assessed. From the phantom study, 25.3% of the features were robust. For the study of inter-observer correlation (ICC) in the segmentation of cystic masses, 82 subjects were prospectively selected, finding 48.4% of the features as excellent regarding concordance. Comparing both datasets, 12 features were established as repeatable, reproducible, and useful for the classification of Bosniak cysts and could serve as initial candidates for the elaboration of a classification model. With those features, the Linear Discriminant Analysis model classified the Bosniak cysts in terms of benignity or malignancy with 88.2% accuracy.

ObjectiveTo develop imaging guidelines for the MR work-up of female genital tract congenital anomalies (FGTCA).MethodsThese guidelines were prepared based on a questionnaire sent to all members of the European Society of Urogenital Radiology (ESUR) Female Pelvic Imaging Working Group (FPI-WG), critical review of the literature and expert consensus decision.ResultsThe returned questionnaires from 17 different institutions have shown reasonable homogeneity of practice. Recommendations with focus on patient preparation and MR protocol are proposed, as these are key to optimised examinations. Details on MR sequences and planning of uterus-orientated sequences are provided.ConclusionsThe multiplanar capabilities and soft tissue resolution of MRI provide superb characterisation of the wide spectrum of findings in FGTCA. A standardised imaging protocol and method of reporting ensures that the salient features are recognised, contributing to a correct diagnosis and classification of FGTCA, associated anomalies and complications. These imaging guidelines are based on current practice among expert radiologists in the field and incorporate up to date information regarding MR protocols and essentials of recently published classification systems.Key Points• MRI allows comprehensive evaluation of female genital tract congenital anomalies, in a single examination.• A dedicated MRI protocol comprises uterus-orientated sequences and vaginal and renal evaluation.• Integration of classification systems and structured reporting helps in successful communication of the imaging findings.