The British Journal of Radiology has published a collection of articles about female genitourinary oncology.
The articles explore current topics, including ovarian and cervical cancer staging, characterization, common pitfalls of the endometrium, molecular imaging, pelvic exenteration, radiogenomics in gynecological cancers, and differentiating uterine leiomyomas and leiomyosarcomas.
The special focus was guest edited by Prof. Evis Sala (University of Cambridge School of Clinical Medicine and Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, U.K.), Dr. Stephanie Nougaret (Cancer Institute of Montpellier, France), and Dr. Alberto Vargas (Memorial Sloan Kettering Cancer Center, New York).
43-year-old woman. The tumor crawls on the surface of the anterior lip of the cervix to anterior fornix of vagina. (b) Sagittal and (c) axial diffusion-weighted imaging (DWI) clearly show tumor extension to the anterior vaginal fornix. Vaginal wall invasion was suspected at anterior fornix by irregularity. This case was diagnosed as stage IIA in 2017 under the previous International Federation of Gynecology and Obstetrics (FIGO) stage system. (d) Round lymph node about 8 mm in diameter was observed at the left obturator node (arrow). (e) FDG-PET/CT showed mild uptake, with suspected lymph node metastasis. At operation, lymph node metastasis was confirmed. Therefore, this case is stage IIIC1 at the present FIGO stage. Figure courtesy of BJR.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
