{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:40:54Z","timestamp":1782949254816,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Cancer Genome Atlas. Two classification workflows were examined: (1) whole-volume input and (2) lesion-focused region of interest. Multiple DL architectures, including ResNet, DenseNet, transformer-based UNeST, and Attention Multiple-Instance Learning (MIL), were implemented within the PyTorch-based MONAI framework. The class imbalance was mitigated using focal loss, oversampling, and dynamic class weighting. The hyperparameters were optimised with Optuna, and balanced accuracy was the primary metric. Results: For a preliminary dataset, the proposed framework demonstrated feasibility for the multi-class classification of ovarian masses. The initial experiments highlighted the potential of transformers and MIL for identifying the relevant imaging features. Conclusions: A reproducible methodological pipeline for DL-based ovarian mass classification using CT and MRI scans has been established. Future work will leverage a multi-institutional dataset to refine these models, aiming to enhance clinical workflows and improve patient outcomes.<\/jats:p>","DOI":"10.3390\/jpm15020076","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T09:36:22Z","timestamp":1739957782000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Methodological Framework for AI-Assisted Diagnosis of Ovarian Masses Using CT and MR Imaging"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1567-9795","authenticated-orcid":false,"given":"Pratik","family":"Adusumilli","sequence":"first","affiliation":[{"name":"Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"},{"name":"Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9NL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-107X","authenticated-orcid":false,"given":"Nishant","family":"Ravikumar","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoff","family":"Hall","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds LS2 9JT, UK"},{"name":"Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9NL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4243-032X","authenticated-orcid":false,"given":"Andrew F.","family":"Scarsbrook","sequence":"additional","affiliation":[{"name":"Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK"},{"name":"Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9NL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"ref_1","unstructured":"Cancer Research UK (2025, January 03). 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