{"paper_id":"724b54c2-eebe-4dc2-ba1b-08e2c891c946","body_text":"Electrical Engineering and Systems Science > Image and Video Processing\n[Submitted on 29 Jul 2026]\nTitle:Multi-scale radiomics in pelvic MRI for endometriosis subtyping: highlighting data heterogeneity constraints\nView PDFAbstract:Analyzing female pelvic MRIs is challenging, especially for evaluating endometriosis, where visual features are influenced by several factors, including anatomical complexity, technical variability, and inter-reader variability. Here, we evaluate a radiomics-based pipeline for patient-level endometriosis subtyping using the publicly available UT-EndoMRI dataset. We extract radiomics features from manually segmented uterine and ovarian regions and compare several multi-scale feature representations and feature-selection strategies. We train supervised classifiers to distinguish patients with at least one endometrioma from those without, and perform an unsupervised perturbation analysis to assess whether radiomics profiles reveal reproducible patient subgroups. The best supervised performance is achieved using raw Wavelet-derived features and a Gradient Boosting classifier, yielding an AUC of 0.80. However, this model produces several false positives, resulting in low specificity. ComBat harmonization does not consistently improve performance, suggesting that post hoc harmonization is insufficient in a small, multi-site cohort in which acquisition groups contained very few patients. Using an unsupervised clustering analysis, we identify reproducible but poorly separated partitions that remain associated with acquisition variables. Overall, these results suggest that pelvic MRI radiomics contain a preliminary signal for endometriosis subtyping, while highlighting the fragility of radiomics-based subtyping in small, multi-site datasets.\nSubmission history\nFrom: Elodie Germani [view email] [via CCSD proxy][v1] Wed, 29 Jul 2026 09:39:38 UTC (1,432 KB)\nReferences & Citations\nLoading...\nBibliographic and Citation Tools\nBibliographic Explorer (What is the Explorer?)\nConnected Papers (What is Connected Papers?)\nLitmaps (What is Litmaps?)\nscite Smart Citations (What are Smart Citations?)\nCode, Data and Media Associated with this Article\nalphaXiv (What is alphaXiv?)\nCatalyzeX Code Finder for Papers (What is CatalyzeX?)\nDagsHub (What is DagsHub?)\nGotit.pub (What is GotitPub?)\nHugging Face (What is Huggingface?)\nScienceCast (What is ScienceCast?)\nDemos\nRecommenders and Search Tools\nInfluence Flower (What are Influence Flowers?)\nCORE Recommender (What is CORE?)\narXivLabs: experimental projects with community collaborators\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\nHave an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.","source_license":"CC0","license_restricted":false}