{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:42:47Z","timestamp":1763545367641,"version":"3.45.0"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"RadioVal project","doi-asserted-by":"publisher","award":["101057699"],"award-info":[{"award-number":["101057699"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This study aims to develop an explainable radiomics-based model for the automatic assessment of image quality in breast cancer Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data. A cohort of 280 images obtained from a public database was annotated by two clinical experts, resulting in 110 high-quality and 110 low-quality images. The proposed methodology involved the extraction of 819 radiomic features and 2 No-Reference image quality metrics per patient, using both the whole image and the background as regions of interest. Feature extraction was performed under two scenarios: (i) from a sample of 12 slices per patient, and (ii) from the middle slice of each patient. Following model training, a range of machine learning classifiers were applied with explainability assessed through SHapley Additive Explanations (SHAP). The best performance was achieved in the second scenario, where combining features from the whole image and background with a support vector machine classifier yielded sensitivity, specificity, accuracy, and AUC values of 85.51%, 80.01%, 82.76%, and 89.37%, respectively. This proposed model demonstrates potential for integration into clinical practice and may also serve as a valuable resource for large-scale repositories and subgroup analyses aimed at ensuring fairness and explainability.<\/jats:p>","DOI":"10.3390\/jimaging11110417","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T08:50:07Z","timestamp":1763542207000},"page":"417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Radiomics-Based Model for Automatic Image Quality Assessment in Breast Cancer DCE MRI Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8139-5790","authenticated-orcid":false,"given":"Georgios S.","family":"Ioannidis","sequence":"first","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"},{"name":"Department of Biomedical Sciences, Radiology-Radiotherapy Sector, University of West Attica, 12243 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katerina","family":"Nikiforaki","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5242-6060","authenticated-orcid":false,"given":"Aikaterini","family":"Dovrou","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"},{"name":"Medical School, University of Crete, 71003 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vassilis","family":"Kilintzis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2194-7709","authenticated-orcid":false,"given":"Grigorios","family":"Kalliatakis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6789-5177","authenticated-orcid":false,"given":"Oliver","family":"Diaz","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Lab (BCN-AIM), Department of Mathematics and Computer Science, Universitat de Barcelona, 08007 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9456-1612","authenticated-orcid":false,"given":"Karim","family":"Lekadir","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Lab (BCN-AIM), Department of Mathematics and Computer Science, Universitat de Barcelona, 08007 Barcelona, Spain"},{"name":"Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats (ICREA), Passeig Llu\u00eds Companys 23, 08010 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-5223","authenticated-orcid":false,"given":"Kostas","family":"Marias","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"},{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Herath, H.M.S.S., Herath, H.M.K.K.M.B., Madusanka, N., and Lee, B.-I. 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