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SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate classification of clinically significant prostate cancer remains a major challenge. While multiparametric MRI (mpMRI) has improved lesion detection, effective categorization in accordance to the Prostate Imaging Reporting and Data System (PI-RADS) remains complex. In this study, we propose and evaluate three complementary approaches for automated PI-RADS classification differing in the way in which the features are extracted from the mpMRI imaging sequences. The first approach extracts hand-crafted radiomic features from manually segmented lesions using the PyRadiomics library. The second approach extends this by integrating fully automated lesion and zonal segmentation to simulate a real-world, manual-free pipeline. The third approach utilizes a custom convolutional neural network (CNN) to learn high-level features images and lesion masks directly. The images come from Apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), and T2-weighted (T2W) imaging. The features issued by the three methods were used to train a set of machine learning models for multi-class PI-RADS classification, specifically targeting the clinically relevant categories 3, 4, and 5. Results show that ADC-derived features consistently yield superior performance, with one of the ensemble models reaching an AUC of 0.83. Combining features across all sequences further improved robustness (AUC\u2009=\u20090.84). PI-RADS 5 classification was most reliable (AUC\u2009\u2265\u20090.94), whereas PI-RADS 3 remained the most difficult to distinguish. Our findings highlight the effectiveness of ADC features and the advantage of combining automated and deep learning-based strategies for robust prostate cancer risk stratification.<\/jats:p>","DOI":"10.1007\/s42979-026-04983-w","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T10:22:13Z","timestamp":1779186133000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated PI-RADS 3\u20135 Classification Using Multiparametric MRI: A Comparative Study of Radiomics and Deep Learning Approaches"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3367-4952","authenticated-orcid":false,"given":"Saman","family":"Fouladi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6756-4749","authenticated-orcid":false,"given":"Isa Bossi","family":"Zanetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3735-0472","authenticated-orcid":false,"given":"Fatemeh","family":"Darvizeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7963-5679","authenticated-orcid":false,"given":"Rosario","family":"Di Meo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Di Palma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eros","family":"Cambie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonino","family":"Licata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9950-7673","authenticated-orcid":false,"given":"Alessandro","family":"Maiocchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9557-6496","authenticated-orcid":false,"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1087-4866","authenticated-orcid":false,"given":"Corrado","family":"Mio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5186-0199","authenticated-orcid":false,"given":"Gabriele","family":"Gianini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8156-7743","authenticated-orcid":false,"given":"Marco","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8513-6254","authenticated-orcid":false,"given":"Deborah","family":"Fazzini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"4983_CR1","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","volume":"389","author":"HU Ahmed","year":"2017","unstructured":"Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. 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