Cross-attention fusion of biparametric MRI sequences with demographic and clinical variables for patient-level classification of clinically significant prostate cancer
Abstract
Abstract Accurate patient-level risk stratification of clinically significant prostate cancer (csPCa) on biparametric MRI remains challenging, as most existing approaches require costly lesion-level annotations or have not been evaluated in large temporally separated cohorts. We developed and validated a multimodal deep learning framework combining T2-weighted (T2w), diffusion-weighted (DWI), and apparent diffusion coefficient (ADC) volumes with age, prostate-specific antigen (PSA), and PSA density (PSAD) for patient-level csPCa classification, trained without lesion-level annotations. The study included a retrospective development cohort of 3, 939 examinations and an independent temporal validation cohort comprising 1, 409 prospectively acquired examinations from 13 centers. The model employs three sequence-specific encoders with asymmetric cross-attention fusion and a two-stage transfer-learning strategy. Adding demographic and clinical variables consistently improved performance over MRI-only models, with PSAD emerging as the most informative complementary factor, primarily by improving specificity. The best-performing model—combining MRI, age, PSA, and PSAD with cross-attention—achieved a mean AUC of $$0.765\!\pm \!0.006$$ on the retrospective held-out test set and $$0.741\!\pm \!0.006$$ on the independent temporal validation cohort. Post hoc subgroup analyses suggested broadly stable rank-order discrimination (AUC) across most strata, though fixed-threshold sensitivity and specificity varied substantially, while Grad-CAM maps offered a qualitative indication of more frequent overlap with suspicious regions under cross-attention. These findings support PSAD-informed multimodal patient-level models as a viable tool for refined csPCa risk stratification on biparametric MRI, with potential to reduce false-positive classification of ISUP grade group 1 disease as clinically significant.
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Authors: Avtantil Dimitriadis, Grigorios Kalliatakis, Osuala Richard, Dimitri Kessler, Oliver Díaz, Simone Mazzetti, Daniele Regge, Karim Lekadir, Dimitrios Fotiadis, Manolis Tsiknakis, Nikolaos Papanikolaou, Sfakianakis Stelios, Varvara Kalokyri, Eleftherios Trivizakis, Nikolaos S. Tachos, Eugenia Mylona, Dimitrios Zaridis, Charalampos Kalantzopoulos, José Guilherme de Almeida, Ana Castro Verde, Ana Carolina Rodrigues, Nuno Rodrigues, Miguel Chambel, HenkJan J. Huisman, Maarten de Rooij, Anindo Saha, Jasper J. Twilt, Jurgen Futterer, Luis Martí‐Bonmatí, Leonor Cerdá-Alberich, Gloria Ribas, Silvia Navarro, Manuel Marfil, Emanuele Neri, Giacomo Aringhieri, Lorenzo Tumminello, Vincenzo Mendola, Deniz Akata, Mustafa Ozmen, Ali Devrim Karaosmanoğlu, Fırat Atak, Muşturay Karçaaltıncaba, Joan Carles Vilanova, Jurgita Ušinskienė, Rūta Briedienė, Audrius Untanas, Kristina Slidevska, Katsaros Vasilis, Georgiou Georgios, Koh Dow-Mu, Robby Emsley, Sharon Vit, Ana Ribeiro, Simon Doran, Tiaan Jacobs, Gracián García‐Martí, Valentina Giannini, Giovanni Cappello, Giovanni Maimone, Valentina Napolitano, Sara Colantonio, Maria Antonietta Pascali, Eva Pachetti, Giulio Del Corso, Danila Germanese, Andrea Berti, Gianluca Carloni, Jayashree Kalpathy-Cramer, Christopher Bridge, João Correia, Walter Hernández, Zoi Giavri, Christos Pollalis, Dimitrios Agraniotis, Ana Jiménez Pastor, José Munuera Mora, C Saillant, Theresa Henne, Rodessa Marquez, Kostas Marias