{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 81,
  "authors": [
    {
      "name": "Avtantil Dimitriadis",
      "orcid": "0000-0002-3667-7051"
    },
    {
      "name": "Grigorios Kalliatakis"
    },
    {
      "name": "Richard Osuala"
    },
    {
      "name": "Dimitri Kessler"
    },
    {
      "name": "Oliver Diaz"
    },
    {
      "name": "Simone Mazzetti"
    },
    {
      "name": "Daniele Regge"
    },
    {
      "name": "Karim Lekadir"
    },
    {
      "name": "Dimitrios Fotiadis"
    },
    {
      "name": "Manolis Tsiknakis"
    },
    {
      "name": "Nikolaos Papanikolaou"
    },
    {
      "name": "the ProCAncer-I Consortium"
    },
    {
      "name": "Stelios Sfakianakis"
    },
    {
      "name": "Varvara Kalokyri"
    },
    {
      "name": "Eleftherios Trivizakis"
    },
    {
      "name": "Nikolaos Tachos"
    },
    {
      "name": "Eugenia Mylona"
    },
    {
      "name": "Dimitrios Zaridis"
    },
    {
      "name": "Charalampos Kalantzopoulos"
    },
    {
      "name": "José Guilherme de Almeida"
    },
    {
      "name": "Ana Castro Verde"
    },
    {
      "name": "Ana Carolina Rodrigues"
    },
    {
      "name": "Nuno Rodrigues"
    },
    {
      "name": "Miguel Chambel"
    },
    {
      "name": "Henkjan Huisman"
    },
    {
      "name": "Maarten de Rooij"
    },
    {
      "name": "Anindo Saha"
    },
    {
      "name": "Jasper J. Twilt"
    },
    {
      "name": "Jurgen Futterer"
    },
    {
      "name": "Luis Martí-Bonmatí"
    },
    {
      "name": "Leonor Cerdá-Alberich"
    },
    {
      "name": "Gloria Ribas"
    },
    {
      "name": "Silvia Navarro"
    },
    {
      "name": "Manuel Marfil"
    },
    {
      "name": "Emanuele Neri"
    },
    {
      "name": "Giacomo Aringhieri"
    },
    {
      "name": "Lorenzo Tumminello"
    },
    {
      "name": "Vincenzo Mendola"
    },
    {
      "name": "Deniz Akata"
    },
    {
      "name": "Mustafa Özmen"
    },
    {
      "name": "Ali Devrim Karaosmanoglu"
    },
    {
      "name": "Firat Atak"
    },
    {
      "name": "Musturay Karcaaltincaba"
    },
    {
      "name": "Joan C. Vilanova"
    },
    {
      "name": "Jurgita Usinskiene"
    },
    {
      "name": "Ruta Briediene"
    },
    {
      "name": "Audrius Untanas"
    },
    {
      "name": "Kristina Slidevska"
    },
    {
      "name": "Katsaros Vasilis"
    },
    {
      "name": "Georgiou Georgios"
    },
    {
      "name": "Dow-Mu Koh"
    },
    {
      "name": "Robby Emsley"
    },
    {
      "name": "Sharon Vit"
    },
    {
      "name": "Ana Ribeiro"
    },
    {
      "name": "Simon Doran"
    },
    {
      "name": "Tiaan Jacobs"
    },
    {
      "name": "Gracián García-Martí"
    },
    {
      "name": "Valentina Giannini"
    },
    {
      "name": "Giovanni Cappello"
    },
    {
      "name": "Giovanni Maimone"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9qn6m2qqvq7ek.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-71998-x",
  "fulltext_url": null,
  "id": "9qn6m2qqvq7ek",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-71998-x",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
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  "pdf_url": "https://www.nature.com/articles/s41598-026-71998-x_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/9qn6m2qqvq7ek.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.1038/s41598-026-71998-x",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-71998-x"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
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  ],
  "title": "Cross-attention fusion of biparametric MRI sequences with demographic and clinical variables for patient-level classification of clinically significant prostate cancer",
  "title_text": "Cross-attention fusion of biparametric MRI sequences with demographic and clinical variables for patient-level classification of clinically significant prostate cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/9qn6m2qqvq7ek",
  "venue": "Scientific Reports",
  "versions": []
}