{
  "abstract": "Objective\n\nAccurate early diagnosis of Clinically Significant prostate cancer (csPCa) is crucial for improving patient survival rates. This study aimed to develop and validate an interpretable, confidence-based deep learning model for csPCa classification using biparametric MRI (bpMRI).Methods\n\nA retrospective analysis was conducted on bpMRI data from 410 patients. A classification model was constructed using the visual geometry group 16-layer (VGG-16) network, incorporating gradient-weighted class activation mapping (Grad-CAM) for visual interpretability, Monte Carlo Dropout (MC-Dropout) for uncertainty estimation, and a rejection mechanism. The model's performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC).Results\n\nAcross all predictions, the confidence-based model achieved an overall accuracy of 79.3% before rejection. At a confidence threshold of 0.8, 80.8% of predictions were retained, among which the selective accuracy was 94.6%, with 99 false-positive and 2 false-negative predictions. Besides, the model's accuracy outperformed that of a senior radiologist (94.6% vs 90.8%). Qualitative Grad-CAM visualization demonstrated that the classifier frequently assigned high activation to MRI regions considered suspicious for csPCa; however, quantitative spatial agreement with lesion annotations was not assessed.Conclusion\n\nThis confidence-based model identifies csPCa lesions and provides reliable confidence scores without pixel-level annotations. By rejecting uncertain predictions, it enhances the transparency and safety of clinical decision-making.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Cheng Hao"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuze Meng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yong Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Xia Du"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/32eaf8jmfb0qn.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1819986",
  "fulltext_url": "https://europepmc.org/articles/PMC13683124",
  "id": "32eaf8jmfb0qn",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42908607",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7220852711",
  "pdf_url": "https://europepmc.org/articles/PMC13683124?pdf=render",
  "pmcid": "PMC13683124",
  "pmid": "42908607",
  "published": "2026-10-07",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/32eaf8jmfb0qn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42908607",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42908607"
    },
    {
      "first_seen": "2026-10-30T12:20:13Z",
      "native_id": "10.3389/fonc.2026.1819986",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1819986/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2234-943X",
      "subfield": "oncology"
    }
  ],
  "title": "Confidence-based classification of clinically significant prostate cancer on biparametric MRI using Monte Carlo dropout and Grad-CAM visualization.",
  "title_text": "Confidence-based classification of clinically significant prostate cancer on biparametric MRI using Monte Carlo dropout and Grad-CAM visualization.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:13Z",
  "url": "https://research.draconitic.com/paper/32eaf8jmfb0qn",
  "venue": "Frontiers in oncology",
  "versions": []
}