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Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Kidney Neoplasms*/pathology ; Kidney Neoplasms*/classification ; Carcinoma, Renal Cell*/pathology ; Carcinoma, Renal Cell*/classification ; Kidney*/pathology ; Artificial Intelligence*; Humans ; ROC Curve
    • Abstract:
      Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
      Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied. This gap in research can primarily be attributed to the limited availability of extensive datasets including benign tumor and normal tissue in addition to specific type of renal cell carcinoma, which hampers the ability to conduct comprehensive studies in these broader categories. This research introduces a model aimed at classifying renal tissue into three primary categories: normal (non-neoplastic), benign tumor, and malignant tumor. Utilizing digital pathology while slide images (WSIs) from nephrectomy specimens of 2,535 patients from multiple institutions, the model provides a foundational approach for distinguishing these key tissue types. The study utilized a dataset of 12,223 WSIs comprising 1,300 WSIs of normal tissue, 700 WSIs of benign tumors, and 10,223 WSIs of malignant tumors. Employing the ResNet-18 architecture and a Multiple Instance Learning approach, the model demonstrated high accuracy, with F1-scores of 0.934 (CI: 0.933-0.934) for normal tissue, 0.684 (CI: 0.682-0.687) for benign tumors, and 0.878 (CI: 0.877-0.879) for malignant tumors. The overall performance was also notable, achieving a weighted average F1-score of 0.879 (CI: 0.879-0.880) and a weighted average area under the receiver operating characteristic curve of 0.969 (CI: 0.969-0.969). This model significantly aids in the swift and accurate diagnosis of renal tissue, encompassing non-neoplastic normal tissue, benign tumor, and malignant tumor.
      (© 2025. The Author(s).)
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    • Grant Information:
      K_G012001187801 Ministry of Trade, Industry and Energy; GRRC-Gachon2023(B01) Gyeonggi-do Regional Research Center; RS-2021-KH113146 Korea Health Industry Development Institute; No. RS-2022-00166555 National Research Foundation of Korea
    • Contributed Indexing:
      Keywords: Deep learning; Digital pathology; Multiple instance learning; Renal tumor classification
    • Publication Date:
      Date Created: 20250111 Date Completed: 20250111 Latest Revision: 20250509
    • Publication Date:
      20250509
    • Accession Number:
      PMC11724863
    • Accession Number:
      10.1038/s41598-025-85857-8
    • Accession Number:
      39799164