Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types
Abstract 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. Nonetheles...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85857-8 |
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| author | Seung Wan Moon Jisup Kim Young Jae Kim Sung Hyun Kim Chi Sung An Kwang Gi Kim Chan Kwon Jung |
| author_facet | Seung Wan Moon Jisup Kim Young Jae Kim Sung Hyun Kim Chi Sung An Kwang Gi Kim Chan Kwon Jung |
| author_sort | Seung Wan Moon |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-10080ab19f9a4ea7b3c7e5d6033f441a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-10080ab19f9a4ea7b3c7e5d6033f441a2025-01-12T12:14:33ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-85857-8Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic typesSeung Wan Moon0Jisup Kim1Young Jae Kim2Sung Hyun Kim3Chi Sung An4Kwang Gi Kim5Chan Kwon Jung6Department of Biomedical Engineering, Pre-medical Course, College of Medicine, Gil Medical Center, Gachon UniversityDepartment of Pathology, Gil Medical Center, Gachon University College of MedicineDepartment of Biomedical Engineering, Pre-medical Course, College of Medicine, Gil Medical Center, Gachon UniversityDepartment of AI Data, National Information Society Agency(NIA)Urban Datalab, Electronics and Telecommunications Research Institute Convergence CenterDepartment of Biomedical Engineering, Pre-medical Course, College of Medicine, Gil Medical Center, Gachon UniversityDepartment of Hospital Pathology, College of Medicine, The Catholic University of KoreaAbstract 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.https://doi.org/10.1038/s41598-025-85857-8Renal tumor classificationDeep learningMultiple instance learningDigital pathology |
| spellingShingle | Seung Wan Moon Jisup Kim Young Jae Kim Sung Hyun Kim Chi Sung An Kwang Gi Kim Chan Kwon Jung Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types Scientific Reports Renal tumor classification Deep learning Multiple instance learning Digital pathology |
| title | Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types |
| title_full | Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types |
| title_fullStr | Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types |
| title_full_unstemmed | Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types |
| title_short | Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types |
| title_sort | leveraging explainable ai and large scale datasets for comprehensive classification of renal histologic types |
| topic | Renal tumor classification Deep learning Multiple instance learning Digital pathology |
| url | https://doi.org/10.1038/s41598-025-85857-8 |
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