Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review
The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classificatio...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1516264/full |
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| author | Ming-Yue Li Yu Pan Yang Lv He Ma Ping-Li Sun Hong-Wen Gao |
| author_facet | Ming-Yue Li Yu Pan Yang Lv He Ma Ping-Li Sun Hong-Wen Gao |
| author_sort | Ming-Yue Li |
| collection | DOAJ |
| description | The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions. |
| format | Article |
| id | doaj-art-76e761e4a42143f8bebe2eac7dc2348c |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-76e761e4a42143f8bebe2eac7dc2348c2025-01-24T05:21:21ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011510.3389/fonc.2025.15162641516264Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature reviewMing-Yue Li0Yu Pan1Yang Lv2He Ma3Ping-Li Sun4Hong-Wen Gao5Department of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Urology, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Anesthesiology, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Pathology, The Second Hospital of Jilin University, Changchun, Jilin, ChinaThe integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions.https://www.frontiersin.org/articles/10.3389/fonc.2025.1516264/fulldigital pathologyartificial intelligencedeep learningWSIRCCprediction |
| spellingShingle | Ming-Yue Li Yu Pan Yang Lv He Ma Ping-Li Sun Hong-Wen Gao Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review Frontiers in Oncology digital pathology artificial intelligence deep learning WSI RCC prediction |
| title | Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review |
| title_full | Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review |
| title_fullStr | Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review |
| title_full_unstemmed | Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review |
| title_short | Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review |
| title_sort | digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction a literature review |
| topic | digital pathology artificial intelligence deep learning WSI RCC prediction |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1516264/full |
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