Towards artificial intelligence‐assisted digital pathology: A systematic evaluation of multimodal generative artificial intelligence in clear cell renal cell carcinoma assessment
Year: 2025
Type: article
Abstract: Clear cell renal cell carcinoma (ccRCC), the most common subtype of RCC, requires accurate pathological grading for effective prognosis. However, current grading methods rely heavily on subjective pathologist assessment, leading to variability. While generative artificial intelligence (GenAI) has shown promise in medical imaging, its application in digital pathology remains underexplored. This study evaluates the performance of three multimodal GenAI models—GPT‐4o, Claude‐3.5‐Sonnet, and Gemini‐1.5‐Pro—in ccRCC grading and prognosis prediction. A total of 499 ccRCC slides from The Cancer Genome Atlas and 349 external samples from two independent cohorts were analyzed. A standardized prompt repetition mechanism and variance‐based stability validation method guided GenAI models in extracting 17 pathological features. Feature stability was assessed using intraclass correlation coefficient (ICC). These features, combined with 3 clinical variables, were used to build grading and prognostic models via logistic regression and 113 machine learning algorithms. Performance was benchmarked against CellProfiler, ResNet‐50, DenseNet‐121, attention‐based multiple instance learning (MIL) and Pathology Language and Image Pre‐training, using the concordance index (C‐index) and area under the receiver operating characteristic curve (AUC). Claude‐3.5‐Sonnet outperformed the other two GenAI models (ICC = 0.76; micro‐average AUC = 0.87), exceeding ResNet‐50 (AUC = 0.78) and attention‐based MIL (AUC = 0.70). Its top prognostic models achieved an average C‐index of 0.739, effectively stratifying high‐ and low‐risk patients. Key predictors included stage, calcification, sarcomatoid differentiation, and vascular networks. GenAI, particularly Claude‐3.5‐Sonnet, enhances accuracy and consistency in ccRCC pathology, showing strong potential for clinical use, especially in resource‐limited settings.
Source: Interdisciplinary medicine
DOI: 10.1002/inmd.70047
Authors: Renyi Lu, Junyi Shen, Aimin Jiang, Wenjin Chen, Chang Qi, Li Chen, Lingxuan Zhu, Weiming Mou, Wenyi Gan, Dongqiang Zeng, Bufu Tang, Mingjia Xiao, Guangdi Chu, Shengkun Peng, Hank Z. H. Wong, Lin Zhang, Hengguo Zhang, Xinpei Deng, Quan Cheng, Xingang Cui, Anqi Lin, Peng Luo
Institutions: Zhujiang Hospital, Kangda College of Nanjing Medical University, Southern Medical University, Second Military Medical University, Changhai Hospital, Shanghai Jiao Tong University, XinHua Hospital, TU Wien, University of Macau, Shanghai First People's Hospital, Jinan University, Zhuhai People's Hospital, Nanfang Hospital, South China University of Technology, Fudan University, Zhongshan Hospital, Wenzhou Medical University, Affiliated Hospital of Qingdao University, University of Electronic Science and Technology of China, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Hong Kong, Monash University, Anhui Medical University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Central South University, Xiangya Hospital Central South University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7083701782