Artificial intelligence-driven quantitative histologic grading and prognostic stratification for clear cell renal cell carcinoma

Accurate WHO/ISUP grading of clear cell renal cell carcinoma (ccRCC) is clinically important but remains time-consuming and subjective in routine practice. Quantitative pathological assessment on whole-slide images (WSIs) is difficult to perform manually. We developed an artificial intelligence (AI) system for automated WHO/ISUP grading and quantitative pathological assessment of ccRCC, aiming to establish a quantitative grading system to enhance prognostic stratification. In this retrospective multicenter study, we analyzed H&E WSIs from 1192 patients from Sun Yat-Sen Memorial Hospital (SYSMH, n = 467), the Third affiliated hospital of Sun Yat-Sen University (SYSTH, n = 241), and The Cancer Genome Atlas (TCGA, n = 484). The AI pipeline performed tumor segmentation, segmentation of high-grade dedifferentiation (sarcomatoid and rhabdoid), and quantification of G1-G3 nuclei, generating slide-level WHO/ISUP grades and quantitative pathological features, including G1-G3 proportions and G4 tumor area ratio. Based on these quantitative features, we merged patient groups with similar recurrence-free survival (RFS) to develop a quantitative grading system. The quantitative prognostic grading system was developed in the pooled SYSMH–SYSTH cohort and independently validated in the TCGA cohort. In the validation sets, the AI-assisted WHO/ISUP grading model showed strong discriminatory performance for the 4-class grading task, with micro-AUCs (95% CIs) of 0.912 (0.892–0.932) in the SYSTH cohort and 0.846 (0.825–0.866) in the TCGA cohort. Quantitative pathological analysis showed that tumors with a high G2 proportion (> 46%) had RFS comparable to that of G3 tumors, whereas G4 tumors could be further divided into two prognostic groups according to the G4 tumor area ratio and high-grade dedifferentiation. Therefore, the quantitative grading system consists of four classifications: G1 + low G2 proportion group as Class 1; High G2 proportion group + G3 as Class 2; Low G4 risk group as Class 3; High G4 risk group as Class 4. The quantitative grading system showed a modest increase in discrimination over the WHO/ISUP grading system. Our AI system facilitates automated WHO/ISUP grading and quantitative pathological assessment of ccRCC on WSIs. The quantitative grading system may complement conventional WHO/ISUP grading for prognostic assessment in ccRCC.