Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognostic biomarkers for ccRCC. Methods: We proposed a weakly-supervised deep learning strategy using conventional histology of 1752 whole slide images from multiple centers. Our study was demonstrated through internal cross-validation and external validations for the deep learning-based models. Results: Automatic diagnosis for ccRCC through intelligent subtyping of renal cell carcinoma was proved in this study. Our graderisk achieved aera the curve (AUC) of 0.840 (95% confidence interval: 0.805-0.871) in the TCGA cohort, 0.840 (0.805-0.871) in the General cohort, and 0.840 (0.805-0.871) in the CPTAC cohort for the recognition of high-grade tumor. The OSrisk for the prediction of 5-year survival status achieved AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Cox regression analysis indicated that graderisk, OSrisk, tumor grade, and tumor stage were found to be independent prognostic factors, which were further incorporated into the competing-risk nomogram (CRN). Kaplan-Meier survival analyses further illustrated that our CRN could significantly distinguish patients with high survival risk, with hazard ratio of 5.664 (3.893-8.239, p < 0.0001) in the TCGA cohort, 35.740 (5.889-216.900, p < 0.0001) in the General cohort and 6.107 (1.815 to 20.540, p < 0.0001) in the CPTAC cohort. Comparison analyses conformed that our CRN outperformed current prognosis indicators in the prediction of survival status, with higher concordance index for clinical prognosis.
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death. Biopsy of gastric mucosal is a standard procedure in gastric cancer screening test. However, manual pathological inspection is labor-intensive and time-consuming. Besides, it is challenging for an automated algorithm to locate the small lesion regions in the gigapixel whole-slide image and make the decision correctly. To tackle these issues, we collected large-scale whole-slide image dataset with detailed lesion region annotation and designed a whole-slide image analyzing framework consisting of 3 networks which could not only determine the screen result but also present the suspicious areas to the pathologist for reference. Experiments demonstrated that our proposed framework achieves sensitivity of 97.05% and specificity of 92.72% in screening task and Dice coefficient of 0.8331 in segmentation task. Furthermore, we tested our best model in real-world scenario on 10, 316 whole-slide images collected from 4 medical centers.