Background. To construct and validate a deep learning cluster from whole slide images (WSI) for depicting the immunophenotypes and functional heterogeneity of the tumor microenvironment (TME) in patients with bladder cancer (BLCA) and to explore an artificial intelligence (AI) score to explore the underlying biological pathways in the developed WSI cluster. Methods. In this study, the WSI cluster was constructed based on a deep learning procedure. Further rerecognition of TME features in pathological images was applied based on a neural network. Then, we integrated the TCGA cohort and several external testing cohorts to explore and validate this novel WSI cluster and a corresponding quantitative indicator, the AI score. Finally, correlations between the AI cluster (AI score) and classical BLCA molecular subtypes, immunophenotypes, functional heterogeneity, and potential therapeutic method in BLCA were assessed. Results. The WSI cluster was identified associated with clinical survival ( P < 0.001 ) and was proved as an independent predictor ( P = 0.031 ), which could also predict the immunology and the clinical significance of BLCA. Rerecognition of pathological images established a robust 3-year survival prediction model (with an average classification accuracy of 86%, AUC of 0.95) for BLCA patients combining TME features and clinical features. In addition, an AI score was constructed to quantify the underlying logic of the WSI cluster ( AUC = 0.838 ). Finally, we hypothesized that high AI score shapes an immune-hot TME in BLCA. Thus, treatment options including immune checkpoint blockade (ICB), chemotherapy, and ERBB therapy can be used for the treatment of BLCA patients in WSI cluster1 (high AI score subtype). Conclusions. In general, we showed that deep learning can predict prognosis and may aid in the precision medicine for BLCA directly from H&E histology, which is more economical and efficient.
Although renal pelvic and ureteral urothelial carcinoma share similarities in their origins, disparities on a genetic and clinical level make them divergent entities. Clinical information from the Surveillance, Epidemiology, and End Results (SEER) database was used to validate the characteristics and molecular subtypes using single‐center data, which were compared between the two types of muscle‐invasive tumors. Simultaneously, to expand the sample size for further verification, we explored a deep learning algorithm to correctly classify molecular subtypes from H&E histology slides. We suggested that the renal pelvic group might have a proclivity towards luminal and the ureter towards basal and P53‐like. Furthermore, we explore the heterogeneity of matrix and immune tumor microenvironment, and the ureteral group had more immune cell infiltration and higher stiffness. Collectively, these results showed that muscle‐invasive upper tract urothelial carcinoma exist in distinct properties of clinical characteristics, molecular subtype, and tumor microenvironment.
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