Diagnostic accuracy of deep learning in detection and prognostication of renal cell carcinoma: a systematic review and meta-analysis

Abstract

Introduction: The prevalence of Renal cell carcinoma (RCC) is increasing among adults. Histopathologic samples obtained after surgical resection or from biopsies of a renal mass require subtype classification for diagnosis, prognosis, and to determine surveillance. Deep learning in artificial intelligence (AI) and pathomics are rapidly advancing, leading to numerous applications such as histopathological diagnosis. In our meta-analysis, we assessed the pooled diagnostic performances of deep neural network (DNN) frameworks in detecting RCC subtypes and to predicting survival. Methods: statistic. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to conduct and report the analysis. Results: = 94.4%). Discussion: The DNN showed excellent pooled diagnostic accuracy rates to classify RCC into subtypes and grade them for prognostic purposes. Further studies are required to establish generalizability and validate these findings on a larger scale.

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