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

A systematic search was done in PubMed, Google Scholar, Embase, and Scopus from inception to November 2023. The random effects model was used to calculate the pooled percentages, mean, and 95% con

SUBMITTER: Chandramohan D 

PROVIDER: S-EPMC11412207 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature