Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer.

ABSTRACT:

Background

Clinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.

Methods

A total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC)

SUBMITTER: Zhang G 

PROVIDER: S-EPMC8226179 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature