Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study.

ABSTRACT:

Background

Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features' value in predicting the TMB status of BLCA patients.

Methods

Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features.

Results

The unsupervised clustering analysis div

SUBMITTER: Tang X 

PROVIDER: S-EPMC8285848 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature