Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer.

ABSTRACT:

Objective

This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation.

Methods

In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative feat

SUBMITTER: Wei Z 

PROVIDER: S-EPMC10831750 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature