CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

ABSTRACT:

Background

To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.

Methods

We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference

SUBMITTER: Song H 

PROVIDER: S-EPMC10507832 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature