Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study.

ABSTRACT:

Background

Accurate stratification of recurrence risk for bladder cancer (BCa) is essential for precise individualized therapy. This study aimed to develop and validate a model for predicting the risk of recurrence in BCa patients postoperatively using 3-phase enhanced CT images.

Methods

We retrospectively enrolled 874 BCa patients across four centers between January 2006 and December 2021. Patients from one center were used as training set, while the remaining patients went into the validation set. We trained a deep learning (DL) model based on convolutional neural networks using 3-phase enhanced CT images. The resulting prediction scores were entered into Cox regression analysis to obtain DL scores and construct a DL signature. DL scores and clinical features were then use

SUBMITTER: Wang H 

PROVIDER: S-EPMC10716002 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature