Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study.

ABSTRACT:

Objective

To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients.

Methods

We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we

SUBMITTER: Sun R 

PROVIDER: S-EPMC10811316 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature