Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.

ABSTRACT: Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were use

SUBMITTER: Cho HH 

PROVIDER: S-EPMC8590002 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature