Predicting Ki-67 expression levels in non-small cell lung cancer using an explainable CT-based deep learning radiomics model.

ABSTRACT:

Objective

To predict Ki-67 expression levels in non-small cell lung cancer (NSCLC) using an interpretable model combining clinical-radiological, radiomic, and deep learning features.

Methods

This retrospective study included 259 NSCLC patients from Center 1 (training/validation sets) and 112 from Center 2 (independent test set). Patients were grouped by a 40% Ki-67 cutoff. Radiomic features and deep learning features were extracted from CT images, where the deep learning features were obtained via a deep residual network (ResNet18). The least absolute shrinkage and selection operator (LASSO) was used to select optimal features and compute radiomics (rad-score) and deep learning (deep-score) scores. Univariate and multivariate logistic regression were used to identify indepen

SUBMITTER: Qin S 

PROVIDER: S-EPMC12727595 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature