Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer.

ABSTRACT:

Purpose

Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC.

Methods

A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top s

SUBMITTER: Li B 

PROVIDER: S-EPMC10831499 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature