Radiomics based on ¹⁸F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer.

ABSTRACT:

Purpose

This study aimed to establish and evaluate the value of integrated models involving ¹⁸F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC).

Methods

A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological f

SUBMITTER: Liu J 

PROVIDER: S-EPMC11310012 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature