Baseline [¹⁸F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP.

ABSTRACT:

Objective

To investigate the predictive value of machine learning-based PET/CT radiomics and clinical risk factors in predicting interim efficacy in patients with follicular lymphoma (FL).

Methods

This study retrospectively analyzed data from 97 patients with FL diagnosed via histopathological examination between July 2012 and November 2023. Lesion segmentation was performed using LIFEx software, and radiomics features were extracted through the uAI Research Portal (uRP) platform, including first-order features, shape features, and texture features. Fourteen filters were applied to the raw images to extract higher-order features from the derived images. Univariate analysis was employed to identify clinical risk factors, and correlation coefficients, MRMR, and LASSO algorithm

SUBMITTER: Wen Z 

PROVIDER: S-EPMC11756111 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature