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 algorithms were used for dimensionality reduction and selection of radiomics features. Finally, a logistic regression machine learning model was developed to predict the interim efficacy of FL using a five-fold cross-validation strategy. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve, accuracy, and the Delong test to compare AUC differences.

Result: Among the 97 patients, 42 (43.30%) achieved complete response (CR) for interim efficacy, while 55 (56.70%) had non-complete response (non-CR). A total of 2264 radiomics features were extracted from the images. Seven clinical risk factors and ten radiomics features associated with interim efficacy were selected to construct the clinical, radiomics, and radiomics-clinical combined models. Among the three logistic regression machine learning models developed, the radiomics-clinical combined model demonstrated the best performance, achieving a mean AUC of 0.849 (95% CI, 0.676-1.000) and an accuracy of 0.795, outperforming the other two models.

Conclusion: Our preliminary results demonstrate that a radiomics-clinical combined model, based on baseline [¹⁸F]FDG PET/CT radiomics features and clinical risk factors, may contribute to predicting interim efficacy in FL patients.