Optimal peritumoral regions and fusion strategies for prediction of the double-expressor subtype in diffuse large B-cell lymphoma: a multi-region radiomics study.
Authors
Affiliations (4)
Affiliations (4)
- Department of Nuclear Medicine, General Hospital of Northern Theatre Command, Shenyang, China.
- Training Base for Graduate, General Hospital of Northern Theater Command, Dalian Medical University, Shenyang, China.
- Training Base for Graduate, General Hospital of Northern Theater Command, China Medical University, Shenyang, China.
- School of Software, Shenyang University of Technology, Shenyang, Liaoning, China.
Abstract
To develop a PET/CT-based radiomics model for noninvasive prediction of the double-expressor lymphoma (DEL) subtype in diffuse large B-cell lymphoma (DLBCL). From January 2019 to July 2025 consecutively enrolled, 143 patients with DLBCL (55 DEL, 88 non-DEL) were randomly divided into a training set and an internal validation set in a 7:3 ratio. Radiomic features were extracted from peritumoral regions with different expansion distances (3, 5, and 10 mm) to identify the optimal peritumoral region. These features were then integrated using various fusion strategies (multi-region fusion, image fusion, and feature fusion) for combined intratumoral and peritumoral analysis. A transfer learning model built on a pretrained ResNet-50 was combined with a clinical model incorporating baseline PET features to develop three integrated models (Clinic+Rad+DL, Stacking, Ensemble). Model performance was evaluated to select the best-performing approach, and SHAP analysis was applied to enhance interpretability. The Rad_MLP model achieved the best performance by integrating intratumoral and 10-mm peritumoral features, with an accuracy of 85.3%, a sensitivity of 89.5%, and a specificity of 79.2%. The positive predictive value (PPV) and negative predictive value (NPV) were 0.773 and 0.905, respectively, with an F1 score of 0.829. Rad_MLP, by integrating the most predictive intratumoral and peritumoral features, substantially improves the accuracy of noninvasive prediction of the DEL subtype in DLBCL.