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Prediction of Double-Expressor Lymphoma in Diffuse Large B-Cell Lymphoma Using a Non-invasive Model Based on [18F]
Jingjing Liang1, Guoxiu Lu2, Qiwen Zhong3
1Department of Nuclear Medicine, General Hospital of Northern Theatre Command, Shenyang, China (J.L., G.L., Q.Z., Q.P., G.Z.); Department of Nuclear Medicine, Chengdu Wenjiang District People's Hospital, Chengdu, China (J.L.); Training Base for Graduate, General Hospital of Northern Theater Command, China Medical University, Shenyang, China (J.L., Q.P.).
Rationale And Objectives:
To develop and validate an interpretable multimodal model integrating deep learning radiomics with clinical data using baseline [18F] FDG PET/CT for non-invasive identification of the double-expressor lymphoma (DEL) in diffuse large B-cell lymphoma (DLBCL).
Materials And Methods:
The histopathological reference standard for DLBCL diagnosis and DEL subtyping was immunohistochemistry according to the 2016 WHO. Patients from January 2019 to July 2025 were retrospectively enrolled and randomly divided into training and test sets at 7:3, and a patient-level analysis was performed. Clinical, radiomics and deep learning features were extracted. After LASSO selection, individual and fusion models were constructed. Statistical analyses were performed using Python (3.6), and p-value < 0.05 was considered significant. Model performance was assessed via receiver operating characteristic (ROC), calibration and decision curve analysis (DCA). SHAP and a nomogram were used for interpretation.
Results:
A total of 143 patients (82 males, 61 females, median age 61.5 years) were enrolled. ViT_KNN model achieved a test AUC of 0.936 (95%Cl 0.856-1.000), outperforming clinical (AUC 0.857, 95%Cl 0.722-0.993) and radiomics (AUC 0.706, 95%Cl 0.547-0.865) models. Late fusion, especially Stacking_LR, achieved the best performance (AUC 0.948, 95%Cl 0.865-1.000, accuracy 95.3%, specificity 100%, sensitivity 88.2%). Calibration and DCA confirmed clinical net benefit. SHAP analysis highlighted deep learning features as most influential. The Stacking_LR nomogram offers an intuitive clinical tool.
Conclusion:
Deep learning radiomics based on baseline [18F] FDG PET/CT enables effective non-invasive identification of DEL. The stacking ensemble strategy enhances multimodal fusion and provides a highly accurate, interpretable tool for DLBCL risk stratification.
