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Updated: Jul 15, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Preoperative phenotypic stratification of primary central nervous system lymphoma using multiparametric MRI-based
Lingxu Chen1,2, Xiaochen Wang1,2, Sihui Wang1,2
1Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background:
Primary central nervous system lymphoma (PCNSL) exhibits substantial biological heterogeneity, particularly across immunohistochemical subtypes such as double-expression lymphoma (DEL) and germinal center B-cell-like (GCB) phenotypes. This study aimed to develop and evaluate multiparametric MRI-based radiomics models for preoperative prediction of DEL andGCB status in PCNSL.
Methods:
We retrospectively included 160 pathologically confirmed PCNSL patients. Multiparametric MRI sequences, including T2-weighted (T2WI), T2-Fluid-Attenuated Inversion Recovery (FLAIR), contrast-enhanced T1-weighted (T1CE), and apparent diffusion coefficient (ADC), were analyzed. Enhancing tumor core and peritumoral edema were automatically segmented using nnU-NetV2-based models, and radiomics features were extracted from both regions across all sequences. After reproducibility filtering, ComBat harmonization, and multistep feature selection performed exclusively within the training cohort, six machine-learning classifiers were trained and evaluated in held-out internal test sets. Model performance was assessed using ROC and decision curve analysis, and SHAP-based feature interpretation.
Results:
For DEL classification, 12 radiomic features were retained. The SVM classifier achieved the best test performance, with an area under the ROC curve (AUC) of 0.807 (95% CI, 0.649-0.936), accuracy of 0.730, sensitivity of 0.714, and specificity of 0.750. For GCB/non-GCB classification, seven radiomic features were used for model construction. The Random Forest classifier achieved the highest test performance, with an AUC of 0.897 (95% CI, 0.796-0.973), accuracy of 0.846, sensitivity of 0.792, and specificity of 0.893.
Conclusions:
Multiparametric MRI-based radiomics analysis demonstrated promising performance for noninvasive prediction of DEL and GCB/non-GCB phenotypes in PCNSL. These findings suggest that MRI-derived radiomic features may capture imaging correlates of biological heterogeneity and may support preoperative risk stratification and individualized treatment planning.
