Prediction of double expression status of primary CNS lymphoma using multiparametric MRI radiomics combined with habitat radiomics: a double-center study.

Abstract

Rationale and objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.

Materials and methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected. Radiomics, habitat radiomics, and combined models were constructed using machine learning classifiers, including KNN, DT, LR, and SVM. The AUC in the test set was used to evaluate the optimal predictive model. DCA curve and calibration curve were employed to evaluate the predictive performance of the models. SHAP analysis was utilized to visualize the contribution of each feature in the optimal model.

Results: For the radiomics-based models, the Combined radiomics model constructed by LR demonstrated better performance, with the AUC of 0.8779 (95% CI: 0.8171-0.9386) in the training set and 0.7166 (95% CI: 0.497-0.9361) in the test set. The Habitat radiomics model (SVM) based on T1-CE showed an AUC of 0.7446 (95% CI: 0.6503- 0.8388) in the training set and 0.7433 (95% CI: 0.5322-0.9545) in the test set. Finally, the Combined all model exhibited the highest predictive performance: LR achieved AUC values of 0.8962 (95% CI: 0.8299-0.9625) and 0.8289 (95% CI: 0.6785-0.9793) in training and test sets, respectively.

Conclusion: The Combined all model developed in this study can provide effective reference value in predicting the DEL status of PCNSL, and habitat radiomics significantly enhances the predictive efficacy.