Radiomic prediction for durable response to high-dose methotrexate-based chemotherapy in primary central nervous system lymphoma.

Background: The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic-based models to predict the durable response (DR) to high-dose methotrexate (HD-MTX)-based chemotherapy in PCNSL patients.

Methods: A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD-MTX-based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast-enhanced MR images. Multiple machine-learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic-clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility.

Results: A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models.

Conclusions: This radiomic signature and integrated models have the potential to accurately predict the DR to HD-MTX-based chemotherapy in PCNSL patients, providing valuable therapeutic insights.