MRI-based radiomics virtual biopsy for BCL6 in primary central nervous system lymphoma

Abstract

Aim: To establish a machine learning model based on a radiomic signature for predicting B-cell lymphoma 6 (BCL-6) rearrangement in primary central nervous system lymphoma (PCNSL).

Materials and methods: Retrospective study on 102 PCNSL patients (31 with BCL-6 rearrangement positive, 71 with BCL-6 rearrangement negative) were randomly divided into the training and validation sets at a ratio of 7:3. Radiomics models based on contrast-enhanced T1-weighted imaging (CE-T1WI) and fluid-attenuated inversion recovery (FLAIR) in different regions, including VOI_{tumour core} and VOI_{peritumoural oedema.} Radiomics features were extracted and selected using LASSO regression, and radiomics score (rad-score) were calculated using the weighted coefficients. Four machine learning models (logistic regression, random forest, support vector machine, K-nearest neighbours) were developed and evaluated based on rad-score. The optimal radiomics model was integrated into the clinical or radiological factors to construct a predictive model through logistic regression analysis. A nomogram was constructed based on independent significant features for individualised prediction.

Results: All rad-scores based on CE-T1WI and FLAIR sequences were significantly associated with BCL6 rearrangement (p < 0.05) in univariate regression analysis. The logistic regression machine learning model performed best with AUCs of 0.935 (training) and 0.923 (validation). Rad-scores from CE-T1WI tumour core and peritumoural oedema were independent significant predictors.

Conclusion: Radiomics signatures based on CE-T1WI and FLAIR sequences have significant value in distinguishing BCL6 rearrangement. The CE-T1WI radiomics model based on VOI_{tumour core} and VOI_{peritumoural oedema} are robust markers for identifying BCL6 rearrangement.