Detecting BCL-6 overexpression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning

Motivation: Based on BCL-6 status，the prognosis of PCNSL can be detected，then the treatment can be adjusted. Goal(s): Detecting BCL-6 Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine Learning. Approach: Using Python code to retrieve Pyromics for radiomics feature screening from T2、T2 Flair、ADC.The AUC value was used to evaluate the detection performance of the image sequence joint classifier, Obtain the best classifier. Results: The multi parameter sequence combined with SVM machine learning has the highest AUC, with BCL-6 overexpression detected in the training and validation sets of 0.945 and 0.865,sensitivity of 98% and 92.7%, specificity of 83.9% and 87.5%. Impact: Based on BCL-6 status, the patients are divided into "good risk" and "poor risk".patients who have a “poor risk”phenotype may be candidates for aggressive initial therapy with chemotherapy and radiaion.It may be desirable to defer WBRTto avoid the radiation-induced neurotoxicity.