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Open Access 24-01-2025 | Lymphoma | Diagnostic Neuroradiology

Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning

Authors: Mingxiao Wang, Guoli Liu, Nan Zhang, Yanhua Li, Shuo Sun, Yahong Tan, Lin Ma

Published in: Neuroradiology

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Abstract

Purpose

In primary central nervous system lymphoma (PCNSL), B-cell lymphoma-6 (BCL-6) is an unfavorable prognostic biomarker. We aim to non-invasively detect BCL-6 overexpression in PCNSL patients using multiparametric MRI and machine learning techniques.

Methods

65 patients (101 lesions) with primary central nervous system lymphoma (PCNSL) diagnosed from January 2013 to July 2023, and all patients were randomly divided into a training set and a validation set according to a ratio of 8 to 2. ADC map derived from DWI (b = 0/1000 s/mm2), fast spin echo T2WI, T2FLAIR, were collected at 3.0 T. A total of 2234 radiomics features from the tumor segmentation area were extracted and LASSO were used to select features. Logistic regression (LR), Naive bayes (NB), Support vector machine (SVM), K-nearest Neighbor, (KNN) and Multilayer Perceptron (MLP), were used for machine learning, and sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) was used to evaluate the detection performance of five classifiers, 6 groups with combinations of different sequences were fitted to 5 classifiers, and optimal classifier was obtained.

Results

BCL-6 status could be identified to varying degrees with 30 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Support vector machine (SVM) combined with three sequence group had the largest AUC (0.95) in training set and satisfactory AUC (0.87) in validation set.

Conclusion

Multiparametric MRI based machine learning is promising in detecting BCL-6 overexpression.
Appendix
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Literature
38.
go back to reference Xia W, Hu B, Li H et Al(2021)Multiparametric-MRI-based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and cross-vendor validation. J Magn Reson Imaging 53(1):242–250. https://doi.org/10.1002/jmri.27344 Xia W, Hu B, Li H et Al(2021)Multiparametric-MRI-based radiomics model for differentiating primary central nervous system lymphoma from glioblastoma: development and cross-vendor validation. J Magn Reson Imaging 53(1):242–250. https://​doi.​org/​10.​1002/​jmri.​27344
Metadata
Title
Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning
Authors
Mingxiao Wang
Guoli Liu
Nan Zhang
Yanhua Li
Shuo Sun
Yahong Tan
Lin Ma
Publication date
24-01-2025
Publisher
Springer Berlin Heidelberg
Keyword
Lymphoma
Published in
Neuroradiology
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI
https://doi.org/10.1007/s00234-025-03551-y

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