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Detecting Double Expression Status in Primary Central Nervous System Lymphoma Using Multiparametric MRI Based Machine

Guoli Liu1,2, Xinyue Zhang1,2, Nan Zhang1,2

  • 1Medical School of Chinese People's Liberation Army (PLA), Beijing, China.

Journal of Magnetic Resonance Imaging : JMRI
|May 18, 2023
PubMed
Summary

Machine learning using multiparametric MRI can detect double expression lymphoma (DEL), a subtype of primary central nervous system lymphoma (PCNSL). This noninvasive approach shows promise for identifying DEL in patients with PCNSL.

Keywords:
BCL-2MYClymphomamachine learningmagnetic resonance imaging

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Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Double expression lymphoma (DEL) is a challenging subtype of primary central nervous system lymphoma (PCNSL) often associated with poor prognosis.
  • Current diagnostic methods for DEL lack noninvasive protein expression detection capabilities.

Purpose of the Study:

  • To develop and evaluate a multiparametric MRI-based machine learning model for detecting DEL in PCNSL patients.

Main Methods:

  • A retrospective study analyzed 40 PCNSL patients (17 DEL, 23 non-DEL) using 3.0T MRI including DWI, T2WI, T2FLAIR, and T1CE sequences.
  • Radiomics features were extracted from segmented lesions, and machine learning algorithms (including elastic net regression and SVMlinear) were employed to identify DEL.
  • Model performance was assessed using sensitivity, specificity, accuracy, F1-score, and AUC, with feature selection via t-tests and recursive feature elimination.

Main Results:

  • The study developed 72 radiomics-based models for DEL detection, demonstrating varying degrees of success.
  • Combining multiple MRI sequences and classifiers improved model performance.
  • The optimal model, SVMlinear, achieved a high mean AUC of 0.92 ± 0.05 and an F1-score of 0.88, outperforming logistic regression.

Conclusions:

  • Multiparametric MRI combined with machine learning offers a promising noninvasive strategy for detecting double expression lymphoma in PCNSL.
  • The developed radiomics models, particularly SVMlinear, show significant potential for improving DEL diagnosis and patient management.