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Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From

Wei Xia1,2,3, Bin Hu3, Haiqing Li3

  • 1Academy for Engineering and Technology, Fudan University, Shanghai, China.

Journal of Magnetic Resonance Imaging : JMRI
|September 1, 2020
PubMed
Summary

Radiomics models using multiparametric-MRI can generalize for differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM). Integrating these models with radiologist diagnoses improved accuracy over radiologists alone.

Keywords:
GBMMP-MRIPCNSLmachine learningradiomics

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Area of Science:

  • Neuroimaging
  • Radiomics
  • Oncology

Background:

  • Accurate preoperative differentiation between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial for guiding neurosurgical interventions.
  • Distinguishing these entities preoperatively can be challenging due to overlapping imaging features.

Purpose of the Study:

  • To validate the generalizability of radiomics models developed using multiparametric-MRI (MP-MRI) for differentiating PCNSL from GBM across different vendors.
  • To assess the performance of integrated models combining radiomics features and expert radiologist interpretations.

Main Methods:

  • A retrospective analysis of 240 patients (129 GBM, 111 PCNSL) using 3.0T MP-MRI (including DWI and CE-T1 WI) was performed.
  • Radiomics models were developed and validated using cross-vendor and mixed-vendor approaches.
  • Integrated models combined radiomics predictions with diagnoses from radiologists with varying experience levels.

Main Results:

  • The best-performing radiomics model utilized contrast-enhanced T1-weighted imaging (CE-T1 WI) and apparent diffusion coefficients (ADCs), achieving an AUC of 0.943.
  • Integrated models demonstrated superior performance compared to radiologists alone, with significantly higher AUCs across all experience levels.
  • For radiologists with 5 years of experience, integrated models achieved AUCs of 0.975 and 0.995, compared to 0.891 and 0.885 respectively.

Conclusions:

  • Radiomics models derived from MP-MRI show generalizable performance in distinguishing PCNSL from GBM.
  • Combining MP-MRI radiomics with radiologist expertise significantly enhances diagnostic accuracy, offering a valuable tool for preoperative decision-making.