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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.
Background:
Double expression lymphoma (DEL) is a subtype of primary central nervous system lymphoma (PCNSL) that often has a poor prognosis. Currently, there are limited noninvasive ways to detect protein expression.
Purpose:
To detect DEL in PCNSL using multiparametric MRI-based machine learning.
Study Type:
Retrospective.
Population:
Forty PCNSL patients were enrolled in the study among whom 17 were DEL (9 males and 8 females, 61.29 ± 14.14 years) and 23 were non-DEL (14 males and 9 females, 55.57 ± 14.16 years) with 59 lesions (28 DEL and 31 non-DEL).
Field Strength/Sequence:
ADC map derived from DWI (b = 0/1000 s/mm2 ), fast spin echo T2WI, T2FLAIR, and contrast-enhanced T1 weighted imaging (T1CE) were collected at 3.0 T.
Assessment:
Two raters manually segmented lesions by ITK-SNAP on ADC, T2WI, T2FLAIR and T1CE. A total of 2234 radiomics features from the tumor segmentation area were extracted. The t-test was conducted to filter the features, and elastic net regression algorithm combined with recursive feature elimination was used to calculate the essential features. Finally, 12 groups with combinations of different sequences were fitted to 6 classifiers, and the optimal models were selected.
Statistical Tests:
Continuous variables were assessed by the t-test, while categorical variables were assessed by the non-parametric test. Interclass correlation coefficient tested variables' consistency. Sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) were used to evaluate model performance.
Results:
DEL status could be identified to varying degrees with 72 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Both SVMlinear and logistic regression (LR) combined with four sequence group had similar largest AUCmean (0.92 ± 0.09 vs. 0.92 ± 0.05), and SVMlinear was considered as the optimal model in this study since the F1-score of SVMlinear (0.88) was higher than that of LR (0.83).
Data Conclusion:
Multiparametric MRI-based machine learning is promising in DEL detection.
Evidence Level:
4 TECHNICAL EFFICACY STAGE: 2.
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