Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods
- 28.03.2020
- Magnetic Resonance
Erschienen in:
- Verfasst von
- Xinhui Wang
- Qi Wan
- Houjin Chen
- Yanfeng Li
- Xinchun Li
- Erschienen in
- European Radiology | Ausgabe 8/2020
Abstract
Objectives
We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods.
Materials and methods
This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results.
Results
For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set.
Conclusion
Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods.
Key Points
• Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions.
• Radiomics model based on multiparametric MRI has better performance than single-sequence models.
• The machine learning methods RFE with SVM perform best in the current cohort.
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- Titel
- Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods
- Verfasst von
-
Xinhui Wang
Qi Wan
Houjin Chen
Yanfeng Li
Xinchun Li
- Publikationsdatum
- 28.03.2020
- Verlag
- Springer Berlin Heidelberg
- Erschienen in
-
European Radiology / Ausgabe 8/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084 - DOI
- https://doi.org/10.1007/s00330-020-06768-y
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