Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method

Jingren Niu; Qing Tan; Xiufen Zou; Suoqin Jin; Jingren Niu; Qing Tan; Xiufen Zou; Suoqin Jin

doi:10.3934/mbe.2023136

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 2890-2907. doi: 10.3934/mbe.2023136

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Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method

1.
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
2.
Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, China

Academic Editor: Shangce Gao

Received: 16 September 2022 Revised: 10 November 2022 Accepted: 13 November 2022 Published: 01 December 2022

Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.
- radiomics,
- medical image analysis,
- glioma grades,
- biomarkers,
- multi-filters,
- multi-objective optimization
Citation: Jingren Niu, Qing Tan, Xiufen Zou, Suoqin Jin. Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2890-2907. doi: 10.3934/mbe.2023136

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Abstract

Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.

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