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10 April 2023 Multi-parametric MRI radiomics analysis with ensemble learning for prostate lesion classification
Yuheng Li, Jing Wang, Chih-Wei Chang, Pretsh Patel, Ashesh Jani, Hui Mao, Tian Liu, Xiaofeng Yang
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Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 124680B (2023) https://doi.org/10.1117/12.2653637
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
Abstract
Non-invasive accurate prostate cancer risk assessment is crucial in radiation treatment planning that impacts patients' quality of life. In this study, we aim to develop a radiomics model using ensemble learning with multi-parametric magnetic resonance imaging (mpMRI) to classify low-grade vs high-grade prostate lesions. We identified 112 prostate patients with biopsy findings and sampled 70% and 30% of the data as training and testing datasets. There were 1198 Radiomics features extracted from mpMRI. A combination of filter-based, wrapper-based and embedded methods was used for feature selection. Ensemble classifiers included multiple machine learning models, such as random forest, k-nearest neighbor and support vector machine, for each MRI modality. A soft voting ensemble classifier was used to achieve the final performance in the test set with 82% accuracy and 0.88 AUC.
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Yuheng Li, Jing Wang, Chih-Wei Chang, Pretsh Patel, Ashesh Jani, Hui Mao, Tian Liu, and Xiaofeng Yang "Multi-parametric MRI radiomics analysis with ensemble learning for prostate lesion classification", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 124680B (10 April 2023); https://doi.org/10.1117/12.2653637
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KEYWORDS
Magnetic resonance imaging
Radiomics
Machine learning
Prostate
Feature selection
Feature extraction
Random forests
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