Multi-parametric MRI radiomics analysis with ensemble learning for prostate lesion classification

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.