Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images

Abstract

Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.

Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC).

Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%.

Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs.

Advances in knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

Keywords: Gleason grading; machine learning; multiparametric magnetic resonance imaging; prostate cancer; radiomics.

Figure 1

Flow diagram of our radiomics-based machine learning framework. T2W1 – T2-weighted; ADC – Apparent diffusion coefficient; AUC – Area under curve; GLCM – Gray-level co-occurrence matrix; GLDM – Gray-level dependence matrix; NGTDM – Neighboring gray-tone difference matrix; GLRLM – Gray-level run-length matrix; GLSZM – Gray-level size-zone matrix; SMOTE – Synthetic Minority Over-Sampling Technique; mRMR – Minimum redundancy maximum relevance; RFE – Recursive feature elimination

Figure 2

An example segmentation of prostate lesion in a representative patient. T2W – T2-weighted; ADC – Apparent diffusion coefficient

Figure 3

Confusion matrix for best-performing model

Figure 4

Receiver operating characteristic curve for best-performing model. ROC – Receiver operating characteristic; AUC – Area under curve