Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

Abstract

Objectives To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa). Methods This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3-5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets. Results The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980-0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920-0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution. Conclusions The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa. Key Points • T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance. • Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest. • The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

“…Monti et al [24] found that for PCa detection, standard radiomics models using T2w and ADC images performed better than an advanced model with additional diffusion kurtosis imaging and DCE. Approaches extracting radiomic features from prostate mpMRI using auto-fixed VOIs have also achieved promising results on peripheral zone csPCa detection, with the highest AUC of 0.87 for the XGBoost classifier [19]. On the other hand, Bonekamp et al [20] showed that for PCa detection, mADC alone achieved good results, comparable to clinical interpretation, and found no further benefit with complex radiomics and machine learning methods, which is in line with our study results.…”

“…In the small lesion subgroup, PI-RADS was non-inferior to our model, which suggests that radiologist reassessment could measurably improve the predictions. Previous studies commonly excluded lesions with volumes of less than 0.5 ml from their analysis [ 19 , 26 , 29 , 30 , 31 , 32 ] or provided no information about the distribution of lesion volumes [ 20 , 33 ].…”

“…Analyzed image features include shape features, first-order statistics, and texture characteristics, such as grey level co-occurrence matrices (GLCM) [ 16 ]. Recent advances in image analysis techniques using radiomic features to assess genomic, proteomic, and clinical phenotypes of the prostate tumor were shown to match or even surpass the qualitative imaging assessment of radiologists [ 17 , 18 , 19 ].…”

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

“…The classification method has a strong impact on the variation in performance [19]. Yet, the question has not been addressed to what extend specific feature and prediction model effect the diagnostic performance to differentiate GrG ≤ 2 against GrG ≥ 3 [3,[22][23][24][25][26]. The purpose of this study was to evaluate the application of the clinical assessment categories PI-RADS and ADCderived radiomic features to build and compare three prediction models and to analyze their influence on the differentiation of clinically significant PCa.…”

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

“…Several studies have utilized a standard pipeline for radiomic analysis, including the following main steps: image acquisition, segmentation (or labeling), feature extraction, feature selection, and statistical and predictive modeling [ 41 , 78 , 79 , 80 , 81 ]. Figure 1 illustrates the process of radiomic analysis as it pertains to identifying signatures for establishing the PCa grade group, as previously implemented by Chaddad et al [ 23 , 24 ].…”