Abstract
Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.
Highlights
Prostate cancer (PCa) is the second most frequent cancer diagnosis made in men, with 1,276,106 new cases and 358,989 deaths reported worldwide in 2018 [1]
We found that the addition of clinical parameters, including digital rectal examination (DRE) and prostate specific antigen density (PSAD) to the radiomics model yielded predictive performances superior to predictive ability of the radiologist’s evaluation (PI-RADS), in regard of AUC and sensitivity/specificity for both considered tasks, the differences did not achieve statistical significance in the majority of comparisons
We showed that radiomics, using quantitative image data, is able to accurately characterize index lesions of the prostate derived from multiparametric magnetic resonance imaging (mpMRI) and performs comparably to radiologists for the differentiation of (i) malignant from benign prostate lesions and (ii) clinically significant prostate cancer (csPCa) from clinically insignificant PCa (cisPCa)
Summary
Prostate cancer (PCa) is the second most frequent cancer diagnosis made in men, with 1,276,106 new cases and 358,989 deaths reported worldwide in 2018 [1]. And accurate detection of clinically significant prostate cancer (csPCa), which is defined as ISUP grade 2 or higher [2], is essential to initiate treatment in a timely manner and improve patient outcomes [3]. Multiparametric magnetic resonance imaging (mpMRI) and consecutive targeted biopsy have become integral diagnostic procedures for the detection and risk stratification of csPCa [4,5], showing an improvement in detection compared to systematic non-targeted prostate biopsy [5,6,7]. The Prostate Imaging-Reporting and Data System (PI-RADS) defines standards for image acquisition and reporting and is broadly utilized in clinical practice. The sensitivity of 93% and the negative predictive value of 89% were reported for the detection of csPCa using the PI-RADS classification [3]. Prostate-specific antigen density (PSAD) has been proved to increase the specificity of prostate-specific antigen (PSA)
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