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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.

ABSTRACT: Background:To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores. Methods:We retrospectively included 201, histopathologically-proven, peripheral zone (PZ) prostate cancer lesions. Gleason scores ?3+3 were considered as clinically insignificant (inPC) and Gleason scores ?3+4 as sPC and were encoded in a binary fashion, serving as ground-truth. MRI was performed at 3T with high spatiotemporal resolution DCE using Golden-angle RAdial SParse (GRASP) MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2-signal intensities (SI) were determined in all lesions and served as input parameters for four supervised ML models: Gradient Boosting Machines (GBM), Neural Networks (NNet), Random Forest (RF) and Support Vector Machines (SVM). ML results and PI-RADS scores were compared with the ground-truth. Next ROC-curves and AUC values were calculated. Results:All ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC (RF, GBM, NNet and SVM vs. PI-RADS: AUC 0.899, 0.864, 0.884 and 0.874 vs. 0.595, all P<0.001). Conclusions:Using quantitative imaging parameters as input, supervised ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC. These results indicate that quantitative imagining parameters contain relevant information for the prediction of sPC from image features.

SUBMITTER: Winkel DJ

PROVIDER: S-EPMC7188610 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.

Winkel David Jean DJ Breit Hanns-Christian HC Shi Bibo B Boll Daniel T DT Seifert Hans-Helge HH Wetterauer Christian C

Quantitative imaging in medicine and surgery 20200401 4

<h4>Background</h4>To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores.<h4>Methods</h4>We retrospectively included 201, histopathologically-proven, peripheral zone (PZ) prostate cancer lesions. Gleason scores ≤3+3 were considered as clinically insignificant (inPC) and Gleason scores ≥3+4 as sPC and were encode ...[more]

PMID: 32355645

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Project description:BackgroundMultiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores.PurposeTo develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores.Study typeRetrospective.PopulationIn all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study.Field strength/sequenceConventional and diffusion-weighted MR images (b values = 0, 1000 sec/mm2 ) were acquired on a 3.0T MR scanner.AssessmentA total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T2 WI, respectively. A predictive model was constructed for differentiating PCa from non-PCa and high-grade from low-grade PCa. The diagnostic performance of each radiomics-based model was compared with that of the PI-RADS v2 scores.Statistical testsA radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.ResultsFor PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T2 WI, ADC, and T2 WI&ADC features, respectively. For low-grade versus high-grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T2 WI, ADC, and T2 WI&ADC features, respectively. PI-RADS v2 had an AUC of 0.867 in differentiating PCa from non-PCa and an AUC of 0.763 in differentiating high-grade from low-grade PCa.Data conclusionBoth the T2 WI- and ADC-based radiomics models showed high diagnostic efficacy and outperformed the PI-RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high-grade vs. low-grade PCa.Level of evidence3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.

Project description:CONTEXT:The Prostate Imaging-Reporting and Data System (PI-RADS) v2 analysis system for multiparametric magnetic resonance imaging (mpMRI) detection of prostate cancer (PCa) is based on PI-RADS v1, accumulated scientific evidence, and expert consensus opinion. OBJECTIVE:To summarize the accuracy, strengths and weaknesses of PI-RADS v2, discuss pathway implications of its use and outline opportunities for improvements and future developments. EVIDENCE ACQUISITION:For this consensus expert opinion from the PI-RADS steering committee, clinical studies, systematic reviews, and professional guidelines for mpMRI PCa detection were evaluated. We focused on the performance characteristics of PI-RADS v2, comparing data to systems based on clinicoradiologic Likert scales and non-PI-RADS v2 imaging only. Evidence selections were based on high-quality, prospective, histologically verified data, with minimal patient selection and verifications biases. EVIDENCE SYNTHESIS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice retains higher accuracy over systematic transrectal ultrasound (TRUS) biopsies for PCa diagnosis. PI-RADS v2 fails to detect all cancers but does detect the majority of tumors capable of causing patient harm, which should not be missed. Test performance depends on the definition and prevalence of clinically significant disease. Good performance can be attained in practice when the quality of the diagnostic process can be assured, together with joint working of robustly trained radiologists and urologists, conducting biopsy procedures within multidisciplinary teams. CONCLUSIONS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice is improved, retaining higher accuracy over systematic TRUS biopsies for PCa diagnosis. PATIENT SUMMARY:Multiparametric magnetic resonance imaging (MRI) and MRI-directed biopsies using the Prostate Imaging-Reporting and Data System improves the detection of prostate cancers likely to cause harm, and at the same time decreases the detection of disease that does not lead to harms if left untreated. The keys to success are high-quality imaging, reporting, and biopsies by radiologists and urologists working together in multidisciplinary teams.

Dataset Information

Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.

Publications

Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.

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