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Open AccessArticle

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

1
Experimental Radiation Oncology Group, Heidelberg University, D-68167 Mannheim, Germany
2
Department of Urology and Urosurgery, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany
3
Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany
4
Department of Radiology, Munich University Hospitals, D-81377 Munich, Germany
5
Siemens Healthineers, CT R&D Image Analytics, D-91301 Forchheim, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2020, 12(7), 1767; https://doi.org/10.3390/cancers12071767
Received: 14 May 2020 / Revised: 24 June 2020 / Accepted: 30 June 2020 / Published: 2 July 2020
(This article belongs to the Special Issue Radiomics and Cancers)
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. View Full-Text
Keywords: prostatic neoplasm; magnetic resonance imaging; radiomics; machine learning; artificial intelligence; PI-RADS; PSA prostatic neoplasm; magnetic resonance imaging; radiomics; machine learning; artificial intelligence; PI-RADS; PSA
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MDPI and ACS Style

Woźnicki, P.; Westhoff, N.; Huber, T.; Riffel, P.; Froelich, M.F.; Gresser, E.; von Hardenberg, J.; Mühlberg, A.; Michel, M.S.; Schoenberg, S.O.; Nörenberg, D. Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters. Cancers 2020, 12, 1767. https://doi.org/10.3390/cancers12071767

AMA Style

Woźnicki P, Westhoff N, Huber T, Riffel P, Froelich MF, Gresser E, von Hardenberg J, Mühlberg A, Michel MS, Schoenberg SO, Nörenberg D. Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters. Cancers. 2020; 12(7):1767. https://doi.org/10.3390/cancers12071767

Chicago/Turabian Style

Woźnicki, Piotr; Westhoff, Niklas; Huber, Thomas; Riffel, Philipp; Froelich, Matthias F.; Gresser, Eva; von Hardenberg, Jost; Mühlberg, Alexander; Michel, Maurice S.; Schoenberg, Stefan O.; Nörenberg, Dominik. 2020. "Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters" Cancers 12, no. 7: 1767. https://doi.org/10.3390/cancers12071767

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