• Ahmad Algohary, Satish Viswanath, Rakesh Shiradkar, Soumya Ghose, Shivani Pahwa, Daniel Moses, Ivan Jambor, Ronald Shnier, Maret Böhm, Anne-Maree Haynes, Phillip Brenner, Warick Delprado, James Thompson, Marley Pulbrock, Andrei S Purysko, Sadhna Verma, Lee Ponsky, Phillip Stricker, and Anant Madabhushi.
• Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
• J Magn Reson Imaging. 2018 Feb 22.

BackgroundRadiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS).PurposeTo evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients.Study TypeRetrospective.Subjects ModelMRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy.Field Strength/Sequence3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI.AssessmentA pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative-biopsy-negative (Group 1, N = 15), MRI-positive-biopsy-positive (Group 2, N = 16), MRI-negative-biopsy-positive (Group 3, N = 10), and MRI-positive-biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy-maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease.Statistical TestsWilcoxon rank-sum tests with P < 0.05 considered statistically significant.ResultsSeven T2 w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone.Data ConclusionRadiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies.Level Of Evidence3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.© 2018 International Society for Magnetic Resonance in Medicine.

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