Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi‐institutional study. Issue 1 (19th December 2016)

• Record Type: Journal Article
• Authors: Ginsburg, Shoshana B.; Algohary, Ahmad; Pahwa, Shivani; Gulani, Vikas; Ponsky, Lee; Aronen, Hannu J.; Boström, Peter J.; Böhm, Maret; Haynes, Anne‐Maree; Brenner, Phillip; Delprado, Warick; Thompson, James; Pulbrock, Marley; Taimen, Pekka; Villani, Robert; Stricker, Phillip; Rastinehad, Ardeshir R.; Jambor, Ivan; Madabhushi, Anant
• Is Part Of: Journal of magnetic resonance imaging. Volume 46:Issue 1(2017)
• Page Start: 184
• Page End: 193
• Publication Date: 2016-12-19
• Subjects: magnetic resonance imaging -- prostate cancer -- radiomics -- multi‐institutional; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25562
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Purpose: To evaluate in a multi‐institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi‐parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). Materials and Methods: 3T mpMRI, including T2‐weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast‐enhanced MRI (DCE‐MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First‐order statistical, co‐occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE‐MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver‐operating characteristic curve (AUC). Classifier performance was compared with a zone‐ignorant classifier. Results: Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per‐voxel basis, a PZ‐specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61–0.71) than a zone‐ignorant classifier trained to detect cancer throughout the entire prostate ( P < 0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values ( P > 0.14) were obtained for all institutions. Conclusion: A zone‐aware classifier significantly improves the accuracy of cancer detection in the PZ. Level of Evidence: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184–193