• Shoshana B Ginsburg, Ahmad Algohary, Shivani Pahwa, Vikas Gulani, Lee Ponsky, Hannu J Aronen, Peter J Boström, Maret Böhm, Anne-Maree Haynes, Phillip Brenner, Warick Delprado, James Thompson, Marley Pulbrock, Pekka Taimen, Robert Villani, Phillip Stricker, Ardeshir R Rastinehad, Ivan Jambor, and Anant Madabhushi.
• Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
• J Magn Reson Imaging. 2017 Jul 1; 46 (1): 184-193.

PurposeTo 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 Methods3T 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.ResultsRadiomic 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.ConclusionA zone-aware classifier significantly improves the accuracy of cancer detection in the PZ.Level Of Evidence3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193.© 2016 International Society for Magnetic Resonance in Medicine.

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