Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI

• Published in: Magnetic resonance imaging Vol. 34; no. 9; pp. 1227 - 1234
• Main Authors: Freidlin, Raisa Z, Agarwal, Harsh K, Sankineni, Sandeep, Brown, Anna M, Mertan, Francesca, Bernardo, Marcelino, Daar, Dagane, Merino, Maria, Citrin, Deborah, Wood, Bradford J, Pinto, Peter A, Choyke, Peter L, Turkbey, Baris
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-11-2016
• Subjects: Aged; Diffusion Magnetic Resonance Imaging - methods; Diffusion-weighted MRI; DWI; Humans; Male; Middle Aged; Prostate - diagnostic imaging; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Radiology; Reproducibility of Results; Sensitivity and Specificity; Tumor localization; prostate cancer; tumor localization; DWI; diffusion-weighted MRI; Prostate cancer; Diffusion-weighted MRI; Tumor localization
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/j.mri.2016.06.004

Abstract Purpose The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in “positive-if-all-positive” manner, from different models to localize tumors. Methods A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D *, D , f , and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65 years (range 54–78) and 21.9 ng/mL (range 4.84–44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0–2000 s/mm2 (TE = 58 ms; TR = 3990 ms; spatial resolution 2.19 × 2.19 × 2.73 mm3 , slices =26, FOV = 140x140mm, slice gap =0.27 mm, NSA = 2). Results The proposed framework detected 24 lesions with 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%, positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients. Conclusion Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.