Eric J Jordan1, Charles Fiske2, Ronald Zagoria1, Antonio C Westphalen3,4. 1. Department of Radiology, University of California San Francisco, San Francisco, CA, USA. 2. NorCal Imaging, RadNet, Walnut Creek, CA, USA. 3. Department of Radiology, University of California San Francisco, San Francisco, CA, USA. AntonioCarlos.Westphalen@ucsf.edu. 4. Department of Radiology & Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave, Room M-327, San Francisco, CA, 94143, USA. AntonioCarlos.Westphalen@ucsf.edu.
Abstract
PURPOSE: To determine the diagnostic accuracy of ADC values in combination with PI-RADS v2 for the diagnosis of clinically significant prostate cancer (CS-PCa) compared to PI-RADS v2 alone. MATERIALS AND METHODS: This retrospective study included 155 men whom underwent 3-Tesla prostate MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. All scans were performed with a surface coil and included T2, diffusion-weighted, and dynamic contrast-enhanced sequences. Suspicious findings were classified using Prostate Imaging Reporting and Data System (PI-RADS) v2 and targeted using MR/US fusion biopsies. Mixed-effect logistic regression analyses were used to determine the ability of PIRADS v2 alone and combined with ADC values to predict CS-PCa. As ADC categories are more practical in clinical situations than numeric values, an additional model with ADC categories of ≤ 800 and > 800 was performed. RESULTS: A total of 243 suspicious lesions were included, 69 of which were CS-PCa, 34 were Gleason score 3+3 PCa, and 140 were negative. The overall PIRADS v2 score, ADC values, and ADC categories are independent statistically significant predictors of CS-PCa (p < 0.001). However, the area under the ROC of PIRADS v2 alone and PIRADS v2 with ADC categories are significantly different in both peripheral and transition zone lesions (p = 0.026 and p = 0.03, respectively) Further analysis of the ROC curves also shows that the main benefit of utilizing ADC values or categories is better discrimination of PI-RADS v2 4 lesions. CONCLUSION: ADC values and categories help to diagnose CS-PCa when lesions are assigned a PI-RADS v2 score of 4.
PURPOSE: To determine the diagnostic accuracy of ADC values in combination with PI-RADS v2 for the diagnosis of clinically significant prostate cancer (CS-PCa) compared to PI-RADS v2 alone. MATERIALS AND METHODS: This retrospective study included 155 men whom underwent 3-Tesla prostate MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. All scans were performed with a surface coil and included T2, diffusion-weighted, and dynamic contrast-enhanced sequences. Suspicious findings were classified using Prostate Imaging Reporting and Data System (PI-RADS) v2 and targeted using MR/US fusion biopsies. Mixed-effect logistic regression analyses were used to determine the ability of PIRADS v2 alone and combined with ADC values to predict CS-PCa. As ADC categories are more practical in clinical situations than numeric values, an additional model with ADC categories of ≤ 800 and > 800 was performed. RESULTS: A total of 243 suspicious lesions were included, 69 of which were CS-PCa, 34 were Gleason score 3+3 PCa, and 140 were negative. The overall PIRADS v2 score, ADC values, and ADC categories are independent statistically significant predictors of CS-PCa (p < 0.001). However, the area under the ROC of PIRADS v2 alone and PIRADS v2 with ADC categories are significantly different in both peripheral and transition zone lesions (p = 0.026 and p = 0.03, respectively) Further analysis of the ROC curves also shows that the main benefit of utilizing ADC values or categories is better discrimination of PI-RADS v2 4 lesions. CONCLUSION: ADC values and categories help to diagnose CS-PCa when lesions are assigned a PI-RADS v2 score of 4.
Entities:
Keywords:
ADC values; PI-RADS; Prostate MRI; Prostate cancer
Authors: Antonio C Westphalen; Farhad Fazel; Hao Nguyen; Miguel Cabarrus; Katryana Hanley-Knutson; Katsuto Shinohara; Peter R Carroll Journal: Int Braz J Urol Date: 2019 Jul-Aug Impact factor: 1.541
Authors: Gehad Ahmad Saleh; Ahmed Abdel Khalek Abdel Razek; Lamiaa Galal El-Serougy; Walaa Shabana; Rihame Abd El-Wahab Journal: Pol J Radiol Date: 2022-01-17