Dong Fang1, Chenglin Zhao2, Da Ren1, Wei Yu1, Rui Wang2, Huihui Wang2, Xuesong Li1, Wenshi Yin1, Xiaoteng Yu1, Kunlin Yang1, Pei Liu1, Gangzhi Shan1, Shuqing Li1, Qun He1, Xiaoying Wang2, Zhongcheng Xin3, Liqun Zhou4. 1. Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Centre, Beijing, China. 2. Department of Radiology, Peking University First Hospital, Beijing, China. 3. Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Centre, Beijing, China. xinzc@bjmu.edu.cn. 4. Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Centre, Beijing, China. zhoulqmail@sina.com.
Abstract
PURPOSE: This study was designed to investigate the effectiveness of magnetic resonance imaging (MRI) in diagnosing prostate cancer (PCa) and high-grade prostate cancer (HGPCa) before transrectal ultrasound (TRUS)-guided biopsy. METHODS: The clinical data of 894 patients who received TRUS-guided biopsy and prior MRI test from a large Chinese center was reviewed. Based on Prostate Imaging Reporting and Data System (PI-RADS) scoring, all MRIs were re-reviewed and assigned as Grade 0-2 (PI-RADS 1-2; PI-RADS 3; PI-RADS 4-5). We constructed two models both in predicting PCa and HGPCa (Gleason score ≥ 4 + 3): Model 1 with MRI and Model 2 without MRI. Other clinical factors include age, digital rectal examination, PSA, free-PSA, volume, and TRUS. RESULTS: PCa and HGPCa were present in 434 (48.5 %) and 218 (24.4 %) patients. An MRI Grade 0, 1, and 2 were assigned in 324 (36.2 %), 193 (21.6 %) and 377 (42.2 %) patients, which was associated with the presence of PCa (p < 0.001) and HGPCa (p < 0.001). Particularly in patients aged ≤55 years, the assignment of MRI Grade 0 was correlated with extremely low rate of PCa (1/27) and no HGPCa. The c-statistic of Model 1 and Model 2 for predicting PCa was 0.875 and 0.841 (Z = 4.2302, p < 0.001), whereas for predicting HGPCa was 0.872 and 0.850 (Z = 3.265, p = 0.001). Model 1 exhibited higher sensitivity and specificity at same cutoffs, and decision-curve analysis also suggested the favorable clinical utility of Model 1. CONCLUSIONS: Prostate MRI before biopsy could predict the presence of PCa and HGPCa, especially in younger patients. The incorporation of MRI in nomograms could increase predictive accuracy.
PURPOSE: This study was designed to investigate the effectiveness of magnetic resonance imaging (MRI) in diagnosing prostate cancer (PCa) and high-grade prostate cancer (HGPCa) before transrectal ultrasound (TRUS)-guided biopsy. METHODS: The clinical data of 894 patients who received TRUS-guided biopsy and prior MRI test from a large Chinese center was reviewed. Based on Prostate Imaging Reporting and Data System (PI-RADS) scoring, all MRIs were re-reviewed and assigned as Grade 0-2 (PI-RADS 1-2; PI-RADS 3; PI-RADS 4-5). We constructed two models both in predicting PCa and HGPCa (Gleason score ≥ 4 + 3): Model 1 with MRI and Model 2 without MRI. Other clinical factors include age, digital rectal examination, PSA, free-PSA, volume, and TRUS. RESULTS: PCa and HGPCa were present in 434 (48.5 %) and 218 (24.4 %) patients. An MRI Grade 0, 1, and 2 were assigned in 324 (36.2 %), 193 (21.6 %) and 377 (42.2 %) patients, which was associated with the presence of PCa (p < 0.001) and HGPCa (p < 0.001). Particularly in patients aged ≤55 years, the assignment of MRI Grade 0 was correlated with extremely low rate of PCa (1/27) and no HGPCa. The c-statistic of Model 1 and Model 2 for predicting PCa was 0.875 and 0.841 (Z = 4.2302, p < 0.001), whereas for predicting HGPCa was 0.872 and 0.850 (Z = 3.265, p = 0.001). Model 1 exhibited higher sensitivity and specificity at same cutoffs, and decision-curve analysis also suggested the favorable clinical utility of Model 1. CONCLUSIONS: Prostate MRI before biopsy could predict the presence of PCa and HGPCa, especially in younger patients. The incorporation of MRI in nomograms could increase predictive accuracy.
Authors: Juan Morote; Angel Borque-Fernando; Marina Triquell; Anna Celma; Lucas Regis; Richard Mast; Inés M de Torres; María E Semidey; José M Abascal; Pol Servian; Anna Santamaría; Jacques Planas; Luis M Esteban; Enrique Trilla Journal: Cancers (Basel) Date: 2022-05-11 Impact factor: 6.575
Authors: Juan Morote; Angel Borque-Fernando; Marina Triquell; Anna Celma; Lucas Regis; Manel Escobar; Richard Mast; Inés M de Torres; María E Semidey; José M Abascal; Carles Sola; Pol Servian; Daniel Salvador; Anna Santamaría; Jacques Planas; Luis M Esteban; Enrique Trilla Journal: Cancers (Basel) Date: 2022-03-21 Impact factor: 6.639