The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer
To evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleaso...
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| Vydáno v: | Oncotarget Ročník 8; číslo 27; s. 44040 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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04.07.2017
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| ISSN: | 1949-2553, 1949-2553 |
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| Abstract | To evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).
Overall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.
The clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.
The PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance. |
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| AbstractList | To evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).
Overall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.
The clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.
The PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance. To evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).PURPOSETo evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).Overall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.RESULTSOverall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.The clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.MATERIALS AND METHODSThe clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.The PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance.CONCLUSIONThe PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance. |
| Author | Cao, Yudong Chen, Yuke Wang, Xiaoying Jin, Jie Yu, Wei Gao, Ge Liu, Qing Cao, Min Fan, Yu Zhao, Zheng |
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| CitedBy_id | crossref_primary_10_4103_aja_aja_51_20 crossref_primary_10_1245_s10434_019_08111_2 crossref_primary_10_1007_s11934_023_01187_0 crossref_primary_10_1016_j_heliyon_2024_e25030 crossref_primary_10_1097_MOU_0000000000000538 crossref_primary_10_3390_cancers14205077 crossref_primary_10_1016_j_asjsur_2025_05_072 crossref_primary_10_3390_ijms22083935 crossref_primary_10_1016_j_urolonc_2024_09_004 crossref_primary_10_1007_s10147_019_01559_y crossref_primary_10_1111_bju_14173 crossref_primary_10_1016_j_clgc_2019_06_001 |
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| SubjectTerms | Aged Biopsy Decision Support Techniques Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Magnetic Resonance Imaging - methods Male Middle Aged Neoplasm Grading Nomograms Prognosis Prostatic Neoplasms - diagnostic imaging Prostatic Neoplasms - pathology Risk Factors |
| Title | The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer |
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