BACKGROUND: The Prostate Cancer Prevention Trial risk calculator for high-grade (PCPTHG) prostate cancer (CaP) was developed to improve the detection of clinically significant CaP. In this study, the authors compared the performance of the PCPTHG against multiparametric magnetic resonance imaging (MP-MRI) in predicting men at risk of CaP. METHODS: Men with an abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE) and a suspicious lesion on a 3-Tesla MP-MRI were enrolled prospectively. Three radiologists reviewed and graded all lesions on a 5-point Likert scale. Biopsy of suspicious lesion(s) was performed using a proprietary MRI/transrectal ultrasound fusion-guided prostate biopsy system, after which 12-core biopsy was performed. A genitourinary pathologist reviewed all pathology slides. The performance of PCPTHG was compared with that of MP-MRI in predicting clinically significant CaP. RESULTS: Of 175 men who were eligible for analysis, 64.6% (113 of 175 men) were diagnosed with CaP, including 93 of 113 men (82.3%) who had clinically significant disease. Age, abnormal DRE, PSA, PSA density, prostate size, extraprostatic extension on MRI, apparent diffusion coefficient value, and MRI lesion size were identified as significant predictors of high-grade CaP (all P < .05). The area under the receiver operating characteristic curve of PCPTHG for predicting high-grade CaP was 0.676 (95% confidence interval [CI], 0.592-0.751). By using a risk cutoff of ≥15% for biopsy as, proposed previously for high-grade CaP, sensitivity was 96.4%, specificity was 7.6%, and the false-positive rate was 51.1%. In contrast, the area under the receiver operating characteristic curve of MP-MRI for high-grade CaP was 0.769 (95% CI, 0.703-0.834), and it was 0.812 (95% CI, 0.754-0.869) for clinically significant CaP. CONCLUSIONS: MP-MRI outperforms PCPTHG in predicting clinically significant CaP, and its application may help select patients who will benefit from CaP diagnosis and treatment.
BACKGROUND: The Prostate Cancer Prevention Trial risk calculator for high-grade (PCPTHG) prostate cancer (CaP) was developed to improve the detection of clinically significant CaP. In this study, the authors compared the performance of the PCPTHG against multiparametric magnetic resonance imaging (MP-MRI) in predicting men at risk of CaP. METHODS:Men with an abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE) and a suspicious lesion on a 3-Tesla MP-MRI were enrolled prospectively. Three radiologists reviewed and graded all lesions on a 5-point Likert scale. Biopsy of suspicious lesion(s) was performed using a proprietary MRI/transrectal ultrasound fusion-guided prostate biopsy system, after which 12-core biopsy was performed. A genitourinary pathologist reviewed all pathology slides. The performance of PCPTHG was compared with that of MP-MRI in predicting clinically significant CaP. RESULTS: Of 175 men who were eligible for analysis, 64.6% (113 of 175 men) were diagnosed with CaP, including 93 of 113 men (82.3%) who had clinically significant disease. Age, abnormal DRE, PSA, PSA density, prostate size, extraprostatic extension on MRI, apparent diffusion coefficient value, and MRI lesion size were identified as significant predictors of high-grade CaP (all P < .05). The area under the receiver operating characteristic curve of PCPTHG for predicting high-grade CaP was 0.676 (95% confidence interval [CI], 0.592-0.751). By using a risk cutoff of ≥15% for biopsy as, proposed previously for high-grade CaP, sensitivity was 96.4%, specificity was 7.6%, and the false-positive rate was 51.1%. In contrast, the area under the receiver operating characteristic curve of MP-MRI for high-grade CaP was 0.769 (95% CI, 0.703-0.834), and it was 0.812 (95% CI, 0.754-0.869) for clinically significant CaP. CONCLUSIONS: MP-MRI outperforms PCPTHG in predicting clinically significant CaP, and its application may help select patients who will benefit from CaP diagnosis and treatment.
Authors: Raisa Z Freidlin; Harsh K Agarwal; Sandeep Sankineni; Anna M Brown; Francesca Mertan; Marcelino Bernardo; Dagane Daar; Maria Merino; Deborah Citrin; Bradford J Wood; Peter A Pinto; Peter L Choyke; Baris Turkbey Journal: Magn Reson Imaging Date: 2016-07-20 Impact factor: 2.546
Authors: Matthew J Watson; Arvin K George; Mahir Maruf; Thomas P Frye; Akhil Muthigi; Michael Kongnyuy; Subin G Valayil; Peter A Pinto Journal: Future Oncol Date: 2016-07-12 Impact factor: 3.404
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Authors: M Minhaj Siddiqui; Arvin K George; Rachel Rubin; Soroush Rais-Bahrami; Howard L Parnes; Maria J Merino; Richard M Simon; Baris Turkbey; Peter L Choyke; Bradford J Wood; Peter A Pinto Journal: J Natl Cancer Inst Date: 2016-04-29 Impact factor: 13.506
Authors: Simpa S Salami; Jeremy B Kaplan; Srinivas Nallandhighal; Mandeep Takhar; Jeffrey J Tosoian; Matthew Lee; Junhee Yoon; Daniel H Hovelson; Komal R Plouffe; Samuel D Kaffenberger; Edward M Schaeffer; R Jeffrey Karnes; Tamara L Lotan; Todd M Morgan; Arvin K George; Jeffrey S Montgomery; Matthew S Davenport; Sungyong You; Scott A Tomlins; Nicole E Curci; Hyung L Kim; Daniel E Spratt; Aaron M Udager; Ganesh S Palapattu Journal: JCO Precis Oncol Date: 2019-06-12