Eric H Kim1, John K Weaver1, Anup S Shetty2, Joel M Vetter1, Gerald L Andriole1, Seth A Strope3. 1. Division of Urologic Surgery, Washington University School of Medicine, St. Louis, MO. 2. Department of Radiology, Washington University School of Medicine, St. Louis, MO. 3. Division of Urologic Surgery, Washington University School of Medicine, St. Louis, MO. Electronic address: stropes@wudosis.wustl.edu.
Abstract
OBJECTIVE: To determine the added value of prostate magnetic resonance imaging (MRI) to the Prostate Cancer Prevention Trial risk calculator. METHODS: Between January 2012 and December 2015, 339 patients underwent prostate MRI prior to biopsy at our institution. MRI was considered positive if there was at least 1 Prostate Imaging Reporting and Data System 4 or 5 MRI suspicious region. Logistic regression was used to develop 2 models: biopsy outcome as a function of the (1) Prostate Cancer Prevention Trial risk calculator alone and (2) combined with MRI findings. RESULTS: When including all patients, the Prostate Cancer Prevention Trial with and without MRI models performed similarly (area under the curve [AUC] = 0.74 and 0.78, P = .06). When restricting the cohort to patients with estimated risk of high-grade (Gleason ≥7) prostate cancer ≤10%, the model with MRI outperformed the Prostate Cancer Prevention Trial alone model (AUC = 0.69 and 0.60, P = .01). Within this cohort of patients, there was no significant difference in discrimination between models for those with previous negative biopsy (AUC = 0.61 vs 0.63, P = .76), whereas there was a significant improvement in discrimination with the MRI model for biopsy-naïve patients (AUC = 0.72 vs 0.60, P = .01). CONCLUSION: The use of prostate MRI in addition to the Prostate Cancer Prevention Trial risk calculator provides a significant improvement in clinical risk discrimination for patients with estimated risk of high-grade (Gleason ≥7) prostate cancer ≤10%. Prebiopsy prostate MRI should be strongly considered for these patients.
OBJECTIVE: To determine the added value of prostate magnetic resonance imaging (MRI) to the Prostate Cancer Prevention Trial risk calculator. METHODS: Between January 2012 and December 2015, 339 patients underwent prostate MRI prior to biopsy at our institution. MRI was considered positive if there was at least 1 Prostate Imaging Reporting and Data System 4 or 5 MRI suspicious region. Logistic regression was used to develop 2 models: biopsy outcome as a function of the (1) Prostate Cancer Prevention Trial risk calculator alone and (2) combined with MRI findings. RESULTS: When including all patients, the Prostate Cancer Prevention Trial with and without MRI models performed similarly (area under the curve [AUC] = 0.74 and 0.78, P = .06). When restricting the cohort to patients with estimated risk of high-grade (Gleason ≥7) prostate cancer ≤10%, the model with MRI outperformed the Prostate Cancer Prevention Trial alone model (AUC = 0.69 and 0.60, P = .01). Within this cohort of patients, there was no significant difference in discrimination between models for those with previous negative biopsy (AUC = 0.61 vs 0.63, P = .76), whereas there was a significant improvement in discrimination with the MRI model for biopsy-naïve patients (AUC = 0.72 vs 0.60, P = .01). CONCLUSION: The use of prostate MRI in addition to the Prostate Cancer Prevention Trial risk calculator provides a significant improvement in clinical risk discrimination for patients with estimated risk of high-grade (Gleason ≥7) prostate cancer ≤10%. Prebiopsy prostate MRI should be strongly considered for these patients.
Authors: Frank-Jan H Drost; Daniël F Osses; Daan Nieboer; Ewout W Steyerberg; Chris H Bangma; Monique J Roobol; Ivo G Schoots Journal: Cochrane Database Syst Rev Date: 2019-04-25
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