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. 2018 May 1;4(5):678-685.
doi: 10.1001/jamaoncol.2017.5667.

A Magnetic Resonance Imaging-Based Prediction Model for Prostate Biopsy Risk Stratification

Sherif Mehralivand  1   2   3 , Joanna H Shih  4 , Soroush Rais-Bahrami  5   6 , Aytekin Oto  7 , Sandra Bednarova  8   9 , Jeffrey W Nix  5 , John V Thomas  6 , Jennifer B Gordetsky  10 , Sonia Gaur  3 , Stephanie A Harmon  11 , Mohummad Minhaj Siddiqui  12 , Maria J Merino  13 , Howard L Parnes  14 , Bradford J Wood  9 , Peter A Pinto  2 , Peter L Choyke  3 , Baris Turkbey  3
Affiliations

A Magnetic Resonance Imaging-Based Prediction Model for Prostate Biopsy Risk Stratification

Sherif Mehralivand et al. JAMA Oncol. .

Abstract

Importance: Multiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters.

Objective: To determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer.

Design, setting, and participants: Patients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed.

Main outcomes and measures: Risk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3 + 4 or higher in at least 1 biopsy core.

Results: Overall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3 + 4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P < .001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers.

Conclusions and relevance: The inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Wood reported holding potential financial interest and/or other relationship with Philips InVivo. Dr Pinto reported holding potential financial interest and/or other relationship with Philips Medical. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Inclusion and Exclusion Criteria for the Development Cohort
MRI indicates magnetic resonance imaging.
Figure 2.
Figure 2.. Plot of Performance Metrics of the Validation Cohort
A, Receiver operating characteristic curves or risk prediction models for clinically significant prostate cancer. B, True-positive rate (TPR) and false-positive rate (FPR). C, Net benefit. D, Net reduction in false-positive results from the 2 risk prediction models. AUC indicates area under the receiver operating characteristic curve; MRI, magnetic resonance imaging.
See this image and copyright information in PMC

Comment in

References

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