Predicting cancer detection rates from multiparametric prostate MRI

Beyond the PI-RADS classification system

Authors

  • Agustin Perez-Londono Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, MA
  • Francisco Ramos Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX
  • Aaron Fleishman Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA
  • Sumedh Kaul Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA
  • Ruslan Korets Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, MA
  • Michael Johnson Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
  • Aria F. Olumi Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, MA
  • Leo Tsai Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA
  • Boris Gershman Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, MA

DOI:

https://doi.org/10.5489/cuaj.8902

Keywords:

Prostatic Neoplasms, Magnetic Resonance Imaging, Early Detection of Cancer, Diagnostic Imaging

Abstract

INTRODUCTION: Although the Prostate Imaging-Reporting and Data System (PI-RADS) categorization represents the standard method for assessing the risk of prostate cancer using prostate magnetic resonance imaging (MRI), there exists wide variation in cancer detection rates (CDRs) in real-world practice. We therefore evaluated the association of clinical and radiographic features with CDRs and developed a predictive model to improve clinical management.

METHODS: We identified men aged 18–89 years with elevated prostate-specfic antigen (PSA) or on active surveillance for prostate cancer who underwent MRI-ultrasound (US) fusion biopsy or in-bore MRI-targeted biopsy. The associations of features with the per-lesion CDR (Gleason 6–10) and clinically significant (cs) CDR (Gleason 7–10) were examined using logistic regression, and results were operationalized into a predictive model.

RESULTS: Targeted biopsy was performed for 347 lesions in 281 patients. Overall, the CDR was 49.0% and the csCDR was 28.0%. On multivariable analysis, increasing PI-RADS category, smaller prostate size, and increasing PSA density were independently associated with higher CDR, while prior prostate biopsy was associated with lower CDR. A solitary PI-RADS 3–5 lesion was independently associated with higher csCDR, while 2+ prior prostate biopsies was associated with a lower csCDR. A predictive model provided a greater net benefit than a strategy of performing biopsy in all PI-RADS 3–5 lesions across a wide range of threshold probabilities.

CONCLUSIONS: Several clinical and radiographic features are independently associated with the risk of prostate cancer in men undergoing MRI-targeted biopsy. A predictive model based on these features can improve clinical decisions regarding biopsy compared to the conventional strategy of performing biopsy for all PI-RADS 3–5 lesions.

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Author Biography

Ruslan Korets, Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, MA

 

 

Published

2024-11-04

How to Cite

Perez-Londono, A., Ramos, F., Fleishman, A., Kaul, S. ., Korets, R., Johnson, M., … Gershman, B. (2024). Predicting cancer detection rates from multiparametric prostate MRI: Beyond the PI-RADS classification system. Canadian Urological Association Journal, 19(3), E85–91. https://doi.org/10.5489/cuaj.8902

Issue

Section

Original Research