Addressing racial disparities in prostate cancer pathology prediction models: external validation and comparison of four models of pathological outcome prediction before radical prostatectomy in the multiethnic SEARCH cohort

Mahdi Mottaghi; Lin Gu; Sriram Deivasigamani; Eric S Adams; Joshua Parrish; Christopher L Amling; William J Aronson; Christopher J Kane; Martha K Terris; Lourdes Guerrios-Rivera; Matthew R Cooperberg; Zachary Klaassen; Stephen J Freedland; Thomas J Polascik

doi:10.1038/s41391-024-00830-2

Addressing racial disparities in prostate cancer pathology prediction models: external validation and comparison of four models of pathological outcome prediction before radical prostatectomy in the multiethnic SEARCH cohort

Prostate Cancer Prostatic Dis. 2024 Apr 11. doi: 10.1038/s41391-024-00830-2. Online ahead of print.

Authors

Mahdi Mottaghi¹, Lin Gu^{2

3}, Sriram Deivasigamani³, Eric S Adams^{2

3}, Joshua Parrish², Christopher L Amling⁴, William J Aronson^{5

6}, Christopher J Kane⁷, Martha K Terris^{8

9

10}, Lourdes Guerrios-Rivera^{11

12}, Matthew R Cooperberg¹³, Zachary Klaassen^{8

9}, Stephen J Freedland^{2

14}, Thomas J Polascik^{2

3}

Affiliations

¹ Section of Urology, Department of Surgery, Durham Veterans Affairs Medical Center, Durham, NC, 27710, USA. mahdi.mottaghi@myyahoo.com.
² Section of Urology, Department of Surgery, Durham Veterans Affairs Medical Center, Durham, NC, 27710, USA.
³ Duke Cancer Institute and Duke University Medical Centre, Durham, NC, USA.
⁴ Oregon Health & Science University, Department of Urology, Portland, OR, 97239, USA.
⁵ Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
⁶ Department of Urology, UCLA School of Medicine, Los Angeles, CA, USA.
⁷ Urology Department, University of California San Diego Health System, San Diego, CA, USA.
⁸ Division of Urology, Department of Surgery, Medical College of Georgia - Augusta University, Augusta, GA, USA.
⁹ Georgia Cancer Center, Augusta, GA, USA.
¹⁰ Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA.
¹¹ University of Puerto Rico, Department of Surgery, San Juan, PR, USA.
¹² VA Caribbean Healthcare System, San Juan, PR, USA.
¹³ Department of Urology, UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA.
¹⁴ Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

PMID: 38605270
DOI: 10.1038/s41391-024-00830-2

Abstract

Background: Certain widely used pathological outcome prediction models that were developed in tertiary centers tend to overpredict outcomes in the community setting; thus, the Michigan Urological-Surgery Improvement Collaborative (MUSIC) model was developed in general urology practice to address this issue. Additionally, the development of these models involved a relatively small proportion of Black men, potentially compromising the accuracy of predictions in this patient group. We tested the validity of the MUSIC and three widely used nomograms to compare their overall and race-stratified predictive performance.

Methods: We extracted data from 4139 (1138 Black) men from the Shared Equal Access Regional Cancer Hospital (SEARCH) database of the Veterans Affairs health system. The predictive performance of the MUSIC model was compared to the Memorial-Sloan Kettering (MSK), Briganti-2012, and Partin-2017 models for predicting lymph-node invasion (LNI), extra-prostatic extension (EPE), and seminal vesicle invasion (SVI).

Results: The median PSA of Black men was higher than White men (7.8 vs. 6.8 ng/ml), although they were younger by a median of three years and presented at a lower-stage disease. MUSIC model showed comparable discriminatory capacity (AUC:77.0%) compared to MSK (79.2%), Partin-2017 (74.6%), and Briganti-2012 (76.3%), with better calibration for LNI. AUCs for EPE and SVI were 72.7% and 76.9%, respectively, all comparable to the MSK and Partin models. LNI AUCs for Black and White men were 69.6% and 79.6%, respectively, while EPE and SVI AUCs were comparable between races. EPE and LNI had worse calibration in Black men. Decision curve analysis showed MUSIC superiority over the MSK model in predicting LNI, especially among Black men.

Conclusion: Although the discriminatory performance of all models was comparable for each outcome, the MUSIC model exhibited superior net benefit to the MSK model in predicting LNI outcomes among Black men in the SEARCH population.