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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 and Prostatic Diseases (2024)Cite this article

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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.

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Fig. 1: Calibration plots of all models, regardless of race.
Fig. 2: Calibration plots of all models, race stratified.
Fig. 3: Decision curve analysis of all models regardless of race.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This material is the result of work supported with resources and the use of facilities at the VA Caribbean Healthcare System. The contents of this publication do not represent the views of the VA Caribbean Healthcare System, the Department of Veterans Affairs, or the United States Government.

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Authors and Affiliations

  1. Section of Urology, Department of Surgery, Durham Veterans Affairs Medical Center, Durham, NC, 27710, USA

    Mahdi Mottaghi, Lin Gu, Eric S. Adams, Joshua Parrish, Stephen J. Freedland & Thomas J. Polascik

  2. Duke Cancer Institute and Duke University Medical Centre, Durham, NC, USA

    Lin Gu, Sriram Deivasigamani, Eric S. Adams & Thomas J. Polascik

  3. Oregon Health & Science University, Department of Urology, Portland, OR, 97239, USA

    Christopher L. Amling

  4. Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA

    William J. Aronson

  5. Department of Urology, UCLA School of Medicine, Los Angeles, CA, USA

    William J. Aronson

  6. Urology Department, University of California San Diego Health System, San Diego, CA, USA

    Christopher J. Kane

  7. Division of Urology, Department of Surgery, Medical College of Georgia – Augusta University, Augusta, GA, USA

    Martha K. Terris & Zachary Klaassen

  8. Georgia Cancer Center, Augusta, GA, USA

    Martha K. Terris & Zachary Klaassen

  9. Charlie Norwood Veterans Affairs Medical Center, Augusta, GA, USA

    Martha K. Terris

  10. University of Puerto Rico, Department of Surgery, San Juan, PR, USA

    Lourdes Guerrios-Rivera

  11. VA Caribbean Healthcare System, San Juan, PR, USA

    Lourdes Guerrios-Rivera

  12. Department of Urology, UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA

    Matthew R. Cooperberg

  13. Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Stephen J. Freedland

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  1. Mahdi Mottaghi
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Contributions

Study concept and design: ESA; MM, Acquisition of data: Institute for Medical Research Team, Drafting of the manuscript: MM; SD, Critical revision of the manuscript: TJP; SJF; ESA; SD; MM; LG; CLA; WJA; CJK; MKT; LGR; MRC; ZK, Statistical analysis: LG, Supervision: TJP; SJF.

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Correspondence to Mahdi Mottaghi.

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Mottaghi, M., Gu, L., Deivasigamani, S. et al. 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). https://doi.org/10.1038/s41391-024-00830-2

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  • DOI: https://doi.org/10.1038/s41391-024-00830-2

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