Robert T Dess1, Krithika Suresh2, Michael J Zelefsky3, Stephen J Freedland4,5, Brandon A Mahal6, Matthew R Cooperberg7, Brian J Davis8, Eric M Horwitz9, Martha K Terris10, Christopher L Amling11, William J Aronson12, Christopher J Kane13, William C Jackson1, Jason W D Hearn1, Curtiland Deville14, Theodore L DeWeese14, Stephen Greco14, Todd R McNutt14, Daniel Y Song14, Yilun Sun1,15, Rohit Mehra16, Samuel D Kaffenberger17, Todd M Morgan17, Paul L Nguyen18, Felix Y Feng7,19,20, Vidit Sharma21, Phuoc T Tran14, Bradley J Stish8, Thomas M Pisansky8, Nicholas G Zaorsky22, Fabio Ynoe Moraes23, Alejandro Berlin24,25, Antonio Finelli26,27, Nicola Fossati28, Giorgio Gandaglia28, Alberto Briganti28, Peter R Carroll7, R Jeffrey Karnes21, Michael W Kattan29, Matthew J Schipper1,15, Daniel E Spratt1. 1. Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor. 2. School of Public Health, University of Colorado, Denver. 3. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York. 4. Division of Urology, Department of Surgery, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California. 5. Durham VA Medical Center, Durham, North Carolina. 6. Harvard Radiation Oncology Program, Massachusetts General Hospital, Boston. 7. Department of Urology, University of California, San Francisco, Helen Diller Family Comprehensive Cancer Center. 8. Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota. 9. Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania. 10. Section of Urology, Medical College of Georgia, Augusta, Georgia. 11. Division of Urology, Department of Surgery, Oregon Health and Science University, Portland. 12. Department of Urology, University of California, Los Angeles, School of Medicine. 13. Department of Urology, University of California, San Diego, Health System. 14. Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland. 15. Department of Biostatistics, University of Michigan, Ann Arbor. 16. Department of Pathology, University of Michigan, Ann Arbor. 17. Department of Urology, University of Michigan, Ann Arbor. 18. Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts. 19. Department of Radiation Oncology, University of California, San Francisco. 20. Department of Medicine, University of California, San Francisco. 21. Department of Urology, Mayo Clinic, Rochester, Minnesota. 22. Department of Radiation Oncology, Penn State Cancer Institute, Hershey, Pennsylvania. 23. Department of Oncology, Queen's University, Kingston, Ontario, Canada. 24. Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. 25. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada. 26. Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. 27. Division of Urology, University of Toronto, Toronto, Ontario, Canada. 28. Department of Urology, Scientific Institute and University Vita-Salute San Raffaele Hospital, Milan, Italy. 29. Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio.
Abstract
Importance: In 2016, the American Joint Committee on Cancer (AJCC) established criteria to evaluate prediction models for staging. No localized prostate cancer models were endorsed by the Precision Medicine Core committee, and 8th edition staging was based on expert consensus. Objective: To develop and validate a pretreatment clinical prognostic stage group system for nonmetastatic prostate cancer. Design, Setting, and Participants: This multinational cohort study included 7 centers from the United States, Canada, and Europe, the Shared Equal Access Regional Cancer Hospital (SEARCH) Veterans Affairs Medical Centers collaborative (5 centers), and the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (43 centers) (the STAR-CAP cohort). Patients with cT1-4N0-1M0 prostate adenocarcinoma treated from January 1, 1992, to December 31, 2013 (follow-up completed December 31, 2017). The STAR-CAP cohort was randomly divided into training and validation data sets; statisticians were blinded to the validation data until the model was locked. A Surveillance, Epidemiology, and End Results (SEER) cohort was used as a second validation set. Analysis was performed from January 1, 2018, to November 30, 2019. Exposures: Curative intent radical prostatectomy (RP) or radiotherapy with or without androgen deprivation therapy. Main Outcomes and Measures: Prostate cancer-specific mortality (PCSM). Based on a competing-risk regression model, a points-based Score staging system was developed. Model discrimination (C index), calibration, and overall performance were assessed in the validation cohorts. Results: Of 19 684 patients included in the analysis (median age, 64.0 [interquartile range (IQR), 59.0-70.0] years), 12 421 were treated with RP and 7263 with radiotherapy. Median follow-up was 71.8 (IQR, 34.3-124.3) months; 4078 (20.7%) were followed up for at least 10 years. Age, T category, N category, Gleason grade, pretreatment serum prostate-specific antigen level, and the percentage of positive core biopsy results among biopsies performed were included as variables. In the validation set, predicted 10-year PCSM for the 9 Score groups ranged from 0.3% to 40.0%. The 10-year C index (0.796; 95% CI, 0.760-0.828) exceeded that of the AJCC 8th edition (0.757; 95% CI, 0.719-0.792), which was improved across age, race, and treatment modality and within the SEER validation cohort. The Score system performed similarly to individualized random survival forest and interaction models and outperformed National Comprehensive Cancer Network (NCCN) and Cancer of the Prostate Risk Assessment (CAPRA) risk grouping 3- and 4-tier classification systems (10-year C index for NCCN 3-tier, 0.729; for NCCN 4-tier, 0.746; for Score, 0.794) as well as CAPRA (10-year C index for CAPRA, 0.760; for Score, 0.782). Conclusions and Relevance: Using a large, diverse international cohort treated with standard curative treatment options, a proposed AJCC-compliant clinical prognostic stage group system for prostate cancer has been developed. This system may allow consistency of reporting and interpretation of results and clinical trial design.
Importance: In 2016, the American Joint Committee on Cancer (AJCC) established criteria to evaluate prediction models for staging. No localized prostate cancer models were endorsed by the Precision Medicine Core committee, and 8th edition staging was based on expert consensus. Objective: To develop and validate a pretreatment clinical prognostic stage group system for nonmetastatic prostate cancer. Design, Setting, and Participants: This multinational cohort study included 7 centers from the United States, Canada, and Europe, the Shared Equal Access Regional Cancer Hospital (SEARCH) Veterans Affairs Medical Centers collaborative (5 centers), and the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (43 centers) (the STAR-CAP cohort). Patients with cT1-4N0-1M0 prostate adenocarcinoma treated from January 1, 1992, to December 31, 2013 (follow-up completed December 31, 2017). The STAR-CAP cohort was randomly divided into training and validation data sets; statisticians were blinded to the validation data until the model was locked. A Surveillance, Epidemiology, and End Results (SEER) cohort was used as a second validation set. Analysis was performed from January 1, 2018, to November 30, 2019. Exposures: Curative intent radical prostatectomy (RP) or radiotherapy with or without androgen deprivation therapy. Main Outcomes and Measures: Prostate cancer-specific mortality (PCSM). Based on a competing-risk regression model, a points-based Score staging system was developed. Model discrimination (C index), calibration, and overall performance were assessed in the validation cohorts. Results: Of 19 684 patients included in the analysis (median age, 64.0 [interquartile range (IQR), 59.0-70.0] years), 12 421 were treated with RP and 7263 with radiotherapy. Median follow-up was 71.8 (IQR, 34.3-124.3) months; 4078 (20.7%) were followed up for at least 10 years. Age, T category, N category, Gleason grade, pretreatment serum prostate-specific antigen level, and the percentage of positive core biopsy results among biopsies performed were included as variables. In the validation set, predicted 10-year PCSM for the 9 Score groups ranged from 0.3% to 40.0%. The 10-year C index (0.796; 95% CI, 0.760-0.828) exceeded that of the AJCC 8th edition (0.757; 95% CI, 0.719-0.792), which was improved across age, race, and treatment modality and within the SEER validation cohort. The Score system performed similarly to individualized random survival forest and interaction models and outperformed National Comprehensive Cancer Network (NCCN) and Cancer of the Prostate Risk Assessment (CAPRA) risk grouping 3- and 4-tier classification systems (10-year C index for NCCN 3-tier, 0.729; for NCCN 4-tier, 0.746; for Score, 0.794) as well as CAPRA (10-year C index for CAPRA, 0.760; for Score, 0.782). Conclusions and Relevance: Using a large, diverse international cohort treated with standard curative treatment options, a proposed AJCC-compliant clinical prognostic stage group system for prostate cancer has been developed. This system may allow consistency of reporting and interpretation of results and clinical trial design.
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