C A Hamilton1, A Miller2, Y Casablanca3, N S Horowitz4, B Rungruang5, T C Krivak6, S D Richard7, N Rodriguez8, M J Birrer9, F J Backes10, M A Geller11, M Quinn12, M J Goodheart13, D G Mutch14, J J Kavanagh15, G L Maxwell16, M A Bookman17. 1. Gynecologic Cancer Center of Excellence, John P. Murtha Cancer Center, Walter Reed National Military Medical Center and Uniformed Services University of the Health Sciences, Bethesda, MD, United States. Electronic address: chad.a.hamilton2.mil@mail.mil. 2. NRG Oncology Statistics and Data Management Center/Gynecologic Oncology Group, Statistical and Data Center, Roswell Park Cancer Institute, Buffalo, NY, United States. 3. Gynecologic Cancer Center of Excellence, John P. Murtha Cancer Center, Walter Reed National Military Medical Center and Uniformed Services University of the Health Sciences, Bethesda, MD, United States. 4. Division of Gynecologic Oncology, Brigham & Women's Hospital, Boston, MA, United States. 5. Division of Gynecologic Oncology, Medical College of Georgia at Augusta University, Augusta, GA, United States. 6. Division of Gynecologic Oncology, Western Pennsylvania Hospital, Pittsburgh, PA, United States. 7. Division of Gynecologic Oncology, Hahnemann University Hospital/Drexel University College of Medicine, Philadelphia, PA, United States. 8. Division of Gynecologic Oncology, Loma Linda University Medical Center, Loma Linda, CA, United States. 9. Gillette Center for Gynecologic Oncology, Massachusetts General Hospital, Boston, MA, United States. 10. Division of Gynecologic Oncology, Ohio State University Wexner Medical Center and James Cancer Hospital, Columbus, OH, United States. 11. Gynecology and Women's Health, University of Minnesota, Minneapolis, MN, United States. 12. Gynaecological Oncology, ANZGOG, Royal Women's Hospital and University of Melbourne, Australia. 13. Gynecologic Oncology, University of Iowa, Iowa City, IA, United States. 14. Gynecologic Oncology, Washington University, St. Louis, MO, United States. 15. MD Anderson Cancer Center, Houston, TX, United States. 16. Inova Fairfax Hospital Department of Obstetrics and Gynecology, Inova Schar Cancer Institute, Falls Church, VA, United States. 17. US Oncology Research and Arizona Oncology, Tucson, AZ, United States.
Abstract
OBJECTIVE: To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. METHODS: Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). RESULTS: The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. CONCLUSIONS: The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.
RCT Entities:
OBJECTIVE: To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. METHODS: Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). RESULTS: The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. CONCLUSIONS: The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.
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