Amanika Kumar1, Shannon Sheedy2, Bohyun Kim2, Rudy Suidan3, Debra M Sarasohn4, Ines Nikolovski4, Yulia Lakhman4, Michaela E McGree5, Amy L Weaver5, Dennis Chi3, William A Cliby6. 1. Division of Gynecologic Surgery, Mayo Clinic, Rochester, MN, United States of America. Electronic address: kumar.amanika@mayo.edu. 2. Department of Radiology, Mayo Clinic, Rochester, MN, United States of America. 3. Department of Gynecologic Oncology, Memorial Sloan Kettering Cancer Center, NYC, NY, United States of America. 4. Department of Radiology, Memorial Sloan Kettering Cancer Center, NYC, NY, United States of America. 5. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States of America. 6. Division of Gynecologic Surgery, Mayo Clinic, Rochester, MN, United States of America.
Abstract
OBJECTIVE: Treatment planning requires accurate estimation of surgical complexity (SC) and residual disease (RD) at primary debulking surgery (PDS) for advanced ovarian cancer (OC). We sought to independently validate two published computed tomography (CT) prediction models. METHODS: We included stage IIIC/IV OC patients who underwent PDS from 2003 to 2011. Two prediction models which included imaging and clinical variables to predict RD > 1 and any gross RD, respectively, were applied to our cohort. Two radiologists scored CTs. Discrimination was estimated using the c-index and calibration were assessed by comparing the observed and predicted estimates. RESULTS: The validation cohort consisted of 276 patients; median age of the cohort was 64 years old and majority had serous histology. The validation and model development cohorts were similar in terms of baseline characteristics, however the RD rates differed between cohorts (9.4% vs 25.4% had RD >1 cm; 50.7% vs. 66.6% had gross RD). Model 1, the model to predict RD >1 cm, did not validate well. The c-index of 0.653 for the validation cohort was lower than reported in the development cohort (0.758) and the model over-predicted the proportion with RD >1 cm. The second model to predict gross RD had excellent discrimination with a c-index of 0.762. CONCLUSIONS: We are able to validate a CT model to predict presence of gross RD in an independent center; the separate model to predict RD >1 cm did not validate. Application of the model to predict gross RD can help with clinical decision making in advanced ovarian cancer.
OBJECTIVE: Treatment planning requires accurate estimation of surgical complexity (SC) and residual disease (RD) at primary debulking surgery (PDS) for advanced ovarian cancer (OC). We sought to independently validate two published computed tomography (CT) prediction models. METHODS: We included stage IIIC/IV OC patients who underwent PDS from 2003 to 2011. Two prediction models which included imaging and clinical variables to predict RD > 1 and any gross RD, respectively, were applied to our cohort. Two radiologists scored CTs. Discrimination was estimated using the c-index and calibration were assessed by comparing the observed and predicted estimates. RESULTS: The validation cohort consisted of 276 patients; median age of the cohort was 64 years old and majority had serous histology. The validation and model development cohorts were similar in terms of baseline characteristics, however the RD rates differed between cohorts (9.4% vs 25.4% had RD >1 cm; 50.7% vs. 66.6% had gross RD). Model 1, the model to predict RD >1 cm, did not validate well. The c-index of 0.653 for the validation cohort was lower than reported in the development cohort (0.758) and the model over-predicted the proportion with RD >1 cm. The second model to predict gross RD had excellent discrimination with a c-index of 0.762. CONCLUSIONS: We are able to validate a CT model to predict presence of gross RD in an independent center; the separate model to predict RD >1 cm did not validate. Application of the model to predict gross RD can help with clinical decision making in advanced ovarian cancer.
Authors: Alli M Straubhar; Olga T Filippova; Renee A Cowan; Yulia Lakhman; Debra M Sarasohn; Ines Nikolovski; Jean M Torrisi; Weining Ma; Nadeem R Abu-Rustum; Ginger J Gardner; Yukio Sonoda; Oliver Zivanovic; Dennis S Chi; Kara Long Roche Journal: Gynecol Oncol Date: 2020-06-06 Impact factor: 5.482