Models to predict outcomes after primary debulking surgery

Independent validation of models to predict suboptimal cytoreduction and gross residual disease

Amanika Kumar, Shannon Sheedy, Bohyun Kim, Rudy Suidan, Debra M. Sarasohn, Ines Nikolovski, Yulia Lakhman, Michaela E. McGree, Amy L. Weaver, Dennis Chi, William Arthur Cliby

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalGynecologic oncology
DOIs
StatePublished - Jan 1 2019

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Ovarian Neoplasms
Tomography
Calibration
Histology
Therapeutics
Radiologists
Clinical Decision-Making

Keywords

  • Advanced ovarian cancer
  • CT scans
  • Cytoreduction
  • Prediction models
  • Residual disease

ASJC Scopus subject areas

  • Oncology
  • Obstetrics and Gynecology

Cite this

Models to predict outcomes after primary debulking surgery : Independent validation of models to predict suboptimal cytoreduction and gross residual disease. / Kumar, Amanika; Sheedy, Shannon; Kim, Bohyun; Suidan, Rudy; Sarasohn, Debra M.; Nikolovski, Ines; Lakhman, Yulia; McGree, Michaela E.; Weaver, Amy L.; Chi, Dennis; Cliby, William Arthur.

In: Gynecologic oncology, 01.01.2019.

Research output: Contribution to journalArticle

Kumar, Amanika ; Sheedy, Shannon ; Kim, Bohyun ; Suidan, Rudy ; Sarasohn, Debra M. ; Nikolovski, Ines ; Lakhman, Yulia ; McGree, Michaela E. ; Weaver, Amy L. ; Chi, Dennis ; Cliby, William Arthur. / Models to predict outcomes after primary debulking surgery : Independent validation of models to predict suboptimal cytoreduction and gross residual disease. In: Gynecologic oncology. 2019.
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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.",
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author = "Amanika Kumar and Shannon Sheedy and Bohyun Kim and Rudy Suidan and Sarasohn, {Debra M.} and Ines Nikolovski and Yulia Lakhman and McGree, {Michaela E.} and Weaver, {Amy L.} and Dennis Chi and Cliby, {William Arthur}",
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T2 - Independent validation of models to predict suboptimal cytoreduction and gross residual disease

AU - Kumar, Amanika

AU - Sheedy, Shannon

AU - Kim, Bohyun

AU - Suidan, Rudy

AU - Sarasohn, Debra M.

AU - Nikolovski, Ines

AU - Lakhman, Yulia

AU - McGree, Michaela E.

AU - Weaver, Amy L.

AU - Chi, Dennis

AU - Cliby, William Arthur

PY - 2019/1/1

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N2 - 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.

AB - 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.

KW - Advanced ovarian cancer

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KW - Cytoreduction

KW - Prediction models

KW - Residual disease

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