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Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study
Journal article   Open access   Peer reviewed

Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study

Neil S Horowitz, G Larry Maxwell, Austin Miller, Chad A Hamilton, Bunja Rungruang, Noah Rodriguez, Scott D Richard, Thomas C Krivak, Jeffrey M Fowler, David G Mutch, …
Gynecologic Oncology, Vol.148(1), pp.49-55
01/2018
DOI: 10.1016/j.ygyno.2017.10.011
PMCID: PMC5962447
PMID: 29174555
url
https://doi.org/10.1016/j.ygyno.2017.10.011View
Published (Version of record) Open Access

Abstract

Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions. Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets. Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p<0.001), stage (p=0.009), CA125 (p<0.001), ascites (p<0.001), and stage-age interaction (p=0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI). We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. •A predictive model for R0 was developed and validated.•Variables included disease score, stage, CA125, ascites, and stage-age interaction.•Model alone should not determine management of advanced stage ovarian cancer.
 Ovarian Cancer Microscopic residual

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