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

Abstract

Objective 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. Methods 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. Results Of the 3882 patients enrolled in GOG 182, 1,480 had complete clinical data available for the analysis. The training data set consisted of 1,007 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% C.I.). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88 95% CI) Conclusions 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.

Pre-diagnosis meat intake and cooking method and ovarian cancer survival: results from the Ovarian Cancer Follow-Up Study (OOPS)

“…3 The 5-year survival rate remains poor at less than 50% in high income countries. 4 Previous studies have established several factors related to the prognosis of OC, including the clinical stage, 5 residual tumor size, 6,7 and level of neoplastic differentiation. 8 These prognostic factors have been familiar to the public but are difficult to modify.…”

New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach

“…Traditionally, management is primary debulking surgery (PDS) followed by first-line platinum-based chemotherapy, usually with a doublet of platinum derivative and taxane [4]. The main prognostic factors identified for clinical outcome in patients with advanced ovarian cancer are tumor stage and mostly residual disease (RD) after PDS, with the goal of surgery being no visible tumor residue (RD = 0) [5,6]. Epithelial ovarian tumors are considered highly heterogeneous with different histological subtypes; based on pathological and molecular characteristics, these tumors have been grouped in type I or II [7,8].…”

Evolving Concepts in the Management of Newly Diagnosed Epithelial Ovarian Cancer

“…Validated models can predict the likelihood of achieving complete primary cytoreduction on the basis of clinical factors. 17 Other models have incorporated laparoscopic assessment of disease burden. 18,19 In addition, molecular markers related to transforming growth factor b pathway activation and invasive mesenchymal biology can predict for a lower likelihood of achieving complete cytoreduction, but they are not yet sufficiently robust to drive surgical decisions, 20 and we await the emergence of models that integrate molecular and clinical factors.…”