Dataset Information

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, 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). 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.

SUBMITTER: Horowitz NS

PROVIDER: S-EPMC5962447 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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

Horowitz Neil S NS Larry Maxwell G G Miller Austin A Hamilton Chad A CA Rungruang Bunja B Rodriguez Noah N Richard Scott D SD Krivak Thomas C TC Fowler Jeffrey M JM Mutch David G DG Van Le Linda L Lee Roger B RB Argenta Peter P Bender David D Tewari Krishnansu S KS Gershenson David D Java James J JJ Bookman Michael A MA

Gynecologic oncology 20171123 1

<h4>Objective</h4>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.<h4>Methods</h4>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 enro ...[more]

PMID: 29174555

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Project description:PurposeThe clinicopathologic significance of mismatch repair (MMR) defects in endometrioid endometrial cancer (EEC) has not been definitively established. We undertook tumor typing to classify MMR defects to determine if MMR status is prognostic or predictive.MethodsPrimary EECs from NRG/GOG0210 patients were assessed for microsatellite instability (MSI), MLH1 methylation, and MMR protein expression. Each tumor was assigned to one of four MMR classes: normal, epigenetic defect, probable mutation (MMR defect not attributable to MLH1 methylation), or MSI-low. The relationships between MMR classes and clinicopathologic variables were assessed using contingency table tests and Cox proportional hazard models.ResultsA total of 1,024 tumors were assigned to MMR classes. Epigenetic and probable mutations in MMR were significantly associated with higher grade and more frequent lymphovascular space invasion. Epigenetic defects were more common in patients with higher International Federation of Gynecology and Obstetrics stage. Overall, there were no differences in outcomes. Progression-free survival was, however, worse for women whose tumors had epigenetic MMR defects compared with the MMR normal group (hazard ratio, 1.37; P < .05; 95% CI, 1.00 to 1.86). An exploratory analysis of interaction between MMR status and adjuvant therapy showed a trend toward improved progression-free survival for probable MMR mutation cases.ConclusionMMR defects in EECs are associated with a number of well-established poor prognostic indicators. Women with tumors that had MMR defects were likely to have higher-grade cancers and more frequent lymphovascular space invasion. Surprisingly, outcomes in these patients were similar to patients with MMR normal tumors, suggesting that MMR defects may counteract the effects of negative prognostic factors. Altered immune surveillance of MMR-deficient tumors, and other host/tumor interactions, is likely to determine outcomes for patients with MMR-deficient tumors.

Project description:BackgroundBlack women with endometrial cancer are more likely to die of their disease compared with white women with endometrial cancer. These survival disparities persist even when disproportionately worse tumor characteristics among black women are accounted. Receipt of less complete adjuvant treatment among black patients with endometrial cancer could contribute to this disparity.ObjectiveWe assessed the hypothesis that black women with endometrial cancer are less likely than their white counterparts to receive adjuvant treatment within subgroups defined by tumor characteristics in the NRG Oncology/Gynecology Oncology Group 210 Study.Study designOur analysis included 615 black and 4283 white women with endometrial cancer who underwent hysterectomy. Women completed a questionnaire that assessed race and endometrial cancer risk factors. Tumor characteristics were available from pathology reports and central review. We categorized women as low-, intermediate-, or high-risk based on the European Society for Medical Oncology definition. Adjuvant treatment was documented during postoperative visits and was categorized as no adjuvant treatment (54.3%), radiotherapy only (16.5%), chemotherapy only (15.2%), and radiotherapy plus chemotherapy (14.0%). We used polytomous logistic regression to estimate odds ratios and 95% confidence intervals for multivariable-adjusted associations between race and adjuvant treatment in the overall study population and stratified by tumor subtype, stage, or European Society for Medical Oncology risk category.ResultsOverall, black women were more likely to have received chemotherapy only (odds ratio, 1.40; 95% confidence interval, 1.04-1.86) or radiotherapy plus chemotherapy (odds ratio, 2.01; 95% confidence interval, 1.54-2.62) compared with white women in multivariable-adjusted models. No racial difference in the receipt of radiotherapy only was observed. In tumor subtype-stratified models, black women had higher odds of receiving radiotherapy plus chemotherapy than white women when diagnosed with low-grade endometrioid (odds ratio, 2.04; 95% confidence interval, 1.06-3.93) or serous tumors (odds ratio, 1.81; 95% confidence interval, 1.07-3.08). Race was not associated with adjuvant treatment among women who had been diagnosed with other tumor subtypes. In stage-stratified models, we observed no racial differences in the receipt of adjuvant treatment. In models that were stratified by European Society for Medical Oncology risk group, black women with high-risk cancer were more likely to receive radiotherapy plus chemotherapy compared with white women (odds ratio, 1.41; 95% confidence interval, 1.03-1.94).ConclusionContrary to our hypothesis, we observed higher odds of specific adjuvant treatment regimens among black women as compared with white women within specific subgroups of endometrial cancer characteristics.

Dataset Information

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

Publications

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

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