Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: an NRG Oncology / Gynecologic Oncology Group Study. Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: an NRG Oncology / Gynecologic Oncology Group Study

Accurate methods to predict nodal and distant metastasis are needed in endometrioid endometrial cancer (EEC) patients to advance personalized care and reduce both overtreatment and undertreatment. A transcript-based classifier for predicting risk of nodal and distant metastasis in EEC patients was developed, and shown to outperform a panel of clinical and molecular features We used microarrays to detail the gene expression in EEC patients and identified a classifer to predict nodal and distant metastasis Overall design: Frozen primary endometioid endometrial cancer tissues acquired at the time of primary surgical staging undrwent transcriptomic analysis using the Affymetrix U133 Plus 2.0 microarray platform. contributor: GYNCOE contributor: GOG