%0 Journal Article %A Sokbom Kang %A Zachary Thompson %A E. Claire McClung %A Reem Abdallah %A Jae K. Lee %A Jesus Gonzalez-Bosquet %A Robert M. Wenham %A Hye Sook Chon %T Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer %D 2018 %R 10.1097/IGC.0000000000001152 %J International Journal of Gynecologic Cancer %P 260-266 %V 28 %N 2 %X Objective This study aimed to develop a prediction model for lymph node metastasis using a gene expression signature in patients with endometrioid-type endometrial cancer.Methods Newly diagnosed endometrioid-type endometrial cancer cases in which the patients had undergone lymphadenectomy during a surgical staging procedure were identified from a national dataset (N = 330). Clinical and pathologic data were extracted from patient medical records, and gene expression datasets of their tumors were used to create a 12-gene predictive model for lymph node metastasis. We used principal components analysis on a training set (n = 110) to develop multivariate logistic models to predict low-risk patients having a probability of lymph node metastasis of less than 4%. The model with the highest prediction performance was selected for an evaluation set (n = 112), which, in turn, was validated in an independent validation set (n = 108).Results The model applied to the evaluation set showed 100% sensitivity (90% confidence interval [CI], 74%–100%) and 42% specificity (90% CI, 34%–51%), which resulted in 100% negative predictive value (90% CI, 89%–100%). In the validation set, we confirmed that the model consistently showed 100% sensitivity (90% CI, 88%–100%), 42% specificity (90% CI, 32%–50%), and 100% negative predictive value (90% CI, 88%–100%).Conclusions Our 12-gene signature model is a useful tool for the identification of patients with endometrioid-type endometrial cancer at low risk of lymph node metastasis, particularly given that it can be used to analyze histologic tissue before surgery and used to tailor surgical options. %U https://ijgc.bmj.com/content/ijgc/28/2/260.full.pdf