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.
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.
Authors: N Colombo; C Creutzberg; F Amant; T Bosse; A González-Martín; J Ledermann; C Marth; R Nout; D Querleu; M R Mirza; C Sessa Journal: Ann Oncol Date: 2015-12-02 Impact factor: 32.976
Authors: Andrea Mariani; Sean C Dowdy; William A Cliby; Bobbie S Gostout; Monica B Jones; Timothy O Wilson; Karl C Podratz Journal: Gynecol Oncol Date: 2008-03-04 Impact factor: 5.482
Authors: Jacques Ferlay; Isabelle Soerjomataram; Rajesh Dikshit; Sultan Eser; Colin Mathers; Marise Rebelo; Donald Maxwell Parkin; David Forman; Freddie Bray Journal: Int J Cancer Date: 2014-10-09 Impact factor: 7.396
Authors: Yovanni Casablanca; Guisong Wang; Heather A Lankes; Chunqiao Tian; Nicholas W Bateman; Caela R Miller; Nicole P Chappell; Laura J Havrilesky; Amy Hooks Wallace; Nilsa C Ramirez; David S Miller; Julie Oliver; Dave Mitchell; Tracy Litzi; Brian E Blanton; William J Lowery; John I Risinger; Chad A Hamilton; Neil T Phippen; Thomas P Conrads; David Mutch; Katherine Moxley; Roger B Lee; Floor Backes; Michael J Birrer; Kathleen M Darcy; George Larry Maxwell Journal: Cancers (Basel) Date: 2022-08-23 Impact factor: 6.575