OBJECTIVE: The aim of this study was to identify a standard for the evaluation of future models for prediction of lymph node metastasis in endometrial cancer through estimation of performance of well-known surgicopathological models. METHODS: Using the medical records of 947 patients with endometrial cancer who underwent surgical management with lymphadenectomy, we retrospectively assessed the predictive performances of nodal metastasis of currently available models. RESULTS: WE EVALUATED THREE MODELS INCLUDED: 1) a model modified from the Gynecologic Oncology Group (GOG) pilot study; 2) one from the GOG-33 data; and 3) one from Mayo Clinic data. The three models showed similar negative predictive values ranging from 97.1% to 97.4%. Using Bayes' theorem, this can be translated into 2% of negative post-test probability when 10% of prevalence of lymph node metastasis was assumed. In addition, although the negative predictive value was similar among these models, the proportion that was classified as low-risk was significantly different between the studies (56.4%, 44.8%, and 30.5%, respectively; p<0.001). CONCLUSION: The current study suggests that a false negativity of 2% or less should be a goal for determining clinical usefulness of preoperative or intraoperative prediction models for low-risk of nodal metastasis.
OBJECTIVE: The aim of this study was to identify a standard for the evaluation of future models for prediction of lymph node metastasis in endometrial cancer through estimation of performance of well-known surgicopathological models. METHODS: Using the medical records of 947 patients with endometrial cancer who underwent surgical management with lymphadenectomy, we retrospectively assessed the predictive performances of nodal metastasis of currently available models. RESULTS: WE EVALUATED THREE MODELS INCLUDED: 1) a model modified from the Gynecologic Oncology Group (GOG) pilot study; 2) one from the GOG-33 data; and 3) one from Mayo Clinic data. The three models showed similar negative predictive values ranging from 97.1% to 97.4%. Using Bayes' theorem, this can be translated into 2% of negative post-test probability when 10% of prevalence of lymph node metastasis was assumed. In addition, although the negative predictive value was similar among these models, the proportion that was classified as low-risk was significantly different between the studies (56.4%, 44.8%, and 30.5%, respectively; p<0.001). CONCLUSION: The current study suggests that a false negativity of 2% or less should be a goal for determining clinical usefulness of preoperative or intraoperative prediction models for low-risk of nodal metastasis.
Authors: Benjamin E Greer; Wui-Jin Koh; Nadeem Abu-Rustum; Michael A Bookman; Robert E Bristow; Susana M Campos; Kathleen R Cho; Larry Copeland; Marta Ann Crispens; Patricia J Eifel; Warner K Huh; Wainwright Jaggernauth; Daniel S Kapp; John J Kavanagh; John R Lurain; Mark Morgan; Robert J Morgan; C Bethan Powell; Steven W Remmenga; R Kevin Reynolds; Angeles Alvarez Secord; William Small; Nelson Teng Journal: J Natl Compr Canc Netw Date: 2009-05 Impact factor: 11.908
Authors: R C Boronow; C P Morrow; W T Creasman; P J Disaia; S G Silverberg; A Miller; J A Blessing Journal: Obstet Gynecol Date: 1984-06 Impact factor: 7.661
Authors: Sigmund Ytre-Hauge; Jenny A Husby; Inger J Magnussen; Henrica M J Werner; Øyvind O Salvesen; Line Bjørge; Jone Trovik; Ingunn M Stefansson; Helga B Salvesen; Ingfrid S Haldorsen Journal: Int J Gynecol Cancer Date: 2015-03 Impact factor: 3.437