子宫内膜癌
阿卡克信息准则
医学
接收机工作特性
阶段(地层学)
一致性
癌症
妇科
肿瘤科
内科学
统计
生物
数学
古生物学
作者
Mayumi Kobayashi-Kato,Erisa Fujii,Yuka Asami,Yuka Ahiko,Kengo Hiranuma,Yasuhisa Terao,Koji Matsumoto,Mitsuya Ishikawa,Takashi Kohno,Tomoyasu Kato,Kouya Shiraishi,Hiroshi Yoshida
标识
DOI:10.1016/j.ygyno.2023.09.011
摘要
Molecular classification was introduced in endometrial cancer staging following the transition of the International Federation of Gynecology and Obstetrics (FIGO) 2008 to FIGO2023. In the early stages, p53 abnormal endometrial carcinoma with myometrial involvement was upstaged to stage IICm, in addition to the downstaging of POLE mutation endometrial cancer to stage IAm. This study compared the goodness of fit and discriminatory ability of FIGO2008, FIGO2023 without molecular classification (FIGO2023), and FIGO2023 with molecular classification (FIGO2023m); no study has been externally validated to date.The study included 265 patients who underwent initial surgery at the National Cancer Center Hospital between 1997 and 2019 and were pathologically diagnosed with endometrial cancer. The three classification systems were compared using Harrell's concordance index (C-index), Akaike information criterion (AIC), and time-dependent receiver operating characteristic (ROC) curves. A higher C-index score and a lower AIC value indicated a more accurate model.Among the three classification systems, FIGO2023m had the lowest AIC value (FIGO2023m: 455.925; FIGO2023: 459.162; FIGO2008: 457.901), highest C-index (FIGO2023m: 0.768; FIGO2023: 0.743; FIGO2008: 0.740), and superior time-dependent ROC curves within 1 year after surgical resection. In the stage IIIC, patients with p53 abnormalities had considerably lower 5-year overall survival than those with a p53 wild-type pattern (24.3% vs. 83.7%, p = 0.0005).FIGO2023m had the best discriminatory ability compared with FIGO2008 and FIGO2023. Even in advanced stages, p53 status was a poor prognostic factor. When feasible, molecular subtypes can be added to the staging criteria to allow better prognostic prediction in all stages.
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