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Clinicopathological analysis of patients with molecularly confirmed stage I adult granulosa cell tumors and prediction of recurrence

医学 列线图 阶段(地层学) 内科学 肿瘤科 妇科 接收机工作特性 胃肠病学 生物 古生物学
作者
Yilin Liu,Jingci Chen,Zhaohui Lu,Xiaoyan Chang,Pengyan Wang,Heng Ma,Xianlong Chen,Shengwei Mo,Zhuoyao Lv,Xinxin Mao,Liju Zong,Shuangni Yu,Jie Chen
出处
期刊:Gynecologic Oncology [Elsevier BV]
卷期号:176: 106-114
标识
DOI:10.1016/j.ygyno.2023.07.007
摘要

Adult granulosa cell tumors (AGCTs) are rare malignancies that accounts for approximately 1% of ovarian neoplasms. As there are currently no well-recognized models for predicting relapse-free survival (RFS), we performed a clinicopathological analysis to identify risk factors for AGCT recurrence.We investigated 130 patients with pathologically diagnosed AGCT as confirmed by the presence of the characteristic FOXL2 C402G mutation.Most patients had International Federation of Gynecology and Obstetrics stage I disease (n = 122, 95.3%). The 10-year RFS rate was 31.4% (22/70) and mean 10-year RFS was 74.4 (95% CI, 65.2-83.7) months. Ten patients experienced recurrence beyond the 10-year follow-up period. Undergoing fertility sparing surgery, an estrogen receptor-α (ERα) score (>0.25), and a Ki-67 index >15% were independent risk factors for recurrence in patients with stage I disease (bias-corrected C-index: 0.776). We constructed a nomogram with well-fitting calibration plots; the areas under the curve (AUCs) for 5-, and 10-year RFS prediction were 0.883 and 0.906 respectively. A simplified model with 3 predictive factors (ERα score, Ki-67 index, and primary surgical procedure) and 2 risk stratification subgroups (low- and high-risk) was constructed; its AUCs for 5-, and 10-year RFS prediction were 0.825 and 0.850 respectively. Kaplan-Meier survival curves showed significant differences in 10-year RFS between the low- and high-risk groups (p < 0.001).The type of primary surgical procedure, ERα score, and Ki-67 index are independent predictors of recurrence for patients with stage I AGCT. Our predictive model based on these factors showed good performance.

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