早期子宫内膜癌淋巴管间隙侵犯的新型预测模型

IF 1 Q4 OBSTETRICS & GYNECOLOGY Turkish Journal of Obstetrics and Gynecology Pub Date : 2024-03-04 DOI:10.4274/tjod.galenos.2024.92597
İbrahim Taşkum, Muhammed Hanifi Bademkıran, Furkan Çetin, Seyhun Sucu, Erkan Yergin, Özcan Balat, Halil Özkaya, Evren Uzun
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引用次数: 0

摘要

摘要使用包含子宫内膜癌预后因素的预测模型,预测早期(1-2 期)子宫内膜癌淋巴管间隙浸润(LVSI)阳性率:我们纳入了2010年至2020年期间在本诊所接受全子宫切除术和双侧输卵管切除术并行盆腔-主动脉旁淋巴结切除术的461例患者,作为推测的早期子宫内膜癌的主要治疗方法。此外,我们还对所有手术标本进行了组织病理学检查,以确定 LVSI 的阳性或阴性,并根据这些病理学结果将患者分为两组。记录并分析了患者的年龄、绝经状态、组织学类型(1-2 型)、组织学分级(1-2-3 级)、子宫肌层浸润深度和腹膜细胞学结果等临床病理学和人口统计学特征。卢斯算法确定了观察结果与预测结果之间的关系。通过计算C指数来评估算法之间的区别:结果:LVSI阳性与高龄、绝经、2型EC、组织学分级晚期、恶性腹腔细胞学、宫颈受累以及肿瘤超过子宫肌层深度的50%(p结论:我们的预测模型有助于预测LVSI阳性:我们的预测模型有助于根据不同的预后因素预测 LVSI。包括在提名图中的预后因素与LVSI显著相关,尤其是子宫肌层浸润深度超过50%、组织学分级晚期和恶性腹膜细胞学。
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A novel predictive model of lymphovascular space invasion in early-stage endometrial cancer.

Objective: To predict lymphovascular space invasion (LVSI) positivity in early-stage (stage 1-2) endometrial cancer (EC) using a predictive model with prognostic factors of EC.

Materials and methods: We included 461 patients who underwent total hysterectomy and bilateral salpingo-oophorectomy with pelvic-paraaortic lymphadenectomy as the primary treatment for presumed early-stage EC at our clinic between 2010 and 2020. Moreover, all surgical specimens were examined histopathologically for the positivity or negativity of LVSI, and the patients were divided into two groups based on these pathologic outcomes. Age, menopausal status, histological type (type 1-2), histological grade (grades 1-2-3), depth of myometrial invasion, and peritoneal cytology results were recorded and analyzed as clinicopathological and demographic characteristics of the patients. The Loess algorithm determined the relationship between the observed and predicted outcomes. The distinction between the algorithms was evaluated by calculating the C-index.

Results: LVSI positivity was significantly associated with advanced age, menopause, type 2 EC, advanced histological grade, malignant peritoneal cytology, cervical involvement, and a tumor exceeding 50% of the myometrial depth (p<0.001, respectively). Remarkably, LVSI was most strongly associated with three explanatory variables: 1- More than 50% myometrial invasion [odds ratio (OR): 3.78; 95% confidence interval (CI): 1.80-7.60], 2- Advanced histological grade [OR=1.98 (1.20-3.20) 95% CI], 3- Malignant peritoneal cytology [OR= 3.06 (1.40-6.30) 95% CI]. The penalized maximum likelihood estimation model correctly classified 87% of the included patients (C-index: 0.876).

Conclusion: Our predictive model may help predict LVSI based on different prognostic factors. The prognostic factors included in the nomogram were significantly associated with LVSI, particularly myometrial invasion depth of more than 50%, advanced histological grade, and malignant peritoneal cytology.

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