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首页> 外文期刊>British Journal of Cancer >Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters
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Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters

机译:基于经典病理学和免疫学参数的子宫内膜癌区域或远处复发预测模型

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Background: Adjuvant therapy increases disease-free survival in endometrial cancer (EC), but has no impact on overall survival and negatively influences the quality of life. We investigated the discriminatory power of classical and immunological predictors of recurrence in a cohort of EC patients and confirmed the findings in an independent validation cohort. Methods: We reanalysed the data from 355 EC patients and tested our findings in an independent validation cohort of 72 patients with EC. Predictors were selected and Harrell's C-index for concordance was used to determine discriminatory power for disease-free survival in the total group and stratified for histological subtype. Results: Predictors for recurrence were FIGO stage, lymphovascular space invasion and numbers of cytotoxic and memory T-cells. For high risk cancer, cytotoxic or memory T-cells predicted recurrence as well as a combination of FIGO stage and lymphovascular space invasion (C-index 0.67 and 0.71 vs 0.70). Recurrence was best predicted when FIGO stage, lymphovascular space invasion and numbers of cytotoxic cells were used in combination (C-index 0.82). Findings were confirmed in the validation cohort. Conclusions: In high-risk EC, clinicopathological or immunological variables can predict regional or distant recurrence with equal accuracy, but the use of these variables in combination is more powerful.
机译:背景:辅助治疗可提高子宫内膜癌(EC)的无病生存期,但对总体生存期无影响,并且对生活质量产生负面影响。我们调查了经典和免疫学预测复发的EC患者队列的辨别力,并在独立的验证队列中证实了这一发现。方法:我们重新分析了355名EC患者的数据,并在72名EC患者的独立验证队列中测试了我们的发现。选择了预测变量,并使用Harrell的C指数进行一致性确定整个组中无病生存的判别力,并对组织学亚型进行分层。结果:复发的预测因素是FIGO分期,淋巴血管间隙浸润以及细胞毒性和记忆T细胞的数量。对于高危癌症,细胞毒性或记忆性T细胞可预测复发,以及FIGO分期和淋巴管腔浸润的组合(C指数0.67和0.71对0.70)。结合使用FIGO分期,淋巴血管间隙浸润和细胞毒性细胞数量,可以最好地预测复发(C指数0.82)。在验证队列中确认了发现。结论:在高危EC中,临床病理或免疫学变量可以相同的准确性预测区域或远处复发,但将这些变量组合使用更有效。

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