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基于网络的Nomograms 模型的建立与验证:用于精准预测完全切除的非小细胞肺癌患者的特定部位复发的条件风险:一项多机构研究。

Development and Validation of Web-Based Nomograms to Precisely Predict Conditional Risk of Site-Specific Recurrence for Patients With Completely Resected Non-small Cell Lung Cancer: A Multiinstitutional Study.

机构信息

Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.

出版信息

Chest. 2018 Sep;154(3):501-511. doi: 10.1016/j.chest.2018.04.040. Epub 2018 Jun 18.

Abstract

BACKGROUND

There is currently no consensus regarding the optimal postoperative follow-up strategy for patients with completely resected non-small cell lung cancer (NSCLC). We aimed to develop web-based nomograms to precisely predict site-specific postoperative recurrence in patients with NSCLC and to guide individual surveillance strategies including when to follow up and what diagnostic tests to perform.

METHODS

We investigated the pattern of recurrence in a series of 2,017 patients with NSCLC (squamous cell carcinoma and nonlepidic invasive adenocarcinoma) who underwent complete surgical resection at Fudan University Shanghai Cancer Center (development cohort), and developed web-based clinicopathologic prediction models for conditional risk of site-specific recurrence based on Cox regression. The variables used in the analysis included sex, age, smoking history, tumor size, tumor histology, lymphovascular invasion, visceral pleural invasion, and pathologic TNM stage. A separate cohort of 3,308 patients with NSCLC from Shanghai Chest Hospital was used for external validation.

RESULTS

In the development cohort and the external validation cohort for the established nomograms to predict overall recurrence, thorax recurrence, abdomen recurrence, neck recurrence, brain recurrence, and bone recurrence, the C-statistics of Harrell et al were 0.743 and 0.748, 0.728 and 0.703, 0.760 and 0.749, 0.779 and 0.757, 0.787 and 0.784, and 0.777 and 0.739, respectively. The calibration plots showed optimal agreement between nomogram-predicted 3-year recurrence-free survival and actual 3-year recurrence-free survival.

CONCLUSIONS

These user-friendly nomograms can precisely predict site-specific recurrence in patients with completely resected NSCLC, based on clinicopathologic features. They may help physicians to make individual postoperative follow-up plans.

摘要

背景

目前对于完全切除的非小细胞肺癌(NSCLC)患者,术后随访策略仍存在争议。我们旨在建立基于网络的列线图,以准确预测 NSCLC 患者的特定部位术后复发,并指导个体化监测策略,包括何时进行随访以及进行哪些诊断性检查。

方法

我们对复旦大学附属肿瘤医院(研发队列) 2017 例完全手术切除的 NSCLC(鳞癌和非贴壁型浸润性腺癌)患者的复发模式进行了研究,并基于 Cox 回归分析,建立了基于临床病理特征的预测特定部位复发风险的网络列线图预测模型。分析中使用的变量包括性别、年龄、吸烟史、肿瘤大小、肿瘤组织学、脉管侵犯、脏层胸膜侵犯和病理 TNM 分期。另一项来自上海胸科医院的 3308 例 NSCLC 患者队列用于外部验证。

结果

在研发队列和外部验证队列中,建立的预测总体复发、胸部复发、腹部复发、颈部复发、脑部复发和骨复发的列线图的 Harrell 等 C 统计量分别为 0.743 和 0.748、0.728 和 0.703、0.760 和 0.749、0.779 和 0.757、0.787 和 0.784 以及 0.777 和 0.739。校准图显示了列线图预测的 3 年无复发生存率与实际 3 年无复发生存率之间的最佳一致性。

结论

这些易于使用的列线图可以基于临床病理特征准确预测完全切除的 NSCLC 患者的特定部位复发,它们可能有助于医生制定个体化的术后随访计划。

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