Suppr超能文献

用于预测非手术治疗的EGFR阳性局部晚期老年食管癌患者生存情况的列线图的开发与内部验证

Development and internal validation of a nomogram for predicting survival of nonoperative EGFR-positive locally advanced elderly esophageal cancers.

作者信息

Wang Jiayang, Peng Jin, Luo Honglei, Song Yaqi

机构信息

Department of Radiation Oncology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.

出版信息

Front Oncol. 2023 May 12;13:1097907. doi: 10.3389/fonc.2023.1097907. eCollection 2023.

Abstract

PURPOSE

This study aims to develop and validate a prediction model for non-operative, epidermal growth factor receptor (EGFR)-positive, locally advanced elderly esophageal cancer (LAEEC).

METHODS

A total of 80 EGFR-positive LAEEC patients were included in the study. All patients underwent radiotherapy, while 41 cases received icotinib concurrent systemic therapy. A nomogram was established using univariable and multivariable Cox analyses. The model's efficacy was assessed through area under curve (AUC) values, receiver operating characteristic (ROC) curves at different time points, time-dependent AUC (tAUC), calibration curves, and clinical decision curves. Bootstrap resampling and out-of-bag (OOB) cross-validation methods were employed to verify the model's robustness. Subgroup survival analysis was also conducted.

RESULTS

Univariable and multivariable Cox analyses revealed that icotinib, stage, and ECOG score were independent prognostic factors for LAEEC patients. The AUCs of model-based prediction scoring (PS) for 1-, 2-, and 3-year overall survival (OS) were 0.852, 0.827, and 0.792, respectively. Calibration curves demonstrated that the predicted mortality was consistent with the actual mortality. The time-dependent AUC of the model exceeded 0.75, and the internal cross-validation calibration curves showed good agreement between predicted and actual mortality. Clinical decision curves indicated that the model had a substantial net clinical benefit within a threshold probability range of 0.2 to 0.8. Model-based risk stratification analysis demonstrated the model's excellent ability to distinguish survival risk. Further subgroup analyses showed that icotinib significantly improved survival in patients with stage III and ECOG score of 1 (HR 0.122, P<0.001).

CONCLUSIONS

Our nomogram model effectively predicts the overall survival of LAEEC patients, and the benefits of icotinib were found in the clinical stage III population with good ECOG scores.

摘要

目的

本研究旨在开发并验证一种针对非手术治疗的、表皮生长因子受体(EGFR)阳性的局部晚期老年食管癌(LAEEC)的预测模型。

方法

本研究共纳入80例EGFR阳性的LAEEC患者。所有患者均接受了放疗,其中41例患者接受了埃克替尼同步全身治疗。使用单变量和多变量Cox分析建立了列线图。通过曲线下面积(AUC)值、不同时间点的受试者工作特征(ROC)曲线、时间依赖性AUC(tAUC)、校准曲线和临床决策曲线来评估模型的疗效。采用Bootstrap重采样和袋外(OOB)交叉验证方法来验证模型的稳健性。还进行了亚组生存分析。

结果

单变量和多变量Cox分析显示,埃克替尼、分期和美国东部肿瘤协作组(ECOG)评分是LAEEC患者的独立预后因素。基于模型的预测评分(PS)对1年、2年和3年总生存(OS)的AUC分别为0.852、0.827和0.792。校准曲线表明预测死亡率与实际死亡率一致。模型的时间依赖性AUC超过0.75,内部交叉验证校准曲线显示预测死亡率与实际死亡率之间具有良好的一致性。临床决策曲线表明,在0.2至0.8的阈值概率范围内,该模型具有显著的净临床获益。基于模型的风险分层分析证明了该模型具有出色的区分生存风险的能力。进一步的亚组分析显示,埃克替尼显著改善了III期且ECOG评分为1的患者的生存(风险比0.122,P<0.001)。

结论

我们的列线图模型有效地预测了LAEEC患者的总生存,并且在ECOG评分良好的临床III期人群中发现了埃克替尼的益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c413/10213387/cc9fb88261bc/fonc-13-1097907-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验