新辅助治疗后进行食管切除术的原发性食管癌患者中,有病理完全反应和无病理完全反应患者的个体化生存率预测在线工具:两个独立提名图的开发和外部验证。

IF 3.3 Q2 GASTROENTEROLOGY & HEPATOLOGY
Yuqin Cao, Binhao Huang, Han Tang, Dong Dong, Tianzheng Shen, Xiang Chen, Xijia Feng, Jiahao Zhang, Liqiang Shi, Chengqiang Li, Heng Jiao, Lijie Tan, Jie Zhang, Hecheng Li, Yajie Zhang
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引用次数: 0

摘要

研究目的本研究旨在开发和验证针对新辅助治疗和食管切除术后获得病理完全反应(pCR)和未获得病理完全反应(非CCR)的食管癌患者的稳健预测模型:设计:从全国癌症数据库中获取6517名接受新辅助治疗和食管切除术的原发性食管癌患者的临床病理数据,作为培训队列。由 444 名中国患者组成的独立队列作为验证集。针对pCR和非pCR患者分别建立了两种不同的总生存期(OS)多变量Cox模型,并使用基于网络的动态提名图(根据患者输入网站的临床特征预测OS的图形表示)进行展示。校准图、一致性指数和决策曲线分析被用来评估预测模型的校准性、区分度和临床实用性:共有13个和15个变量分别用于预测接受新辅助治疗后进行食管切除术的pCR和非PCR患者的OS。主要预测因素包括人口统计学特征、治疗前临床分期、手术方式、病理信息和术后治疗。对pCR和非pCR患者的预测模型显示出良好的校准性和临床实用性,具有可接受的区分度,超过了目前的肿瘤、结节、转移分期系统:本研究为pCR患者(https://predict-survival.shinyapps.io/pCR-eso/)和非pCR患者(https://predict-survival.shinyapps.io/non-pCR-eso/)开发的基于网络的动态提名图有助于计算接受新辅助治疗和根治性食管切除术患者的OS概率,帮助临床医生和患者做出个性化的治疗决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online tools to predict individualised survival for primary oesophageal cancer patients with and without pathological complete response after neoadjuvant therapy followed by oesophagectomy: development and external validation of two independent nomograms.

Objective: This study aimed to develop and validate robust predictive models for patients with oesophageal cancer who achieved a pathological complete response (pCR) and those who did not (non-pCR) after neoadjuvant therapy and oesophagectomy.

Design: Clinicopathological data of 6517 primary oesophageal cancer patients who underwent neoadjuvant therapy and oesophagectomy were obtained from the National Cancer Database for the training cohort. An independent cohort of 444 Chinese patients served as the validation set. Two distinct multivariable Cox models of overall survival (OS) were constructed for pCR and non-pCR patients, respectively, and were presented using web-based dynamic nomograms (graphical representation of predicted OS based on the clinical characteristics that a patient could input into the website). The calibration plot, concordance index and decision curve analysis were employed to assess calibration, discrimination and clinical usefulness of the predictive models.

Results: In total, 13 and 15 variables were used to predict OS for pCR and non-pCR patients undergoing neoadjuvant therapy followed by oesophagectomy, respectively. Key predictors included demographic characteristics, pretreatment clinical stage, surgical approach, pathological information and postoperative treatments. The predictive models for pCR and non-pCR patients demonstrated good calibration and clinical utility, with acceptable discrimination that surpassed that of the current tumour, node, metastases staging system.

Conclusions: The web-based dynamic nomograms for pCR (https://predict-survival.shinyapps.io/pCR-eso/) and non-pCR patients (https://predict-survival.shinyapps.io/non-pCR-eso/) developed in this study can facilitate the calculation of OS probability for individual patients undergoing neoadjuvant therapy and radical oesophagectomy, aiding clinicians and patients in making personalised treatment decisions.

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来源期刊
BMJ Open Gastroenterology
BMJ Open Gastroenterology GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
5.90
自引率
3.20%
发文量
68
审稿时长
2 weeks
期刊介绍: BMJ Open Gastroenterology is an online-only, peer-reviewed, open access gastroenterology journal, dedicated to publishing high-quality medical research from all disciplines and therapeutic areas of gastroenterology. It is the open access companion journal of Gut and is co-owned by the British Society of Gastroenterology. The journal publishes all research study types, from study protocols to phase I trials to meta-analyses, including small or specialist studies. Publishing procedures are built around continuous publication, publishing research online as soon as the article is ready.
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