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一种新型列线图可单独预测晚期胃癌行 D2 胃切除术患者的疾病特异性生存。

A novel nomogram individually predicting disease-specific survival after D2 gastrectomy for advanced gastric cancer.

机构信息

Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, 510060, Guangdong, P. R. China.

Department of Surgical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, 110000, Liaoning, P. R. China.

出版信息

Cancer Commun (Lond). 2018 May 15;38(1):23. doi: 10.1186/s40880-018-0293-0.

DOI:10.1186/s40880-018-0293-0
PMID:29764518
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5993138/
Abstract

BACKGROUND

Few studies have shown nomograms that may predict disease-specific survival (DSS) probability after curative D2 gastrectomy for advanced gastric cancer (AGC), particularly among Chinese patients. This study sought to develop an elaborative nomogram that predicts long-term DSS for AGC in Chinese patients.

METHODS

A retrospective study was conducted on 6753 AGC patients undergoing D2 gastrectomy between January 1, 2000 and December 31, 2012 from three large medical hospitals in China. We assigned patients from Sun Yat-sen University Cancer Center to the training set, and patients from the First Affiliated Hospital of China Medical University and Tianjin Medical University Cancer Hospital to two separate external validation sets. A multivariate survival analysis was performed using Cox proportional hazards regression model in a training set, and a nomogram was constructed. Harrell's C-index was used to evaluate discrimination and calibration plots were used to validate similarities between survival probabilities predicted by the nomogram model and actual survival rates in two validation sets.

RESULTS

The multivariate Cox regression model identified age, tumor size, location, Lauren classification, lymphatic/venous invasion, depth of invasion, and metastatic lymph node ratio as covariates associated with survival. In the training set, the nomogram exhibited superior discrimination power compared with the 8th American Joint Committee on Cancer TNM classification (Harrell's C-index, 0.82 vs. 0.74; P < 0.001). In two validation sets, the nomogram's discrimination power was also excellent relative to TNM classification (C-index, 0.83 vs. 0.75 and 0.81 vs. 0.74, respectively; P < 0.001 for both). After calibration, the nomogram produced survival predictions that corresponded closely with actual survival rate.

CONCLUSIONS

The established nomogram was able to predict 3-, 5-, and 10-year DSS probabilities for AGC patients. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacity, suggesting its clinical utility.

摘要

背景

少数研究表明,对于接受根治性 D2 胃切除术的晚期胃癌(AGC)患者,存在预测疾病特异性生存(DSS)概率的列线图,但这些研究大多来自西方国家,特别是针对中国患者的研究则较少。本研究旨在建立一种预测中国患者 AGC 长期 DSS 的详细列线图。

方法

本研究回顾性分析了 2000 年 1 月 1 日至 2012 年 12 月 31 日期间,3 家中国大型医疗中心的 6753 例接受 D2 胃切除术的 AGC 患者。中山大学肿瘤防治中心的患者被分配到训练集中,中国医科大学第一附属医院和天津医科大学肿瘤医院的患者被分配到两个独立的外部验证集中。在训练集中,使用 Cox 比例风险回归模型进行多变量生存分析,并构建列线图。Harrell 的 C 指数用于评估判别能力,校准图用于验证列线图模型预测的生存概率与两个验证集中的实际生存率之间的相似性。

结果

多变量 Cox 回归模型确定年龄、肿瘤大小、部位、Lauren 分类、淋巴/静脉侵犯、浸润深度和转移淋巴结比例为与生存相关的协变量。在训练集中,与第 8 版美国癌症联合委员会 TNM 分期(Harrell 的 C 指数:0.82 比 0.74;P<0.001)相比,列线图显示出更高的判别能力。在两个验证集中,与 TNM 分期相比,列线图的判别能力也非常出色(C 指数分别为 0.83 比 0.75 和 0.81 比 0.74;均 P<0.001)。经过校准后,列线图的生存预测与实际生存率非常吻合。

结论

本研究建立的列线图能够预测 AGC 患者的 3、5 和 10 年 DSS 概率。验证表明,该列线图具有良好的判别能力和校准能力,具有临床应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/075c1f75f4e9/40880_2018_293_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/0391f144d147/40880_2018_293_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/647d4323b76d/40880_2018_293_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/1e61267b1bb6/40880_2018_293_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/075c1f75f4e9/40880_2018_293_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/0391f144d147/40880_2018_293_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/647d4323b76d/40880_2018_293_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/1e61267b1bb6/40880_2018_293_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/062c/5993138/075c1f75f4e9/40880_2018_293_Fig4_HTML.jpg

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