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开发并验证用于预测结肠癌根治性切除术后总生存的列线图

Development and external validation of a nomogram predicting overall survival after curative resection of colon cancer.

机构信息

Department of Gastrointestinal Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China.

Department of General Surgery, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

出版信息

J Int Med Res. 2021 May;49(5):3000605211015023. doi: 10.1177/03000605211015023.


DOI:10.1177/03000605211015023
PMID:33990147
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8127758/
Abstract

OBJECTIVE: To develop and externally validate a prognostic nomogram to predict overall survival (OS) in patients with resectable colon cancer. METHODS: Data for 50,996 patients diagnosed with non-metastatic colon cancer were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were assigned randomly to the training set (n = 34,168) or validation set (n = 16,828). Independent prognostic factors were identified by multivariate Cox proportional hazards regression analysis and used to construct the nomogram. Harrell's C-index and calibration plots were calculated using the SEER validation set. Additional external validation was performed using a Chinese dataset (n = 342). RESULTS: Harrell's C-index of the nomogram for OS in the SEER validation set was 0.71, which was superior to that using the 7th edition of the American Joint Committee on Cancer TNM staging (0.59). Calibration plots showed consistency between actual observations and predicted 1-, 3-, and 5-year survival. Harrell's C-index (0.72) and calibration plot showed excellent predictive accuracy in the external validation set. CONCLUSIONS: We developed a nomogram to predict OS after curative resection for colon cancer. Validation using the SEER and external datasets revealed good discrimination and calibration. This nomogram may help predict individual survival in patients with colon cancer.

摘要

目的:开发并外部验证用于预测可切除结肠癌患者总生存期(OS)的预后列线图。

方法:从监测、流行病学和最终结果(SEER)数据库中检索了 50996 例诊断为非转移性结肠癌患者的数据。患者被随机分配到训练集(n=34168)或验证集(n=16828)。通过多变量 Cox 比例风险回归分析确定独立的预后因素,并用于构建列线图。使用 SEER 验证集计算 Harrell 的 C 指数和校准图。使用中国数据集(n=342)进行了额外的外部验证。

结果:SEER 验证集中用于 OS 的列线图的 Harrell 的 C 指数为 0.71,优于第 7 版美国癌症联合委员会 TNM 分期(0.59)。校准图显示实际观察值与预测的 1、3 和 5 年生存率之间具有一致性。在外部验证集中,Harrell 的 C 指数(0.72)和校准图显示出优异的预测准确性。

结论:我们开发了一种用于预测结肠癌根治性切除后 OS 的列线图。使用 SEER 和外部数据集进行验证显示出良好的区分度和校准度。该列线图可能有助于预测结肠癌患者的个体生存。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/d5e8a1da70c6/10.1177_03000605211015023-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/5555820eb745/10.1177_03000605211015023-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/fdf066acfafa/10.1177_03000605211015023-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/c063e95c6cca/10.1177_03000605211015023-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/d5e8a1da70c6/10.1177_03000605211015023-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/5555820eb745/10.1177_03000605211015023-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/fdf066acfafa/10.1177_03000605211015023-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/c063e95c6cca/10.1177_03000605211015023-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2729/8127758/d5e8a1da70c6/10.1177_03000605211015023-fig4.jpg

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引用本文的文献

[1]
Constructing a nomogram to predict overall survival of colon cancer based on computed tomography characteristics and clinicopathological factors.

World J Gastrointest Oncol. 2024-10-15

[2]
Prognostic nomogram in patients with right-sided colon cancer after colectomy: a surveillance, epidemiology, and end results-based study.

Front Oncol. 2024-3-14

[3]
Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.

Heliyon. 2024-3-12

[4]
Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm.

Digit Health. 2023-9-26

[5]
Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.

Sci Rep. 2023-9-21

[6]
Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients.

BMC Surg. 2023-6-29

[7]
A nomogram for individually predicting the overall survival in colonic adenocarcinoma patients presenting with perineural invasion: a population study based on SEER database.

Front Oncol. 2023-5-19

[8]
Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China.

Front Oncol. 2022-11-17

[9]
Nomogram for Prediction of Postoperative Morbidity in Patients with Colon Cancer Requiring Emergency Therapy.

Med Sci Monit. 2022-6-30

[10]
Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort.

Cancer Manag Res. 2022-1-4

本文引用的文献

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Development and validation of a novel nomogram for pretreatment prediction of liver metastasis in pancreatic cancer.

Cancer Med. 2020-5

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