Development and validation of nomograms for predicting the prognosis of colorectal cancer patients

构建和验证用于预测结直肠癌患者预后的列线图

阅读:1

Abstract

BACKGROUND: Accurate prognosis prediction is essential in colorectal cancer (CRC) for guiding treatment decisions, yet the traditional tumor-node-metastasis (TNM) staging system often lacks precision. This study aimed to develop improved prognostic tools for CRC patients. METHODS: Prognostic nomogram models were developed using data from 2,435 CRC patients who underwent curative resection. Parameters were selected via least absolute shrinkage and selection operator (LASSO) regression to include overall survival (OS) and disease-free survival (DFS) nomograms. The performance of these nomograms was evaluated against the TNM staging system using ROC analysis, calibration curves, and decision curve analysis (DCA). RESULTS: Critical prognostic factors identified included tumor invasion depth, distant metastasis, tumordifferentiation grade, extranodal tumor deposits (ENTD), R1 resection, and log odds of positive lymph nodes (LODDS). The OS nomogram demonstrated area under the curve (AUC) values of 0.786, 0.776, and 0.803 for predicting 1-, 3-, and 5-year survival, respectively, compared to 0.768, 0.750, and 0.782 for TNM staging. The DFS nomogram predicted 1-, 3-, and 5-year DFS with AUCs of 0.764, 0.777, and 0.789, respectively, compared to 0.762, 0.761, and 0.770 for TNM staging. Calibration plots indicated strong predictive capabilities, and DCA confirmed greater net benefits over TNM staging. CONCLUSIONS: Our developed prognostic nomogram models offer enhanced accuracy over traditional TNM staging in predicting CRC prognosis. Integrating these models into clinical practice can potentially improve personalized treatment strategies for postoperative CRC patients, enhancing overall clinical outcomes.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。