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Novel nomograms to predict lymph node metastasis and liver metastasis in patients with early colon carcinoma

Authors :
Yongcong Yan
Haohan Liu
Kai Mao
Mengyu Zhang
Qianlei Zhou
Wei Yu
Bingchao Shi
Jie Wang
Zhiyu Xiao
Source :
Journal of Translational Medicine, Vol 17, Iss 1, Pp 1-16 (2019)
Publication Year :
2019
Publisher :
BMC, 2019.

Abstract

Abstract Background Lymph node status and liver metastasis (LIM) are important in determining the prognosis of early colon carcinoma. We attempted to develop and validate nomograms to predict lymph node metastasis (LNM) and LIM in patients with early colon carcinoma. Methods A total of 32,819 patients who underwent surgery for pT1 or pT2 colon carcinoma were enrolled in the study based on their records in the SEER database. Risk factors for LNM and LIM were assessed based on univariate and multivariate binary logistic regression. The C-index and calibration plots were used to evaluate LNM and LIM model discrimination. The predictive accuracy and clinical values of the nomograms were measured by decision curve analysis. The predictive nomograms were further validated in the internal testing set. Results The LNM nomogram, consisting of seven features, achieved the same favorable prediction efficacy as the five-feature LIM nomogram. The calibration curves showed perfect agreement between nomogram predictions and actual observations. The decision curves indicated the clinical usefulness of the prediction nomograms. Receiver operating characteristic curves indicated good discrimination in the training set (area under the curve [AUC] = 0.667, 95% CI 0.661–0.673) and the testing set (AUC = 0.658, 95% CI 0.649–0.667) for the LNM nomogram and encouraging performance in the training set (AUC = 0.766, 95% CI 0.760–0.771) and the testing set (AUC = 0.825, 95% CI 0.818–0.832) for the LIM nomogram. Conclusion Novel validated nomograms for patients with early colon carcinoma can effectively predict the individualized risk of LNM and LIM, and this predictive power may help doctors formulate suitable individual treatments.

Details

Language :
English
ISSN :
14795876
Volume :
17
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Journal of Translational Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.f6cfaf3364b84472a74f1b9c42b31c3b
Document Type :
article
Full Text :
https://doi.org/10.1186/s12967-019-1940-1