Liu Y, Zhang H, Zheng M, Wang C, Hu Z, Wang Y, Xiong H, Fan B, Wang Y, Hu H, Tang Q, Wang G. Nomogram to Predict the Occurrence and Prognosis of Distant Metastasis in T1N0 Colon Cancer: A SEER Data-Based Study.
Int J Gen Med 2021;
14:9131-9143. [PMID:
34876846 PMCID:
PMC8643170 DOI:
10.2147/ijgm.s335151]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 11/15/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose
Distant metastasis (DM) is relatively rare in T1 colon cancer (CC) patients, especially in those with negative lymph node metastasis. The aim of this study was to explore the main clinical factors and build nomogram for predicting the occurrence and prognosis of DM in T1N0 colon cancer patients.
Methods
Patients with T1N0 stage CC were collected from the Surveillance, Epidemiology, and End Result (SEER) database. All patients were divided into development and validation cohorts with the 3:1 ratio. Logistic regressions were performed to analyze the clinical risk factors for DM. Cox regression model was used to identify potential prognostic factors for patients with DM. The performance of nomogram was evaluated by concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves and decision curve analyses (DCAs). Based on cancer-specific survival (CSS), Kaplan-Meier curves were generated and analyzed using Log rank tests.
Results
A total of 6770 patients were enrolled in this study, including 428 patients (6.3%) with DM. Age, size, grade, CEA were independent risk factors associated with DM. Age, grade, CEA, surgery and chemotherapy were independent prognostic factors for CSS. Nomograms were applied and C-index, calibration curves, ROC curves and DCA curves proved good discrimination, calibration and clinical practicability of the nomogram in predicting the occurrence and prognosis of DM in T1N0 CC patients. In the DM nomogram, the AUCs for development and validation cohort were 0.901 (95% CI = 0.879-0.922) and 0.899 (95% CI=0.865-0.940), respectively. The calibration curves (development cohort: S: p = 0.712; validation cohort: S: p = 0.681) showed the relatively satisfactory prediction accuracy. Similarly, the AUCs of the nomogram at 1-, 2-, and 3-year were 0.763 (95% CI=0.744-0.782), 0.794 (95% CI=0.775-0.813), and 0.822 (95% CI=0.803-0.841) for the development cohort, and 0.785 (95% CI=0.754-0.816), 0.748 (95% CI=0.717-0.779) and 0.896 (95% CI=0.865-0.927) for the validation cohort in the CSS nomogram. The C-indices of the development and validation cohort were 0.718 (95% CI=0.639-0.737) and 0.712 (95% CI=0.681-0.743).
Conclusion
The population-based nomogram could help clinicians predict the occurrence and prognosis of DM in T1N0 CC patients and provide a reference to perform appropriate metastatic screening plans and rational therapeutic options for the special population.
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