Xu J, Yao Z, Liao G, OuYang X, Mao S, Cao J, Lai B. Prediction of distant metastasis and specific survival prediction of small intestine cancer patients with metastasis: A population-based study.
Cancer Med 2023;
12:15037-15053. [PMID:
37255376 PMCID:
PMC10417179 DOI:
10.1002/cam4.6166]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND
Small intestine cancer (SIC) is difficult to diagnose early and presents a poor prognosis due to distant metastasis. This study aimed to develop nomograms for diagnosing and assessing the prognosis of SIC with distant metastasis.
METHODS
Patients diagnosed with SIC between 2010 and 2015 were included from the Surveillance, Epidemiology and End Results database. Univariate and multifactor analysis determined independent risk factors for distant metastasis and prognostic factors for overall and cancer-specific survival. We then constructed the corresponding three nomograms and assessed the diagnostic accuracy of the nomograms by net reclassification improvement, receiver operating characteristic curves and calibration curves, assessed the clinical utility by decision curve analysis.
RESULTS
The cohort consisted of 6697 patients, of whom 1299 had distant metastasis at diagnosis. Tstage, Nstage, age, tumor size, grade, and histological type were independent risk factors for distant metastasis. Age, histological type, T stage, N stage, grade, tumor size, whether receiving surgery, number of lymph nodes removed, and the presence of bone or lung metastases were predictors of both overall survival and cancer-specific survival. The nomograms showed excellent accuracy in predicting distant metastasis and prognosis.
CONCLUSION
Nomograms were developed and validated for SIC patients with distant metastasis, aiding physicians in making rational and personalized clinical decisions.
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