Abstract 1196P
Background
Regional lymph node metastasis risk factors for rectal neuroendocrine tumors: a population-based study A short title: Regional lymph node metastasis risk factors for rectal neuroendocrine tumors Key words: regional lymph node metastasis, rectal neuroendocrine tumors, SEER, West China Hospital (WCH) databases, tumor size. Purpose: Risk factors for regional lymph node (r-LN) metastasis in rectal neuroendocrine tumors (R-NETs) are still undetermined. We aimed to explore risk factors for patients with r-LN metastatic R-NETs. Information on patients was obtained from the Surveillance, Epidemiology, and End Results (SEER) and West China Hospital (WCH) databases.
Methods
Patients diagnosed with R-NETs between January 2010 and December 2015 from the SEER database composed the construction cohort, while cases from the WCH database were used as the validation cohort. A novel nomogram was constructed to predict r-LN metastasis probability based on a logistic regression model. The performance of the nomogram was internally and externally validated using calibration curves and the concordance index (C-index).
Results
Multivariate logistic analysis identified four poor independent r-LN metastasis factors, including age (≥ 53), differentiation grade (> G1), T stage of primary tumor (> T1) and tumor size (> 1 cm), which we selected as the four risk predictors for nomogram construction. In the internal validation and external validation, the C-index in the internal validation set and the external validation set were 0.968 and 0.877, respectively. The nomogram model was well calibrated, and the ROC curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more accurately differentiate risk subgroups and improve the discrimination of R-NET prognosis.
Conclusions
We developed and validated a prediction risk model of r-LN metastasis for patients with R-NETs. The novel nomogram is a reliable tool to predict the risk of r-LN metastasis, which may assist clinicians in identifying high-risk patients and devising individual treatments.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
1·3·5 project for disciplines of excellence–Clinical Research Incubation Project, West China Hospital, Sichuan University.
Disclosure
All authors have declared no conflicts of interest.
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