A nomogram to assess small-intestinal neuroendocrine tumor ('carcinoid') survival.

Neuroendocrine tumors (NETs) are a heterogeneous group of cancers of which the commonest site is the small intestine (SI). Most information available to determine tumor behavior reflects univariate assessment of factors or is anecdotal or experience based. There currently exists no objective multivariate analysis of indices that defines SI NET prognosis. A key unmet need is the lack of a rigorous mathematical-based tool - a nomogram - for the assessment of parameters that define progress, determine prognosis and can guide therapy. Since prediction of NET behavior is a critical criterion in determining clinical strategy, we constructed a NET nomogram (Modlin Score) for prognosis prediction, patient group comparisons and a guide for stratification of treatment and surveillance. We used hazard ratio (HR), Cox analysis and Kaplan-Meier analysis of published data and the current Surveillance, Epidemiology and End Results (SEER) database (approx. 20,000 patients) to develop a nomogram from 15 variables demonstrated to provide significant multivariate HRs. These included age, gender, ethnicity, symptoms, urinary 5-hydroxyindoleacetic acid, plasma chromogranin A, liver function tests, tumor size, invasion, metastasis, histology, Ki-67 index, carcinoid heart disease and therapy (surgery or long-acting somatostatin analogs). Internal validation was assessed using 33 SI NET patients. A NET nomoscore (Modlin Score) was developed by HR weighting and stratification into low (<75), medium (75-95) and high risk (>95). This identified significant differences (p <0.03, Kaplan-Meier) in survival (15.5 ± 4.3, 9.7 ± 2.5 and 6.4 ± 1.1 years, respectively). The Modlin Score was significantly elevated (p <0.01) in deceased compared to alive patients. This nomogram represents an optimized construct based upon currently analyzable data, and application will facilitate accurate stratification for comparison in clinical trials. External validation and amplification by identification of additional indices, e.g. molecular biomarkers, are necessary. The development of a mathematically validated nomogram provides a platform for objective assessment of SI NET disease, a finite basis for precise prognostication and a tool to guide management strategy.