A Nomogram to Predict Individual Survival of Patients with Liver-Limited Metastases from Gastroenteropancreatic Neuroendocrine Neoplasms: A US Population-Based Cohort Analysis and Chinese Multicenter Cohort Validation Study

Abstract

<b><i>Introduction:</i></b> Although gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) with liver metastasis encompass a wide variety of clinical conditions with various prognosis, no statistical model for predicting the prognosis of these patients has been established. We sought to establish a more elaborative and individualized nomogram to predict survival of patients with liver-limited metastatic GEP-NENs. In addition, this nomogram was validated by both the Surveillance, Epidemiology, and End Results (SEER) database and a Chinese multicenter cohort. <b><i>Methods:</i></b> Patients diagnosed with GEP-NENs with liver-limited metastasis between 2010 and 2016 were identified from the SEER database. Kaplan-Meier survival analysis was performed to analyze survival outcomes. A nomogram was established based on the independent prognostic variables identified from univariate and multivariate Cox regression analyses. The nomogram was evaluated in both an internal validation SEER dataset and an external validation dataset composed of patients from the Chinese multicenter cohort. <b><i>Results:</i></b> A total of 1,474 patients from the SEER database and 192 patients from the multicenter cohort were included. Age, tumor size, differentiation, primary tumor resection, and liver metastasis resection were identified as independent prognostic factors by univariate and multivariate Cox analyses and were verified by Kaplan-Meier survival analysis (all <i>p</i> < 0.0001). A nomogram was developed and validated by calibration curves and areas under the curve of the external validation cohort, which showed good consistency and veracity in predicting overall survival. <b><i>Conclusion:</i></b> A nomogram was developed for the first time to predict the survival of patients with liver-limited metastases from GEP-NENs. Both internal and external validation demonstrated excellent discrimination and calibration of our nomogram. Based on this prognostic model, clinicians could develop more personalized treatment strategies and surveillance protocols.

Evaluating Prognosis of Gastrointestinal Metastatic Neuroendocrine Tumors: Constructing a Novel Prognostic Nomogram Based on NETPET Score and Metabolic Parameters from PET/CT Imaging

“…In a study based on the SEER database targeting patients with GEP-NENs, the nomogram they constructed for OS yielded C-indexes of 0.821 and 0.823 in the primary and validation groups, respectively [ 57 ]. Additionally, in a study focusing on patients with GEP-NENs and liver-limited metastasis, the nomogram for OS achieved C-indexes of 0.814, 0.826, and 0.789 in the training, internal validation, and external validation sets, respectively [ 58 ].These results are closely aligned with ours, which are 0.810 and 0.849, yet they lacked an investigation into patients’ PFS, which is crucial for a disease characterized by a relatively mild and chronic course, such as NETs. In our study, we also constructed a predictive model for patients’ PFS and obtained favorable results.…”

A Nomogram to Predict the Risk for MACCE within 1 Year after Discharge of Patients with NVAF and HFpEF: A Multicenter Retrospective Study

“…To assist physicians in providing appropriate clinical interventions, we developed a nomogram, which is a visual and practical tool based on these independent risk factors. Nomograms are widely used in diagnosing diseases, predicting treatment effect, and assessing clinical outcomes [ 16 , 17 , 18 ]. For instance, an elderly female with a 7-year history of AF (60 points) had NVAF and HFpEF, no history of hyperthyroidism (0 points), and poor medication adherence (50 points).…”

Gastroenteropancreatic neuroendocrine neoplasms: current development, challenges, and clinical perspectives

“…AI is a new field of science that provides human cognitive abilities to perform complex tasks, such as decisionmaking, which can be performed only by humans. The emergence of AI has enabled the introduction of more accurate diagnostic models for determining patient prognoses and guiding clinical decision-making [95][96][97][98][99][100][101]. Bevilacqua et al [95] developed a noninvasive model based on presurgical 68 Ga-DOTANOC PET/CT and conventional diagnostic methods.…”