Development of a nomogram model to predict survival outcomes in patients with primary hepatic neuroendocrine tumors based on SEER database

A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding: A Two-center Retrospective Study

OBJECTIVE

This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding (DUGIB), and identify high-risk patients who require emergent therapy.

METHODS

From January 2020 to April 2022, the clinical data of 256 DUGIB patients who received treatments in the intensive care unit (ICU) were retrospectively collected from Renmin Hospital of Wuhan University (n=179) and the Eastern Campus of Renmin Hospital of Wuhan University (n=77). The 179 patients were treated as the training cohort, and 77 patients as the validation cohort. Logistic regression analysis was used to calculate the independent risk factors, and R packages were used to construct the nomogram model. The prediction accuracy and identification ability were evaluated by the receiver operating characteristic (ROC) curve, C index and calibration curve. The nomogram model was also simultaneously externally validated. Decision curve analysis (DCA) was then used to demonstrate the clinical value of the model.

RESULTS

Logistic regression analysis showed that hematemesis, urea nitrogen level, emergency endoscopy, AIMS65, Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB. The ROC curve analysis indicated the area under curve (AUC) of the training cohort was 0.980 (95%CI: 0.962-0.997), while the AUC of the validation cohort was 0.790 (95%CI:0.685-0.895). The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts (P=0.778, P=0.516).

CONCLUSION

The developed nomogram is an effective tool for risk stratification, early identification and intervention for DUGIB patients.

Primary neuroendocrine tumors and primary neuroendocrine carcinomas of the liver: a proposal for a multidiscipline definition

Primary hepatic neuroendocrine tumors and primary hepatic neuroendocrine carcinomas are rare and pose challenges for both diagnosis and for determining whether the tumor is primary to the liver versus metastatic disease. The lack of a uniform definition for primary hepatic neuroendocrine neoplasms is also a limitation to understanding and treating these rare tumors. Recently, there have been significant histological advances in the diagnosis and classification of neuroendocrine tumors in general, as well as significant advances in imaging for neuroendocrine neoplasms, all of which are important for their treatment. This article presents a multiple disciplinary definition and proposed guidelines for diagnosing a neuroendocrine tumor/neuroendocrine carcinomas as being primary to the liver.

Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor

Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies.

Hepatectomy and pneumectomy combined with targeted therapy for primary hepatic neuroendocrine carcinoma: Case report and review of the literature

Primary hepatic neuroendocrine carcinoma (PHNEC) manifests as a rare type of liver tumor. PHNEC is not specifically clinical or radiographical and is often misdiagnosed and mistreated. Here, we present a case report of PHNEC in a 50-year-old woman who was admitted to our department with concealed pain in the right upper abdomen. The initial diagnosis was a probable hepatic space-occupying lesion with tumor bleeding. The patient was subjected to a partial right hemihepatectomy, cholecystectomy, partial resection of the lower lobe of the right lung, partial resection of the diaphragm, and resection of the right perirenal fat sac to alleviate her symptoms. After surgery, gene sequencing was performed to determine the possible cause of the condition. However, five months after discharge, the patient was hospitalized again because of retroperitoneal and peritoneal multiple metastases. Nine months after surgery, the patient died. This case is likely to aid in furthering our understanding of PHNEC to improve the future diagnosis and treatment of this disease.

Development and Validation of Nomogram for Predicting Survival of Primary Liver Cancers Using Machine Learning

Background and Aims

Primary liver cancer (PLC) is a common malignancy with poor survival and requires long-term follow-up. Hence, nomograms need to be established to predict overall survival (OS) and cancer-specific survival (CSS) from different databases for patients with PLC.

Methods

Data of PLC patients were downloaded from Surveillance, Epidemiology, and End Results (SEER) and the Cancer Genome Atlas (TCGA) databases. The Kaplan Meier method and log-rank test were used to compare differences in OS and CSS. Independent prognostic factors for patients with PLC were determined by univariate and multivariate Cox regression analyses. Two nomograms were developed based on the result of the multivariable analysis and evaluated by calibration curves and receiver operating characteristic curves.

Results

OS and CSS nomograms were based on age, race, TNM stage, primary diagnosis, and pathologic stage. The area under the curve (AUC) was 0.777, 0.769, and 0.772 for 1-, 3- and 5-year OS. The AUC was 0.739, 0.729 and 0.780 for 1-, 3- and 5-year CSS. The performance of the two new models was then evaluated using calibration curves.

Conclusions

We systematically reviewed the prognosis of PLC and developed two nomograms. Both nomograms facilitate clinical application and may benefit clinical decision-making.