Prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms: a multi-center study in China

Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients

BACKGROUND

Lymph node ratio (LNR) was demonstrated to play a crucial role in the prognosis of many tumors. However, research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm (NEN) patients was limited.

AIM

To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.

METHODS

A total of 286 patients from the Surveillance, Epidemiology, and End Results database were divided into the training set and validation set at a ratio of 8:2. 92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set. Cox regression analysis was used to explore the relationship between LNR and disease-specific survival (DSS) of gastric NEN patients. Random survival forest (RSF) algorithm and Cox proportional hazards (CoxPH) analysis were applied to develop models to predict DSS respectively, and compared with the 8th edition American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging.

RESULTS

Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death. The RSF model exhibited the best performance in predicting DSS, with the C-index in the test set being 0.769 [95% confidence interval (CI): 0.691-0.846] outperforming the CoxPH model (0.744, 95%CI: 0.665-0.822) and the 8th edition AJCC TNM staging (0.723, 95%CI: 0.613-0.833). The calibration curves and decision curve analysis (DCA) demonstrated the RSF model had good calibration and clinical benefits. Furthermore, the RSF model could perform risk stratification and individual prognosis prediction effectively.

CONCLUSION

A higher LNR indicated a lower DSS in postoperative gastric NEN patients. The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set, showing potential in clinical practice.

Clinicopathological characteristics of gastric neuroendocrine neoplasms: A comprehensive analysis

OBJECTIVE

This study aimed to explore the clinicopathological characteristics and prognostic implications of gastric neuroendocrine neoplasms (g-NENs).

METHODS

A retrospective enrollment of 142 patients diagnosed with g-NENs was conducted at Zhejiang Cancer Hospital between January 1, 2007 and December 31, 2021. The study compared essential clinicopathological features and survival rates. Additionally, the prognosis of gastric neuroendocrine carcinomas/mixed neuroendocrine-non-neuroendocrine neoplasms (g-NEC/MiNEN) were contrasted with those of gastric adenocarcinoma (GAC) and signet ring cell carcinoma (SRCC).

RESULTS

The study comprised a total of 142 g-NENs cases, with a male-to-female ratio of approximately 2:1. The 5-year survival rates for g-NEC and g-MiNEN were 26.7% and 35.2%, respectively. Corresponding 5-year survival rates for G1 and G2 were observed at 100% and 80.0%, respectively. g-NEC/MiNEN showed a significantly worse prognosis compared to g-NET (p < 0.001). g-NEC/MiNEN exhibited a poor prognosis compared to GAC (p < 0.001), and within poorly differentiated GAC, g-NEC/MiNEN demonstrated a worse prognosis (p = 0.007). Additionally, patients receiving postoperative adjuvant therapy exhibited notably prolonged overall survival (OS) in the case of g-NEC/MiNEN (p = 0.010).

CONCLUSION

In short, the prognosis of g-NEC/MiNEN was worse than that of g-NET, GAC and poorly differentiated GAC, but this group benefit from postoperative adjuvant therapy.

Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms

Background

Neuroendocrine neoplasms (NENs) are tumors that originate from secretory cells of the diffuse endocrine system and typically produce bioactive amines or peptide hormones. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform.

Methods

In this investigation, data from 1256 patients were used, of whom 119 patients from the First Affiliated Hospital of Soochow University in China and 1137 cases from the surveillance epidemiology and end results (SEER) database were combined. Six machine learning algorithms, including the logistic regression model (LR), random forest (RF), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to build the predictive model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Among the 1256 patients with gastric NENs, 276 patients (21.97 %) developed lymph node metastasis. T stage, tumor size, degree of differentiation, and sex were predictive factors of lymph node metastasis. The RF model achieved the best predictive performance among the six machine learning models, with an AUC, accuracy, sensitivity, and specificity of 0.81, 0.78, 0.76, and 0.82, respectively.

Conclusion

The RF model provided the best prediction and can help physicians determine the lymph node metastasis risk of gastric NEN patients to formulate individualized medical strategies.