Long-term surgical outcomes of gastric neuroendocrine carcinoma and mixed neuroendocrine-non-neuroendocrine neoplasms

Long-term survival with complete remission after irinotecan plus cisplatin therapy for metachronous liver metastasis from a gastric mixed neuroendocrine-non-neuroendocrine neoplasm: a case report

Gastric mixed neuroendocrine-non-neuroendocrine neoplasm, which consists of both exocrine and endocrine components, is rare and exhibits aggressive biological behavior and poor prognosis. A 64-year-old man underwent investigation for abdominal pain. An endoscopic examination revealed a 30-mm protruded lesion in the gastric antrum for which distal gastrectomy with lymph node dissection was performed. Histopathological examination showed two distinct components, an adenocarcinoma component and neuroendocrine carcinoma component, with immunohistochemical staining positive for CD56, chromogranin A, and synaptophysin. The final diagnosis was mixed neuroendocrine-non-neuroendocrine neoplasm, pT2, pN3, M0, stage IIIA. Multiple liver metastases were detected 3 months after surgery. The patient received irinotecan plus cisplatin therapy, which was effective, and the liver metastases disappeared. The patient remains alive, without any recurrence, more than 10 years after surgery. We present a rare case of gastric mixed neuroendocrine-non-neuroendocrine neoplasm and review the literature to contribute to improving our understanding of this kind of tumor.

Prognostic significance of tertiary lymphoid structures in gastric neuroendocrine carcinoma with association to delta-like ligand 3 and neuroendocrine expressions

BACKGROUND

Gastric neuroendocrine carcinomas (NECs) are rare cancers with highly aggressive behavior. Although tertiary lymphoid structures (TLSs) are well-known prognostic factors in various cancers, their role in gastric NECs remain unexplored. Unique immunohistochemical subtypes of pulmonary NECs have been discovered, however, their feasibility in gastric NECs is unknown.

METHODS

The presence and maturation of TLSs (lymphoid aggregates, primary and secondary follicles) were assessed in 48 surgically resected gastric NECs and were compared with immunohistochemical subtypes, using a panel of ASCL1, NeuroD1, POU2F3, YAP1, and DLL3 with three neuroendocrine (NE) markers.

RESULTS

Patients with secondary follicles had significantly better overall survival (OS) and recurrence-free survival (RFS; both, p = 0.004) than those without them. Based on the hierarchical clustering, gastric NECs were classified into all low/negative (31%), high-YAP1 (19%), high-DLL3/low-NE (29%), and high-NE (21%) expression groups. The high-DLL3/low-NE group was associated with absent TLSs (p = 0.026) and showed the worst OS (p = 0.026). Distant metastasis and a lack of secondary follicles were poor independent prognostic factors of OS and RFS.

CONCLUSION

The assessment of TLSs is a feasible and potent biomarker for gastric NECs, thus enabling better prognosis and more effective immunotherapy. Furthermore, gastric NECs can be categorized as four immunohistochemically distinct groups, of which the high-DLL3/low-NE group has the worst OS with lack of TLSs.

Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine

Survival rates following radical surgery for gastric neuroendocrine neoplasms (g-NENs) are low, with high recurrence rates. This fact impacts patient prognosis and complicates postoperative management. Traditional prognostic models, including the Cox proportional hazards (CoxPH) model, have shown limited predictive power for postoperative survival in gastrointestinal neuroectodermal tumor patients. Machine learning methods offer a unique opportunity to analyze complex relationships within datasets, providing tools and methodologies to assess large volumes of high-dimensional, multimodal data generated by biological sciences. These methods show promise in predicting outcomes across various medical disciplines. In the context of g-NENs, utilizing machine learning to predict survival outcomes holds potential for personalized postoperative management strategies. This editorial reviews a study exploring the advantages and effectiveness of the random survival forest (RSF) model, using the lymph node ratio (LNR), in predicting disease-specific survival (DSS) in postoperative g-NEN patients stratified into low-risk and high-risk groups. The findings demonstrate that the RSF model, incorporating LNR, outperformed the CoxPH model in predicting DSS and constitutes an important step towards precision medicine.

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.

Gastric mixed neuroendocrine non-neuroendocrine neoplasms

The uncommon tumour known as gastric mixed neuroendocrine-non-neuroendocrine neoplasms (G-MiNENs) is made up of parts of neuroendocrine carcinoma and adenocarcinoma. The biological and clinical features are different from those of gastric adenocarcinoma. Their pathophysiology, diagnostic standards, and clinical behaviour have all been the subject of lengthy debates, and their nomenclature has undergone multiple changes. Its emergence has created new challenges in the classification and diagnosis of gastric tumours. This review will update information on the topic, covering molecular aspects, diagnostic criteria, treatment, and prognostic factor discovery. It will also provide a historical context that will aid in understanding the evolution of the idea and nomenclature of mixed gastric tumours. Additionally, it will provide the reader a thorough understanding of this difficult topic of cancer that is applicable to real-world situations.

Management of Gastric Neuroendocrine Tumors: A Review

Gastric neuroendocrine tumors (G-NET) are rare tumors arising from enterochromaffin-like cells of the gastric mucosa. They belong to a larger group called gastroenteropancreatic neuroendocrine tumors and are classified as low, intermediate, or high-grade tumors based on their proliferative indices. They are further categorized into three subtypes based on their morphologic characteristics, pathogenesis, and behavior. Types 1 and 2 tumors are characterized by elevated serum gastrin and are usually multifocal. They typically occur in the setting of atrophic gastritis or MEN1/Zollinger Ellison syndrome, respectively. Type 2 tumors are associated with the most symptoms, such as abdominal pain and diarrhea. Type 3 tumors are associated with normal serum gastrin, are usually solitary, and occur sporadically. This type has the most aggressive phenotype and metastatic potential. Treatment and prognosis for G-NET is dependent on their type, size, and stage. Type 1 has the best prognosis, and Type 3 has the worst. This review discusses the presentation, workup, and surgical management of these tumors.

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.

Incidence, survival, and prognostic factors for patients with gastrointestinal mixed neuroendocrine non-neuroendocrine neoplasms: a SEER population-based study

BACKGROUND

Mixed neuroendocrine non-neuroendocrine neoplasms (MiNENs) are a group of rare tumors with limited research currently available. This study aimed to analyze the incidence, survival, and prognostic factors of gastrointestinal MiNENs.

METHODS

We included data from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019. We compared the clinicopathologic characteristics and survival rates between MiNENs and neuroendocrine tumors (NETs), and calculated the incidence of MiNENs. We utilized univariate and multivariate Cox analysis to assess independent factors of prognosis and established a nomogram to predict 1-, 2-, and 3-year cancer-specific survival (CSS). Calibration and receiver operating characteristic (ROC) curves were drawn to validate the accuracy and reliability of the model. Decision curve analysis (DCA) was used to assess the clinical utility of the model.

RESULTS

Patients with gastrointestinal MiNENs had a poorer prognosis than those with NETs. The overall incidence of gastrointestinal MiNENs has been increasing annually. Multivariate Cox regression analysis revealed that tumor size, lymph node metastasis, distant metastasis, and surgery were independent risk factors for CSS in MiNENs patients. Based on these risk factors, the 1-, 2-, and 3-year CSS nomogram model for MiNENs patients was established. Calibration, ROC, and DCA curves of the training and validation sets demonstrated that this model had good accuracy and clinical utility.

CONCLUSION

Gastrointestinal MiNENs are rare tumors with an increasing incidence rate. The nomogram model is expected to be an effective tool for personalized prognosis prediction in MiNENs patients, which may benefit clinical decision-making.