New Developments in Gastric Neuroendocrine Neoplasms

Endoscopic misdiagnosis of a polypoid gastric neuroendocrine neoplasm: literature review

A 75-year-old man presented to our department, complaining of recurrent abdominal pain for the past month. Esophagogastroduodenoscopy (EGD) revealed a single 0.6 cm polypoid lesion, classified as Yamada Type II, at the lesser curvature of the lower body of the stomach. The lesion exhibited a smooth surface, pale pink coloration, and no depression, ulceration, or erosion. Narrow-band imaging (NBI) demonstrated sparse superficial microvascular patterns on the lesion's surface. The observed polypoid lesion was treated via endoscopic mucosal resection (EMR). Pathological examination of the EMR specimen revealed a neuroendocrine neoplasm (NEN) in the gastric body, classified as grade G3. Immunohistochemical staining demonstrated that the tumor cells were positive for pan-cytokeratin (P-CK), CD56, synaptophysin, INSM1, and Ki67 (60% positive). Given the high grade, the patient underwent a subsequent partial gastrectomy. Pathological examination of the surgical specimen demonstrated a microscopic region of approximately 2 mm with dark-staining nuclei. Immunohistochemical staining showed positivity for CD56 and synaptophysin, and negativity for CD20. These findings were consistent with residual NEN, with the residual lesion measuring approximately 2 mm in maximum diameter.

Clinical Characteristics and Long-term Outcomes of Gastric Neuroendocrine Tumors

Background/Aims

Gastric neuroendocrine tumors (GNETs), once rare, have become more prevalent due to the increased use of endoscopy and increased physician awareness. The clinical characteristics and long-term outcomes of GNET management were explored in this study.

Methods

The clinical data of 69 patients who treated at Seoul National University Hospital between January 2013 and October 2023 were retrospectively studied. Baseline characteristics, recurrence rates, associated factors, and overall survival rates were analyzed.

Results

Of the tumors, 71.0% were grade 1, 24.6% were grade 2, 1.4% were grade 3, and 2.9% were poorly differentiated. In terms of tumor type, 69.6% were type I, 1.4% were type II, and 29.0% were type III. A significant proportion of patients with grade 1 tumors received more endoscopic treatment, whereas a significant proportion of patients with grade 2 tumors underwent surgery or chemotherapy (p=0.015). The overall 5-year survival and recurrence rates were 93.8% and 7.25% (5/69), respectively. Among five patients who experienced recurrence, three had metachronous recurrence, all of which were type I; the remaining two patients exhibited distant hepatic metastasis, encompassing types I and III. The time to recurrence was 1 to 9.8 years. Margin positivity (p=0.002) and invasion deeper than the submucosal layer (p=0.007) were associated with higher recurrence rates. However, there was no significant association between recurrence and intestinal metaplasia, atrophic gastritis, or Helicobacter pylori infection.

Conclusions

Most patients with GNETs in this study had grade I and type I tumors, and the overall prognosis was favorable. Patients with risk factors for recurrence warrant further investigation. Those presenting margin positivity or deep invasion after resection should be closely monitored and undergo follow-up examinations, as necessary.

A unified framework harnessing multi-scale feature ensemble and attention mechanism for gastric polyp and protrusion identification in gastroscope imaging

This study aims to address the diagnostic challenges in distinguishing gastric polyps from protrusions, emphasizing the need for accurate and cost-effective diagnosis strategies. It explores the application of Convolutional Neural Networks (CNNs) to improve diagnostic accuracy. This research introduces MultiAttentiveScopeNet, a deep learning model that incorporates multi-layer feature ensemble and attention mechanisms to enhance gastroscopy image analysis accuracy. A weakly supervised labeling strategy was employed to construct a large multi-class gastroscopy image dataset for training and validation. MultiAttentiveScopeNet demonstrates significant improvements in prediction accuracy and interpretability. The integrated attention mechanism effectively identifies critical areas in images to aid clinical decisions. Its multi-layer feature ensemble enables robust analysis of complex gastroscopy images. Comparative testing against human experts shows exceptional diagnostic performance, with accuracy, micro and macro precision, micro and macro recall, and micro and macro AUC reaching 0.9308, 0.9312, 0.9325, 0.9283, 0.9308, 0.9847 and 0.9853 respectively. This highlights its potential as an effective tool for primary healthcare settings. This study provides a comprehensive solution to address diagnostic challenges differentiating gastric polyps and protrusions. MultiAttentiveScopeNet improves accuracy and interpretability, demonstrating the potential of deep learning for gastroscopy image analysis. The constructed dataset facilitates continued model optimization and validation. The model shows promise in enhancing diagnostic outcomes in primary care.

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.

Modern Management of Gastric Neuroendocrine Neoplasms

OPINION STATEMENT

Gastric neuroendocrine neoplasms (G-NENs) are a heterogeneous group of tumors that broadly fall into two groups. The first group, driven by oversecretion of gastrin, are generally multifocal, small, and behave indolently with a low (but non-zero) risk of progression and metastatic spread. They are conventionally categorized into type 1, with endogenous gastric-based overproduction of gastrin, and type 2 G-NEN, with overproduction of gastrin from an extra-gastric gastrin-secreting tumor. The second group, termed type 3 G-NEN, occur spontaneously and are potentially more aggressive, having a clinical course analogous to other neuroendocrine tumors of the gastrointestinal tract. Type 1 G-NEN can be managed with endoscopic surveillance and resection of visible lesions with great success, reserving surgery for the rare high-risk lesion, whereas surgical resection of the causative gastrin-secreting tumor in type 2 G-NEN is usually curative. Type 3 G-NEN is usually managed with formal surgical resection but there is growing evidence that limited surgery or even endoscopic resection in appropriately selected patients with low risk is both safe and effective. A novel subtype of G-NEN, associated with long-term proton pump inhibitor usage, is increasing in incidence. The pathophysiology seems to parallel type 1 G-NEN. In the setting of metastatic disease, which can occur in any subtype but is most common by far in type 3 G-NEN, the lack of trial data unique to G-NEN results in extrapolation of strategies and agents for treatment of non-gastric neuroendocrine disease. The rapid pace of development in this area is likely to benefit the metastatic G-NEN patient as well. As treatment is predicate on type of G-NEN, establishing the etiology of the lesion is crucial but growing knowledge of G-NEN pathophysiology and close collaboration between pathologists, gastroenterologists, radiologists, surgeons, and oncologists have enabled a growing trend towards de-escalation and less-invasive treatment paradigms.

Personalized treatment of well-differentiated gastric neuroendocrine tumors based on clinicopathological classification and grading: A multicenter retrospective study

BACKGROUND

The incidence of well-differentiated gastric neuroendocrine tumors (G-NET) is increasing annually, and while they have a good prognosis and low mortality rate, their high recurrence rate makes treatment options controversial. This study aims to determine the relationship between individualized treatment plans and the recurrence of G-NET.

METHODS

We performed a multicenter, retrospective study of 94 patients with highly differentiated G-NET and treated at Peking Union Medical College Hospital, Yantai Yuhuangding Hospital, and Beijing Zhong-Neng-Jian Hospital from November 2015 to September 2023. Risk factors for recurrence of G-NETs were investigated using chi-squared test and multifactorial logistic regression analysis.

RESULTS

After a median follow-up of 49 months, the overall recurrence rate among the 94 G-NET patients was 14% (13/94). The recurrence rates of endoscopic mucosal resection (EMR), endoscopic submucosal dissection (ESD), somatostatin analog (SSA) therapy, and surgery were 43% (6/14), 10% (5/49), 5% (1/22), and 11% (1/9), respectively. Post-treatment recurrence rates were significantly different ( P = 0.014) among four treatments (EMR, ESD, SSA, and surgery), and further subgroup comparisons revealed lower recurrence rates in the ESD and SSA groups than in the EMR group. From the second month onward, SSA therapy considerably reduced the gastrin levels from 1081.0 (571.5, 2472.8) pg/mL to 461.5 (255.3, 795.0) pg/mL ( Z = -3.521, P <0.001). Both chi-squared test and multifactorial logistic regression analysis suggested that among the clinicopathological parameters studied, only the pre-treatment gastrin level ( P = 0.018 and 0.005) and the type of treatment ( P = 0.014 and 0.017) were significantly associated with G-NET recurrence.

CONCLUSIONS

Individualized treatment strategies may reduce the risk of relapse after G-NET treatment. Long-term SSA therapy may be a secure and efficacious treatment option for type 1 G-NET with more than six lesions, and it substantially decreases the incidence of post-treatment recurrence.

[Characteristics of gastric neuroendocrine tumors and the PDX-1 transcription factor expression]

OBJECTIVE

To study the features of gastric neuroendocrine tumors (NETs) and the diagnostic and prognostic significance of PDX-1 expression in them.

MATERIAL AND METHODS

207 NETs identified in 56 men and 115 women (59 had multiple NETs), and 94 cases of gastric cancer (comparison group) were studied morphologically and immunohistochemically.

RESULTS

In more than half of the cases (54.93%), NETs were localized in the body of the stomach; the cardiac and antral parts of the stomach accounted for 8.64% and 11.73%, respectively. NETs of the cardiac region predominated in men, and of the body and antrum - in women. NETs of the cardiac region predominated in men, and of the body and antrum - in women. The vast majority of NETs were highly differentiated (89.20%), of which Grade 1, 2 and 3 were 55.41%, 40.76% and 3.82%, respectively. Neuroendocrine carcinomas (NEC) accounted for 10.80% of all NET cases. NECs were more often localized in the cardiac part of the stomach and accounted for 35.71% of all NETs in the cardiac part. The share of NEC among all NETs of the antrum was 15.79%, of the body of the stomach - only 3.37%. Metastases were found in 17.90% of NETs. Expression of PDX-1 was detected in 44.73% of NETs, 70% of NECs and 74.50% of gastric cancers.

CONCLUSION

PDX-1 is involved in the mechanisms of precancerous and cancerous lesions of the stomach and its overexpression is detected in the majority of the most malignant NETs and gastric cancers.

Management of type 1 gastric neuroendocrine tumors: an 11-year retrospective single-center study

BACKGROUND

Type 1 gastric neuroendocrine tumors (NETs) are relatively rare to the extent that some physicians have little experience in diagnosing and treating them. The purpose of this study was to increase the understanding of the disease by analyzing and summarizing the management and prognoses of patients with type 1 gastric NETs at our center.

METHODS

The data of 229 patients (59.4% female) with type 1 gastric NETs who were treated at our center during 2011-2022 were retrospectively analyzed.

RESULTS

The average patient age was 50.5 ± 10.8 years. Multiple tumors affected 72.5% of the patients; 66.4% of the tumors were < 1 cm, 69.4% were NET G1, and 2.2% were stage III-IV. A total of 76.9% of the patients had received endoscopic management, 60.7% had received traditional Chinese medicine treatment, 10.5% received somatostatin analogues treatment, and 6.6% underwent surgical resection. Seventy patients (41.2%) experienced the first recurrence after a median follow-up of 31 months (range: 2-122 months), and the median recurrence-free time was 43 months. The 1-, 2-, and 3-year cumulative recurrence-free survival rates were 71.8%, 56.8%, and 50.3%, respectively. During a median follow-up of 39 months (range: 2-132 months), one patient had bilateral pulmonary metastasis, and no disease-related deaths were observed.

CONCLUSION

Type 1 gastric NETs have a high recurrence rate and a long disease course, underscoring the importance of long-term and comprehensive management.

Effective Treatment of SSTR2-Positive Small Cell Lung Cancer Using 211At-Containing Targeted α-Particle Therapy Agent Which Promotes Endogenous Antitumor Immune Response

Small cell lung cancer (SCLC) is a neuroendocrine tumor with a high degree of malignancy. Due to limited treatment options, patients with SCLC have a poor prognosis. We have found, however, that intravenously administered octreotide (Oct) armed with astatine-211 ([211At]SAB-Oct) is effective against a somatostatin receptor 2 (SSTR2)-positive SCLC tumor in SCLC tumor-bearing BALB/c nude mice. In biodistribution analysis, [211At]SAB-Oct achieved the highest concentration in the SCLC tumors up to 3 h after injection as time proceeded. A single intravenous injection of [211At]SAB-Oct (370 kBq) was sufficient to suppress SSTR2-positive SCLC tumor growth in treated mice by inducing DNA double-strand breaks. Additionally, a multitreatment course (370 kBq followed by twice doses of 370 kBq for a total of 1110 kBq) inhibited the growth of the tumor compared to the untreated control group without significant off-target toxicity. Surprisingly, we found that [211At]SAB-Oct could up-regulate the expressions of calreticulin and major histocompatibility complex I (MHC-I) on the tumor cell membrane surface, suggesting that α-particle internal irradiation may activate an endogenous antitumor immune response through the regulation of immune cells in the tumor microenvironment, which could synergically enhance the efficacy of immunotherapy. We conclude that [211At]SAB-Oct is a potential new therapeutic option for SSTR2-positive SCLC.

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.

Factors Predicting Type I Gastric Neuroendocrine Neoplasia Recurrence: A Single-Center Study

Type I gastric neuroendocrine neoplasms (gNENs) are associated with atrophic gastritis and have a high recurrence rate, which means frequent endoscopies are required. The objective of this study was to identify factors predicting the local recurrence of type I gNENs. The clinical course and the pathological and biochemical data of patients with type I gNENs treated at Bnai Zion Medical Center between 2006 and 2022 were analyzed retrospectively. Twenty-seven type I gNENs were evaluated. The follow-up period was 41 months (range: 11–288 months). Recurrence of the tumor occurred in 13/27 (48%) patients after 35 months (median (M), interquartile range (IQR): 21–67.5). Serum gastrin levels were significantly higher in patients with recurrent disease versus patients with non-recurrent disease (788 vs. 394 ng/L; p = 0.047), while the Ki-67 index was significantly lower in patients with recurrent disease versus patients with non-recurrent disease (1% vs. 3.5%; p = 0.035). Tumor size, mitotic count, and serum chromogranin A levels did not correlate with recurrence. The present study emphasizes the role of gastrin in the pathogenesis of gNEN recurrence and highlights the debate regarding the ability of the Ki-67 index to predict the clinical course of this disease.

Endoscopic surveillance alone is feasible and safe in type I gastric neuroendocrine neoplasms less than 10 mm in diameter

PURPOSE

Type I gastric neuroendocrine neoplasms (g-NENs) have a low risk of metastasis and a generally favourable prognosis. Patients with small type I g-NENs (≤10 mm) frequently require no treatment, whereas those with larger polyps usually undergo resection. We evaluated the safety and outcomes of endoscopic surveillance after no initial treatment in selected patients with type I g-NENs.

METHODS

Retrospective analysis of type I g-NEN patients across two European Neuroendocrine Tumour Society Centers of Excellence 2003-2019.

RESULTS

Following initial assessment, 87 of 115 patients with type I g-NEN (75 with polyps ≤10 mm) received no initial treatment and underwent endoscopic surveillance. 79/87 (91%) demonstrated no clinically meaningful change in tumour size or grade over a median 62 month follow up. Only two patients developed NEN progression that required a change in management and two other patients developed gastric adenocarcinoma/high grade dysplasia; all four initially had ≥11 mm g-NENs.

CONCLUSIONS

Patients with ≤10 mm type I g-NENs were unlikely to develop clinically significant tumour progression and in most cases, resection was not needed. The endoscopic surveillance interval could therefore potentially be safely increased to every 2-3 years in such patients. However, lifelong surveillance is still advocated due to the additional risk of developing gastric adenocarcinoma.