How should incidental NEN of the pancreas and gastrointestinal tract be followed?

Comprehensive genomic and transcriptomic characterization of high-grade gastro-entero-pancreatic neoplasms

BACKGROUND

High-grade gastro-entero-pancreatic neoplasms (HG GEP-NENs) can be stratified according to their morphology and Ki-67 values into three prognostic classes: neuroendocrine tumors grade 3 (NETs G3), neuroendocrine carcinomas with Ki-67 < 55% (NECs <55) and NECs with Ki-67 ≥ 55% (NECs ≥55).

METHODS

We analyzed a cohort of 49 HG GEP-NENs by targeted Next-Generation Sequencing (TrueSight Oncology 500), RNA-seq, and immunohistochemistry for p53, Rb1, SSTR-2A, and PD-L1.

RESULTS

Frequent genomic alterations affected TP53 (26%), APC (20%), KRAS and MEN1 (both 11%) genes. NET G3 were enriched in MEN1 (p = 0.02) mutations, while both NECs groups were enriched in TP53 (p = 0.001), APC (p = 0.002) and KRAS (p = 0.02) mutations and tumors with TMB ≥ 10 muts/Mb (p = 0.01). No differentially expressed (DE) gene was found between NECs <55% and NECs ≥55%, while 1129 DE genes were identified between NET G3 and NECs. A slight enrichment of CD4+ and CD8+ T cells in NECs and of cancer-associated fibroblasts and macrophages (M2-like) in NET G3. Multivariate analysis identified histologic type and Rb1 loss as independent prognostic factors for overall survival.

CONCLUSIONS

This study showed that GEP-NET G3 and GEP-NECs exhibit clear genomic and transcriptomic differences, differently from GEP-NECs <55% and GEP-NECs ≥55%, and provided molecular findings with prognostic and potentially predictive value.

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.

Update on gastroenteropancreatic neuroendocrine tumors

The incidence gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) has dramatically risen over the last three decades, probably due to the increased detection of asymptomatic lesions. The diagnostic work-up for patients with suspected GEP-NENs is based on conventional imaging, endoscopy, pathology, and functional imaging, including ⁶⁸Gallium-DOTATATE PET and ¹⁸F-FDG PET. The choice of the best treatment strategy should be based on the evaluation of tumor-related features and patient's characteristics. A conservative management, consisting of active surveillance or endoscopic resection, has been advocated for patients with small, incidentally discovered, nonfunctioning tumors without features of aggressiveness. On the other hand, surgery with lymphadenectomy, also with a minimally invasive approach, represents the gold standard for the curative treatment of localized disease. Moreover, surgical resection plays an important role also in the context of a multimodal treatment strategy for patients with advanced GEP-NENs. Finally, a wide range of medical therapies, comprising somatostatin analogues, peptide receptor radionuclide therapy, target therapies and several chemotherapy regimens, can be offered to patients with advanced GEP-NENs not amenable of surgical resection, according to the biological and molecular features of their disease.

Evaluating the ACS-NSQIP Risk Calculator in Primary GI Neuroendocrine Tumor: Results from the United States Neuroendocrine Tumor Study Group

The ACS established an online risk calculator to help surgeons make patient-specific estimates of postoperative morbidity and mortality. Our objective was to assess the accuracy of the ACS-NSQIP calculator for estimating risk after curative intent resection for primary GI neuroendocrine tumors (GI-NETs). Adult patients with GI-NET who underwent complete resection from 2000 to 2017 were identified using a multi-institutional database, including data from eight academic medical centers. The ability of the NSQIP calculator to accurately predict a particular outcome was assessed using receiver operating characteristic curves and the area under the curve (AUC). Seven hundred three patients were identified who met inclusion criteria. The most commonly performed procedures were resection of the small intestine with anastomosis (N = 193, 26%) and partial colectomy with anastomosis (N = 136, 18%). The majority of patients were younger than 65 years (N = 482, 37%) and ASA Class III (N = 337, 48%). The most common comorbidities were diabetes (N = 128, 18%) and hypertension (N = 395, 56%). Complications among these patients based on ACS NSQIP definitions included any complication (N = 132, 19%), serious complication (N = 118, 17%), pneumonia (N = 7, 1.0%), cardiac complication (N = 1, 0.01%), SSI (N = 80, 11.4%), UTI (N = 17, 2.4%), venous thromboembolism (N = 18, 2.5%), renal failure (N = 16, 2.3%), return to the operating room (N = 27, 3.8%), discharge to nursing/rehabilitation (N = 22, 3.1%), and 30-day mortality (N = 9, 1.3%). The calculator provided reasonable estimates of risk for pneumonia (AUC = 0.721), cardiac complication (AUC = 0.773), UTI (AUC = 0.716), and discharge to nursing/ rehabilitation (AUC = 0.779) and performed poorly (AUC < 0.7) for all other complications Fig. 1). The ACS-NSQIP risk calculator estimates a similar proportion of risk to actual events in patients with GI-NET but has low specificity for identifying the correct patients for many types of complications. The risk calculator may require modification for some patient populations.

Combination of laparoscopy and endoscopy as an option for treatment patients with gastric neuroendocrine tumors

Background

Neuroendocrine tumors (NETs) are relatively rare neoplasms with the increasing survival due to the development of early diagnostics. There is no universal position in treatment and follow up of small (~20 mm) gastric NETs.

Clinical cases

Two female patients 51 and 66 y.o. with multiple gastric NETs <2 cm were observed in our department. In both cases treatment was performed by combination of two minimally invasive technologies: laparoscopy and gastroscopy. According to the localization of tumors in one case intraluminal gastric resection controlled by laparoscopy was performed. In the second case laparoscopic gastric resection with gastroscopy assistance was done.

Discussion

There are two positions for surgical treatment of small NETs: to operate as the typical premalignant neoplasm or to make submucosa resections. We demonstrated combination of laparoscopy and gastroscopy as feasible approach with minimal risk of complications.