Neuroendocrine tumors (NETs) are a heterogeneous group of tumors originating in various anatomic locations. The management of this disease poses a significant challenge because of the heterogeneous clinical presentations and varying degree of aggressiveness. The recent completion of several phase III trials, including those evaluating octreotide, sunitinib, and everolimus, demonstrate that rigorous evaluation of novel agents in this disease is possible and can lead to practice-changing outcomes. Nevertheless, there are many aspects to the treatment of NETs that remain unclear and controversial. The North American Neuroendocrine Tumor Society (NANETS) published a set of consensus guidelines in 2010 which provided an overview for the treatment of patients with these malignancies. Here, we present a set of consensus tables intended to complement these guidelines and serve as a quick, accessible reference for the practicing physician.

Introduction: The TNM classification for lung cancer, originally designed for NSCLC, is applied to staging of bronchopulmonary carcinoid tumors. The validity of the eighth edition of the staging system for carcinoid tumors has not been assessed. In this study, we evaluated its prognostic accuracy by using data from a large national population-based cancer registry.Methods: Patients with typical and atypical bronchopulmonary carcinoids diagnosed between 2000 and 2013 were identified from the National Cancer Institute's Surveillance, Epidemiology and End Results registry. We used competing risks analysis to compare 10-year diseasespecific survival (DSS) across stages.Results: Overall, 4645 patients with bronchopulmonary carcinoid tumors were identified. Worsening DSS with increasing TNM status and stage was demonstrated across both typical and atypical carcinoids, with overlaps between adjacent subcategories. The combined stages (I versus II, II versus III, and III versus IV) showed greater separation in DSS despite persistent overlaps between groups. For typical carcinoids, we found decreased DSS for stages II, III, and IV, with hazard ratios of 3.8 (95% confidence interval [CI]: 2.6-5.6), 4.3 (95% CI: 3.0-6.1), and 9.0 (95% CI: 6.1-13.1), respectively, compared with stage I. Conclusion:The combined stage categories of the eighth edition of the TNM staging system provide useful information on outcomes for typical and atypical carcinoids. However, persistent overlaps in combined stage and subcategories of the staging system limit the usefulness of the TNM staging system, particularly in intermediate stages. These limitations suggest the need for future further study and refinement.

Background & Aims High-resolution microendoscopy is an optical imaging technique with the potential to improve the accuracy of endoscopic screening for esophageal squamous neoplasia. Although these microscopic images can readily be interpreted by trained personnel, quantitative image analysis software could facilitate the use of this technology in low-resource settings. In this study we developed and evaluated quantitative image analysis criteria for the evaluation of neoplastic and non-neoplastic squamous esophageal mucosa. Methods We performed image analysis of 177 patients undergoing standard upper endoscopy for screening or surveillance of esophageal squamous neoplasia, using high-resolution microendoscopy, at 2 hospitals in China and 1 in the United States from May 2010 to October 2012. Biopsies were collected from imaged sites (n=375); a consensus diagnosis was provided by 2 expert gastrointestinal pathologists and used as the standard. Results Quantitative information from the high-resolution images was used to develop an algorithm to identify high-grade squamous dysplasia or invasive squamous cell cancer, based on histopathology findings. Optimal performance was obtained using mean nuclear area as the basis for classification, resulting in sensitivities and specificities of 93% and 92% in the training set, 87% and 97% in the test set, and 84% and 95% in an independent validation set, respectively. Conclusions High-resolution microendoscopy with quantitative image analysis can aid in the identification of esophageal squamous neoplasia. Use of software-based image guides may overcome issues of training and expertise in low-resource settings, allowing for widespread use of these optical biopsy technologies.

Pancreatic neuroendocrine tumors (PNETs) are relatively rare; however, the incidence has increased over the last few decades. They are classified as functional or non-functional tumors according to the presence of associated clinical symptoms. The majority are non-functional tumors. For classification and staging, the World Health Organization 2010 classification system is the most commonly accepted. Chromogranin A is the most sensitive marker but has insufficient specificity. In general, PNETs are hypervascular tumors, and multiphasic contrast-enhanced computed tomography is considered the first choice for imaging study. Multiphasic magnetic resonance imaging can detect PNETs smaller than 2 cm and small liver metastasis compared with other modalities. Somatostatin receptor scintigraphy is often used in cases where functional PNETs are suspected. Positron emission tomography (PET) scan with 18F-fluorodeoxyglucose cannot visualize PNETs, but PET with 68-Ga DOTATATE can. Endoscopic ultrasonography can characterize smaller PNETs using contrast and confirm histology through fine needle aspiration or biopsy. In this article, we review the characteristics of grading systems and diagnostic modalities commonly used for PNETs.

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs. Using quantitative barcoded small RNA sequencing and state-of-the-art sequence annotation, we generated comprehensive miRNA expression profiles from archived pancreatic, ileal, appendiceal and rectal NETs. Following data preprocessing, we randomly assigned sample profiles to discovery (80%) and validation (20%) sets prior to data mining using machine-learning techniques. High expression analyses indicated that miR-375 was the most abundant individual miRNA and miRNA cistron in all samples. Leveraging prior knowledge that GEP-NET behavior is influenced by embryonic derivation, we developed a dual-layer hierarchical classifier for differentiating GEP-NET types. In the first layer, our classifier discriminated midgut (ileum, appendix) from non-midgut (rectum, pancreas) NETs based on miR-615 and -92b expression. In the second layer, our classifier discriminated ileal from appendiceal NETs based on miR-125b, -192 and -149 expression, and rectal from pancreatic NETs based on miR-429 and -487b expression. Our classifier achieved overall accuracies of 98.5% and 94.4% in discovery and validation sets, respectively. We also found provisional evidence that low- and intermediate-grade pancreatic NETs can be discriminated based on miR-328 expression. GEP-NETs can be reliably classified and potentially graded using a limited panel of miRNA markers, complementing morphological and immunohistochemistry-based approaches to histologic evaluation.