The Clinicopathological Significance of Tumor Cell Subtyping in Appendiceal Neuroendocrine Tumors: A Series of 135 Tumors.

Appendiceal neuroendocrine tumors (NETs) are common and often are identified as incidental lesions at the time of appendectomy. The guidelines for management are based on tumor size, degree of invasion, and the Ki67 proliferation index. Most small bowel NETs are composed of serotonin-producing EC-cells, but there are multiple other neuroendocrine cell types. In the rectum, there are L-cell tumors that express peptide YY (PYY), glucagon-like peptides (GLPs), and pancreatic polypeptide (PP); they are thought to have a better prognosis than serotonin-producing tumors. We investigated whether the appendix has distinct neuroendocrine tumor types based on cell type and whether that distinction has clinical significance. We collected 135 appendiceal NETs from the pathology archives of UHN Toronto and UHCMC (Cleveland). We analyzed the expression of biomarkers including CDX2, SATB2, PSAP, serotonin, glucagon (that detects GLPs), PYY, and pancreatic polypeptide (PP) and correlated the results with clinicopathologic parameters. Immunohistochemistry identified three types of appendiceal NETs. There were 75 (56%) classified as EC-cell tumors and 37 (27%) classified as L-cell tumors; the remaining 23 (17%) expressed serotonin and one of the L-cell biomarkers and were classified as mixed. EC-cell tumors were significantly larger with more extensive invasion involving the muscularis propria, subserosa, and mesoappendix compared with L-cell tumors. Mixed tumors were intermediate in all of these parameters. Both EC-cell and mixed tumors had lymphatic and/or vascular invasion while L-cell tumors had none. Unlike EC-cell NETs, L-cell tumors were not associated with lymph node metastasis. Tumor type correlated with pT stage and the only patient with distant metastatic disease in this series had an EC-cell tumor. Our study confirms that appendiceal NETs are not a homogeneous tumor population. There are at least three types of appendiceal NET, including EC-cell, L-cell, and mixed tumors. This information is important for surveillance of patients, as monitoring urinary 5HIAA levels is only appropriate for patients with serotonin-producing tumors, whereas measurement of GLPs and/or PP is more appropriate for patients with L-cell tumors. Our data also show that tumor type is of significance with EC-cell tumors exhibiting the most aggressive behavior.

Assessment of Interobserver Agreement Among Gynecologic Pathologists Between Three-Tier Versus Binary Pattern-based Classification Systems for HPV-associated Endocervical Adenocarcinoma.

The three-tier (A vs. B vs. C) pattern-based (Silva) classification system is a strong and fairly reproducible predictor of the risk of lymph node involvement and recurrence of human papillomavirus (HPV)-associated endocervical adenocarcinoma (EA). Recently, a binary pattern-based classification system has been proposed which incorporates the Silva pattern and lymphovascular invasion (LVI) to assign tumors as "low risk" or "high risk" and this may have superior prognostic significance compared with the three-tier system as well as current International Federation of Gynecology and Obstetrics (FIGO) staging of cervix-confined disease. The interobserver reproducibility of this binary system, however, is unknown. Representative slides from 59 HPV-associated EAs (1-3 slides/case) were independently reviewed by 5 gynecologic pathologists who participated in an online training module before the study. In the first review, a pattern was assigned using the three-tier system. On the second review, a "low risk" or "high risk" designation was assigned and the presence or absence of LVI was specifically documented. Interobserver agreement was assessed using Fleiss' kappa. The binary system showed improved interobserver agreement (kappa=0.634) compared with the three-tier system (kappa=0.564), with a higher proportion of cases having agreement between at least 4/5 reviewers (86% vs. 73%). Nineteen and 8 cases showed improved and worse interobserver agreement using the binary system, respectively; the remainder showed no change. 3/5 reviewers showed no intraobserver discrepancy while the remaining 2 did in a small subset of cases (n=2 and 4, respectively). In this study, a binary pattern-based classification system showed improved interobserver agreement compared with the traditional three-tier system.