Comparison of Monitor-Image and Printout-Image Methods in Ki-67 Scoring of Gastroenteropancreatic Neuroendocrine Tumors

Update from the 5th Edition of the World Health Organization Classification of Head and Neck Tumors: Overview of the 2022 WHO Classification of Head and Neck Neuroendocrine Neoplasms

This review article provides a brief overview of the new WHO classification by adopting a question-answer model to highlight the spectrum of head and neck neuroendocrine neoplasms which includes epithelial neuroendocrine neoplasms (neuroendocrine tumors and neuroendocrine carcinomas) arising from upper aerodigestive tract and salivary glands, and special neuroendocrine neoplasms including middle ear neuroendocrine tumors (MeNET), ectopic or invasive pituitary neuroendocrine tumors (PitNET; formerly known as pituitary adenoma) and Merkel cell carcinoma as well as non-epithelial neuroendocrine neoplasms (paragangliomas). The new WHO classification follows the IARC/WHO nomenclature framework and restricts the diagnostic term of neuroendocrine carcinoma to poorly differentiated epithelial neuroendocrine neoplasms. In this classification, well-differentiated epithelial neuroendocrine neoplasms are termed as neuroendocrine tumors (NET), and are graded as G1 NET (no necrosis and < 2 mitoses per 2 mm²; Ki67 < 20%), G2 NET (necrosis or 2-10 mitoses per 2 mm², and Ki67 < 20%) and G3 NET (> 10 mitoses per 2 mm² or Ki67 > 20%, and absence of poorly differentiated cytomorphology). Neuroendocrine carcinomas (> 10 mitoses per 2 mm², Ki67 > 20%, and often associated with a Ki67 > 55%) are further subtyped based on cytomorphological characteristics as small cell and large cell neuroendocrine carcinomas. Unlike neuroendocrine carcinomas, head and neck NETs typically show no aberrant p53 expression or loss of RB reactivity. Ectopic or invasive PitNETs are subtyped using pituitary transcription factors (PIT1, TPIT, SF1, GATA3, ER-alpha), hormones and keratins (e.g., CAM5.2). The new classification emphasizes a strict correlation of morphology and immunohistochemical findings in the accurate diagnosis of neuroendocrine neoplasms. A particular emphasis on the role of biomarkers in the confirmation of the neuroendocrine nature of a neoplasm and in the distinction of various neuroendocrine neoplasms is provided by reviewing ancillary tools that are available to pathologists in the diagnostic workup of head and neck neuroendocrine neoplasms. Furthermore, the role of molecular immunohistochemistry in the diagnostic workup of head and neck paragangliomas is discussed. The unmet needs in the field of head and neck neuroendocrine neoplasms are also discussed in this article. The new WHO classification is an important step forward to ensure accurate diagnosis that will also form the basis of ongoing research in this field.

Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning

The Ki‐67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki‐67 PI requires calculation of Ki‐67‐positive and Ki‐67‐negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning‐based Ki‐67 PI algorithm (KAI) that objectively calculates Ki‐67 PI. Our study material consisted of NETs divided into training (n = 39), testing (n = 124), and validation (n = 60) series. All slides were digitized and processed in the Aiforia^® Create (Aiforia Technologies, Helsinki, Finland) platform. The ICC between the pathologists and the KAI was 0.89. In 46% of the tumors, the Ki‐67 PIs calculated by the pathologists and the KAI were the same. In 12% of the tumors, the Ki‐67 PI calculated by the KAI was 1% lower and in 42% of the tumors on average 3% higher. The DL‐based Ki‐67 PI algorithm yields results similar to human observers. While the algorithm cannot replace the pathologist, it can assist in the laborious Ki‐67 PI assessment of NETs. In the future, this approach could be useful in, for example, multi‐center clinical trials where objective estimation of Ki‐67 PI is crucial.

Ki-67 Proliferation Index Assessment in Gastroenteropancreatic Neuroendocrine Tumors by Digital Image Analysis With Stringent Case and Hotspot Level Concordance Requirements

OBJECTIVES

The Ki-67 proliferation index is integral to gastroenteropancreatic neuroendocrine tumor (GEP-NET) assessment. Automated Ki-67 measurement would aid clinical workflows, but adoption has lagged owing to concerns of nonequivalency. We sought to address this concern by comparing 2 digital image analysis (DIA) platforms to manual counting with same-case/different-hotspot and same-hotspot/different-methodology concordance assessment.

METHODS

We assembled a cohort of GEP-NETs (n = 20) from 16 patients. Two sets of Ki-67 hotspots were manually counted by three observers and by two DIA platforms, QuantCenter and HALO. Concordance between methods and observers was assessed using intraclass correlation coefficient (ICC) measures. For each comparison pair, the number of cases within ±0.2xKi-67 of its comparator was assessed.

RESULTS

DIA Ki-67 showed excellent correlation with manual counting, and ICC was excellent in both within-hotspot and case-level assessments. In expert-vs-DIA, DIA-vs-DIA, or expert-vs-expert comparisons, the best-performing was DIA Ki-67 by QuantCenter, which showed 65% cases within ±0.2xKi-67 of manual counting.

CONCLUSIONS

Ki-67 measurement by DIA is highly correlated with expert-assessed values. However, close concordance by strict criteria (>80% within ±0.2xKi-67) is not seen with DIA-vs-expert or expert-vs-expert comparisons. The results show analytic noninferiority and support widespread adoption of carefully optimized and validated DIA Ki-67.

Ki-67 proliferation index in neuroendocrine tumors: Can augmented reality microscopy with image analysis improve scoring?

BACKGROUND

The Ki-67 index is important for grading neuroendocrine tumors (NETs) in cytology. However, different counting methods exist. Recently, augmented reality microscopy (ARM) has enabled real-time image analysis using glass slides. The objective of the current study was to compare different traditional Ki-67 scoring methods in cell block material with newer methods such as ARM.

METHODS

Ki-67 immunostained slides from 50 NETs of varying grades were retrieved (39 from the pancreas and 11 metastases). Methods with which to quantify the Ki-67 index in up to 3 hot spots included: 1) "eyeball" estimation (EE); 2) printed image manual counting (PIMC); 3) ARM with live image analysis; and 4) image analysis using whole-slide images (WSI) (field of view [FOV] and the entire slide).

RESULTS

The Ki-67 index obtained using the different methods varied. The pairwise kappa results varied from no agreement for image analysis using digital image analysis WSI (FOV) and histology to near-perfect agreement for ARM and PIMC. Using surgical pathology as the gold standard, the EE method was found to have the highest concordance rate (84.2%), followed by WSI analysis of the entire slide (73.7%) and then both the ARM and PIMC methods (63.2% for both). The PIMC method was the most time-consuming whereas image analysis using WSI (FOV) was the fastest method followed by ARM.

CONCLUSIONS

The Ki-67 index for NETs in cell block material varied by the method used for scoring, which may affect grade. PIMC was the most time-consuming method, and EE had the highest concordance rate. Although real-time automated counting using image analysis demonstrated inaccuracies, ARM streamlined and hastened the task of Ki-67 quantification in NETs.