Grading pancreatic neuroendocrine neoplasms by Ki-67 staining on cytology cell blocks: manual count and digital image analysis of 58 cases

Endoscopic Ultrasound-Guided Fine Needle Acquisition for Evaluation of Pancreatic Neuroendocrine Tumors: A Meta-Analysis

AIMS

The aim of this meta-analysis was to assess the diagnostic performance of EUS-FNA/B in patients with panNETs.

METHODS

We conducted a computerized search of the MEDLINE and Embase databases to identify relevant articles. The primary outcomes involved grading concordance rate, diagnostic rate, and correlation coefficient (Cohen's κ) for FNA/B samples compared with surgical specimens. Secondary outcomes included sample adequacy, mean number of passes, and adverse events.

RESULTS

Forty-five studies involving 2978 patients were finally included. The pooled concordance rate between EUS-FNA/B and surgical grading was 0.77 (95% CI: 0.73-0.80; I2=48.2%). A significantly higher level of concordance was observed in G1 subgroup (0.88, 95% CI: 0.84-0.91), whereas the G2 subgroup revealed the lowest level of agreement (0.59, 95% CI: 0.52-0.65; P < 0.001). Pooled diagnostic rate for FNA/B sampling was 0.83 (95% CI: 0.79-0.86; I2=63.3%). In addition, FNB outperformed FNA in terms of sample adequacy (0.93 for FNB vs. 0.81 for FNA; P=0.007) and number of needle passes required (2.53 for FNB vs. 3.32 for FNA; P=0.013). Moreover, the overall level of agreement for grading was moderate (κ=0.59, 95% CI: 0.49-0.68; I2=84.5%). There were a limited number of adverse events that had minor influence on patient outcomes (0.03, 95% CI: 0.02-0.05; I2=19.2%).

CONCLUSIONS

EUS-FNA/B is a reliable approach for the diagnosis and preoperative grading of panNET, with FNB demonstrating superior performance compared with FNA.

Pancreas Fine Needle Aspiration: Current and Future Impact on Patient Care

Pancreatic lesions can be solid or cystic and comprise a wide range of benign, premalignant, and malignant entities. Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is the current primary sampling method for the preoperative diagnosis of pancreatic lesions. Optimal handling of cytology/small tissue specimens is critical to ensure that the often-scant diagnostic material is appropriately utilized for ancillary and/or molecular studies when appropriate. Ultimately, evaluation of EUS-FNA cytology and small biopsy material can provide accurate and timely diagnoses to guide patient management and triage them to surveillance or surgical intervention.

Doing more with less: integrating small biopsies in cytology practice

Cytopathologists are at the forefront of specimen acquisition during many different procedures while providing rapid on site evaluation (ROSE). This has added pressure to cytopathologists as more and more ancillary testing is being requested on smaller amounts of tissue. By focusing on the most common organ sites: lung, head and neck, and pancreas, there is a discussion of what the cytopathologist needs to know to triage tissue successfully. Finally, there is a discussion of the logistical aspects of integrating small biopsies into everyday practice.

Automated imaging analysis of Ki-67 immunohistochemistry on whole slide images of cell blocks from pancreatic neuroendocrine neoplasms

INTRODUCTION

Accurate grading of pancreatic neuroendocrine tumors (PanNETs) relies on the assessment of Ki-67 immunohistochemistry (IHC). While digital imaging analysis (DIA) has been employed for Ki-67 IHC assessment in surgical specimens, its applicability to cytologic specimens remains underexplored. This study aimed to evaluate an automated DIA for assessing Ki-67 IHC on PanNET cell blocks.

MATERIALS AND METHODS

The study included 61 consecutive PanNETs and 5 pancreatic neuroendocrine carcinomas. Ki-67 IHC slides from cell blocks were digitally scanned into whole slide images using Philips IntelliSite Scanners and analyzed in batches using the Visiopharm Ki-67 App in a digital workflow. Ki-67 scores obtained through DIA were compared to pathologists' manual scores.

RESULTS

The Pearson correlation coefficient of the percentage of Ki-67-stained nuclei between DIA reads and the originally reported reads was 0.9681. Concordance between DIA Ki-67 grades and pathologists' Ki-67 grades was observed in 92.4% (61/66) of cases with the calculated Cohen's Kappa coefficient of 0.862 (almost perfect agreement). Discordance between DIA and pathologists' consensus reads occurred in 5 PanNET cases which were upgraded from G1 to G2 by DIA due to contaminated Ki-67-stained inflammatory cells.

CONCLUSIONS

DIA demonstrated excellent concordance with pathologists' assessments, with only minor grading discrepancies. However, the essential role of pathologists in confirming results is emphasized to enhance overall accuracy.

Small biopsies for pancreatic lesions: Is there still room for fine needle aspiration?

Pancreatic carcinoma is an aggressive tumour with increasing incidence in both sexes worldwide. Early detection is, therefore, essential for patient management. A recent advancement involves the utilization of larger, thicker gauge needles, which enable the collection of core-type biopsies (FNB). Here, we investigated the role of fine needle aspiration and cytopathology in the diagnostic workflow of pancreatic lesions. A search query was designed to search for articles in the PubMed database comparing FNA and FNB for biopsy of pancreatic lesions, and detailed data were extracted from selected studies. Statistical analyses were performed using the R package meta version 6.2. Twenty-one studies made the final cut for data extraction. Overall, median age was 64.3 years (±6.1; 47.6-71.5), male: female proportion 53.9 (±11.3; 27.6-67.4), lesion size 3.1 cm (±0.5; 1.9-4.2 cm) and percentage of malignant cases 78.3% (±26.8; 2.1-100). FNA and FNB diagnostic yield was 85.8% (±10.3; 70.0-100.0) and 89.2% (±7.7; 70.0-98.6), respectively. Average accuracy was 89.5% (±11.7; 63.0-100.0) for FNA and 90.8% (±7.1; 77.0-100.0) for FNB. Adverse effects rate was 1.0% (±1.3; 0-4.3) for FNA and 2.2% (±4.4; 0-16.1) for FNB. None of the selected variables had a significant statistical difference between both methods. FNA and FNB perform similarly for diagnostic material acquisition in pancreatic lesions. The best outcome comes from the association of both techniques, emphasizing the value of combining cytological and histological morphology for the most accurate analysis.

Improving fine needle aspiration to predict the tumor biological aggressiveness in pancreatic neuroendocrine tumors using Ki-67 proliferation index, phosphorylated histone H3 (PHH3), and BCL-2

INTRODUCTION

Surgery is the only known cure for sporadic pancreatic neuroendocrine tumors (PNETs). Therefore, the prediction of the PNETs biological aggressiveness evaluated on endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) has a significant impact on clinical management. The proliferation rate of Ki-67 in PNETs can help to predict the biological aggressiveness of the tumor. In addition, there is a relatively new proliferation marker called phosphorylated histone H3 (PHH3) that can identify and quantify dividing cells in tissue samples, which is a marker highly specific to mitotic figures. Other markers such as BCL-2 also contribute to tumorigenesis and may be involved in the differentiation of neuroendocrine cells.

MATERIALS AND METHODS

A retrospective observational study was performed on patients undergoing surveillance for PNETs from January 2010 to May 2021. Data collection included the patients' age, sex, tumor location, tumor size in the surgical specimen, and tumor grade in FNA. The 2019 World Health Organization (WHO) classification guideline was followed to diagnose PNETs, including grade and stage. Immunohistochemical stainings for Ki-67, PHH3 and BCL-2 in PNETs were performed.

RESULTS

After excluding cell blocks containing fewer than 100 tumor cells, 44 patients with EUS-FNA and surgical resection specimens were included in this study. There were 19 cases of G1 PNETs, 20 cases of G2 PNETs, and 5 cases of G3 PNETs. The grade assigned based on the Ki-67 index was higher and more sensitive than that based on the mitotic count using H&E slides in some cases of G2 and G3 PNETs. However, there was no significant difference between the mitotic count using PHH3-positive tumor cells and the Ki-67 index to grade PNETs. All grade 1 tumors (19 cases) on surgical resection specimens were correctly graded on FNA (100 % concordance rate). Within the 20 G2 PNETs, 15 cases of grade 2 on surgical resection specimens were graded correctly on FNA based on the Ki-67 index only. Five cases of grade 2 PNETs on surgical resection specimens were graded as grade 1 on FNA when using only the Ki-67 index. Three of five grade 3 tumors on surgical resection specimens were graded as grade 2 on FNA based on the Ki-67 index only. Using only FNA Ki-67 to predict PNET tumor grade, the concordance (accuracy) rate was 81.8 % in total. However, all these eight cases (5 cases of G2 PNETs and 3 cases of G3 PNETs) were graded correctly by using the Ki-67 index plus mitotic rate (using PHH3 IHC stains). Four of 18 (22.2 %) patients with PNETs were positive for BCL-2 stain. In these 4 cases positive for BCL-2 stains, 3 cases were G2 PNETs and one case was G3 PNETs.

CONCLUSION

Grade and the proliferative rate in EUS-FNA can be used to predict the tumor grade in surgical resection specimens. However, when using only FNA Ki-67 to predict PNET tumor grade, about 18 % of cases were downgraded by one level. To solve the problem, immunohistochemical staining for BCL-2 and especially PHH3 would be helpful. Our results demonstrated that the mitotic count using PHH3 IHC stains not only improved the accuracy and precision of PNET grading in the surgical resection specimens, but also could reliably be used in routine scoring of mitotic figures of FNA specimens.

"SMART" cytology: The next generation cytology for precision diagnosis

Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.

Neuroendocrine Neoplasms of the Pancreas: Diagnostic Challenges and Practical Approach

Most pancreatic neuroendocrine neoplasms are slow-growing, and the patients may survive for many years, even after distant metastasis. The tumors usually display characteristic organoid growth patterns with typical neuroendocrine morphology. A smaller portion of the tumors follows a more precipitous clinical course. The classification has evolved from morphologic patterns to the current World Health Organization classification, with better-defined grading and prognostic criteria. Recent advances in molecular pathology have further improved our understanding of the pathogenesis of these tumors. Various issues and challenges remain, including the correct recognition of a neuroendocrine neoplasm, accurate classification and grading of the tumor, and differentiation from mimickers. This review focuses on the practical aspects during the workup of pancreatic neuroendocrine neoplasms and attempts to provide a general framework to help achieve an accurate diagnosis, classification, and grading.

Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring

Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation index due to lack of method standardization and inter-reader variability. The application of digital pathology coupled with machine learning has been shown to be highly accurate and reproducible for the evaluation of Ki-67 in NENs. We systematically reviewed all published studies on the subject of Ki-67 assessment in pancreatic NENs (PanNENs) employing digital image analysis (DIA). The most common advantages of DIA were improvement in the standardization and reliability of Ki-67 evaluation, as well as its speed and practicality, compared to the current gold standard approach of manual counts from captured images, which is cumbersome and time consuming. The main limitations were attributed to higher costs, lack of widespread availability (as of yet), operator qualification and training issues (if it is not done by pathologists), and most importantly, the drawback of image algorithms counting contaminating non-neoplastic cells and other signals like hemosiderin. However, solutions are rapidly developing for all of these challenging issues. A comparative meta-analysis for DIA versus manual counting shows very high concordance (global coefficient of concordance: 0.94, 95% CI: 0.83-0.98) between these two modalities. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs, provided that measures are in place to ensure counting of only tumor cells either by software modifications or education of non-pathologist operators, as well as selection of standard regions of interest for analysis. NENs, being cellular and monotonous neoplasms, are naturally more amenable to Ki-67 assessment. However, lessons of this review may be applicable to other neoplasms where proliferation activity has become an integral part of theranostic evaluation including breast, brain, and hematolymphoid neoplasms.

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.

Grading of pancreatic neuroendocrine tumors on endoscopic ultrasound-guided fine-needle aspiration using Ki-67 index and 2017 World Health Organization criteria: An analysis of 32 cases

Objectives:

Biological behavior of pancreatic neuroendocrine tumors (Pan NETs) is difficult to predict on morphology alone. The assessment of proliferation by the Ki-67 proliferation index (PI) is considered to be an important prognostic parameter in these tumors and has been endorsed by the 2017 World Health Organization (WHO) grading system for Pan NETs. Although widely accepted on surgical specimens, there is varied opinion on grading of these tumors on cytology samples. This study aimed at classification and grading of Pan NETs on endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) using the recent 2017 WHO criteria and assess the reliability of Ki-67 grading by comparing it with histology samples wherever available.

Material and Methods:

Search of cytopathology lab records over a 3-year period (June 2015–May 2018) revealed 33 cases of pancreatic NETs diagnosed on EUS-FNA specimens. Using the guidelines of 2017 WHO classification and grading of Pan NETs, retrospective grading of these Pan NETs was done. They were graded as Grades 1, 2, and 3 well differentiated Pan NETs and poorly differentiated Grade 3 neoplasms based on Ki-67 PI and cytomorphology. Cytomorphological features were compared across the three grades. The cytological grading was then compared with the histological grading where available.

Results:

Ki-67 grading on cytology was done in 32 cases (22 on cell block and 10 on smears), of which 19 (59.4%) were Grade 1, 8 (25%) were Grade 2, and 5 (15.6%) were Grade 3 tumors. The most common cytomorphological features observed in Grade 1 tumors were small round uniform cells with granular chromatin and prominent plasmacytoid morphology. As the grade increased, tumor cells showed increased pleomorphism, angulated nuclei, and less frequent plasmacytoid cells. Histopathology (biopsy/resected specimens) was available in 11 of the 32 cases. Comparison of grading on cytology and histology showed concordance in ten of the 11 cases (k value = 0.862).

Conclusion:

Our data suggest that grading of Pan NETs by assessing Ki-67 PI on cytology samples collected by EUS-FNA shows good agreement with that measured on histology samples.

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

Background

The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning.

Methods

We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining.

Results

The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.

Conclusions

Our study demonstrates the enormous potential of automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on WTS, and the automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy. We will provide those labelled images as an open-free platform for researchers to assess the performance of computer algorithms for automated Ki-67 scoring on IHC stained slides.

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.

Quantitative image analysis for CD8 score in lung small biopsies and cytology cell-blocks

INTRODUCTION

Immunotherapy has shown promising results in non-small cell lung cancer (NSCLC), for which tumour-infiltrating cytotoxic (CD8+) T cells play a critical role. We investigated the utility of image analysis (IA) to quantify CD8+ T cells in a series of matched small biopsies and resections of NSCLC.

METHODS

CD8 immunohistochemistry was performed on cell-blocks (CB), core needle biopsies (CNB) and corresponding resections from primary NSCLCs. Slides were digitised using an Aperio AT2 scanner (Leica) and annotated by whole slide image (WSI) or fields of view occupied by tissue spots (TS). Quantitative IA was performed with a customised Aperio algorithm (Leica). CD8 scores (number of T cells with 1-3+ staining/total area) were then compared.

RESULTS

Forty-four cases with CB or CNB material and a corresponding resection were analysed. Average CD8 score was determined in CB (7.67 WSI, 77.67 TS) and/or CNB (47.35 WSI, 325.67 TS), and corresponding resections (190.35 WSI, 336.58 TS). CD8 score concordance was highest (78.6%) for CNBs using WSI annotation. Overall, small biopsies (CB or CNB) correlated with the resection in 71.4% cases using WSI and 63.3% cases using TS annotation. IA performed better for low CD8 scores.

CONCLUSIONS

These findings show that CD8 density in NSCLC can be quantified by IA in small biopsies and cell blocks, achieving the best concordance using WSI scores. Discrepancies were attributed to values near the cut-off and background detection of staining. These data warrant future studies with more cases and follow-up data to further investigate the clinical utility of IA for CD8 analysis in NSCLC.

Advances in the cytologic diagnosis of gastroenteropancreatic neuroendocrine neoplasms

Two-thirds of neuroendocrine neoplasms arising in the human body originate from the gastrointestinal system or pancreas. Gastroenteropancreatic neuroendocrine neoplasms are heterogeneous, comprising both well differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). The clinical presentation, molecular characteristics, and behavior are distinct for NETs and NECs. Fine-needle aspiration is an important modality for the primary diagnosis and staging of these neoplasms and can provide information of prognostic and therapeutic significance. Our evolving understanding of neuroendocrine neoplasm biology has led to several iterations of classification. In this review, new concepts and issues most relevant to cytology diagnosis of gastroenteropancreatic neuroendocrine neoplasms are discussed, such as newer detection methods that aid in diagnosis and staging, recent changes in World Health Organization classification, practical issues related to grading these neoplasms on cytology, guidelines for diagnostic reporting, and panels of immunohistochemical stains for the diagnosis of metastasis. The current understanding of genetic and epigenetic events related to tumor development and potential applications for cytology also are presented as they relate to prognostication and recent therapeutic advances.