Comparison of different anti-Ki67 antibody clones and hot-spot sizes for assessing proliferative index and grading in pancreatic neuroendocrine tumours using manual and image analysis

Exploration of Digital Image Analysis for Ki67 Quantification in the Grading of Medullary Thyroid Carcinoma: A Pilot Study with 85 Cases

BACKGROUND

Although uncommon, medullary thyroid carcinoma (MTC) accounts for a significant proportion of thyroid cancer deaths. Recent studies have validated the two-tier International Medullary Thyroid Carcinoma Grading System (IMTCGS) to predict clinical outcomes. A 5% Ki67 proliferative index (Ki67PI) cut-off separates low-grade from high-grade MTC. In this study, we compared digital image analysis (DIA) to manual counting (MC) for determining the Ki67PI in a MTC cohort, and explored the challenges encountered.

METHODS

Available slides from 85 MTCs were reviewed by two pathologists. The Ki67PI was documented by immunohistochemistry for each case, scanned with the Aperio® slide scanner at 40× magnification, and quantified using the QuPath® DIA platform. The same hotspots were screenshot, printed in color, and blindly counted. For each case, over 500 MTC cells were counted. Each MTC was graded using IMTCGS criteria.

RESULTS

In our MTC cohort (n = 85), 84.7 and 15.3% were low- and high-grade with the IMTCGS. In the entire cohort, QuPath® DIA performed well (R2 = 0.9891) but appeared to undercall compared to MC. QuPath® performed better in high-grade cases (R2 = 0.99) compared to low-grade cases (R2 = 0.7071). Overall, Ki67PI determined with either MC or DIA did not affect IMTCGS grade. Encountered DIA challenges include optimizing cell detection, overlapping nuclei, and tissue artifacts. Encountered MC challenges include background staining, morphologic overlap with normal elements, and counting time.

CONCLUSION

Our study highlights the utility of DIA in quantifying Ki67PI for MTC and can serve as an adjunct for grading in conjunction with the other criteria of mitotic activity and necrosis.

QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R² = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

Risk Stratification of Pancreatic Neuroendocrine Neoplasms Based on Clinical, Pathological, and Molecular Characteristics

Pancreatic neuroendocrine neoplasms consist of heterogeneous diseases. Depending on the novel features detected by various modern technologies, their classification and related prognosis predictions continue to change and develop. The role of traditional clinicopathological prognostic factors, including classification systems, is also being refined, and several attempts have been made to predict a more accurate prognosis through novel serum biomarkers, genetic factors, and epigenetic factors that have been identified through various state-of-the-art molecular techniques with multiomics sequencing. In this review article, the latest research results including the traditional approach to prognostic factors and recent advanced strategies for risk stratification of pancreatic neuroendocrine neoplasms based on clinical, pathological, and molecular characteristics are summarized. Predicting prognosis through multi-factorial assessments seems to be more efficacious, and prognostic factors through noninvasive methods are expected to develop further advances in liquid biopsy in the future.

Pancreatic neuroendocrine neoplasms: a 2022 update for radiologists

Pancreatic neuroendocrine neoplasms (PaNENs) are a unique group of pancreatic neoplasms with a wide range of clinical presentations and behaviors. Given their heterogeneous appearance and increasing detection on cross-sectional imaging, it is essential that radiologists understand the variable presentation and distinctions PaNENs display compared to other pancreatic neoplasms. Additionally, some of these neoplasms may be hormonally functional, and it is imperative that radiologists be aware of the common clinical presentations of hormonally active PaNENs. Knowledge of PaNEN pathology and treatments may influence which imaging modality is optimal for each patient. Each imaging modality used for PaNENs has distinct advantages and disadvantages, particularly in different treatment settings. Thus, the focus of this manuscript is to provide an update for the radiologist on PaNEN pathology, imaging, and treatments.

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.

An update on the development of concepts, diagnostic criteria, and challenging issues for neuroendocrine neoplasms across different digestive organs

Digestive neuroendocrine neoplasms (NENs) are a group of heterogeneous neoplasms found throughout the digestive tract, with different behaviour and genetic background. In the last few years, nomenclature and WHO/UICC classifications of digestive NENs have changed, and molecular classifications have emerged, especially in pancreatic locations. Increasing patho-molecular details are needed to diagnose the different categories of NEN, including the use of helpful immunohistochemical markers. In this review, we address these topics in three successive chapters. We first briefly review recent updates in classifications, discuss important grading and proliferating issues and advances in the molecular understanding of NEN. Then, we provide an update on diagnosis, including the most important differential diagnoses of NEN, with a focus on high-grade neoplasms and mixed tumours. Finally, we highlight a variety of currently used and next-generation predictive and prognostic biomarkers as well as biomarkers of tumour origin and describe some site specificities of gastrointestinal NEN. We specifically focus on biomarkers available to pathologists with the potential to change the way patients with NEN are diagnosed and treated.

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 as a Prognostic Biomarker in Invasive Breast Cancer

Simple Summary

In breast cancer development, the expression of Ki-67 is strongly associated with cancer proliferation and is a known indicator of prognosis and outcome. Ki-67 expression levels are also useful to inform treatment decision making in some cases. As a result, routine measurement of Ki-67 is now widely performed during pathological tumour evaluation. However, the Ki-67 appraisal is not without its limitations and shortcomings—the aim of this study was to provide an overview of Ki-67 use in the clinical setting, the current challenges associated with its measurement, and the novel strategies that will hopefully enhance Ki-67 proliferation indices for prospective breast cancer patients.

Abstract

The advent of molecular medicine has transformed breast cancer management. Breast cancer is now recognised as a heterogenous disease with varied morphology, molecular features, tumour behaviour, and response to therapeutic strategies. These parameters are underpinned by a combination of genomic and immunohistochemical tumour factors, with estrogen receptor (ER) status, progesterone receptor (PgR) status, human epidermal growth factor receptor-2 (HER2) status, Ki-67 proliferation indices, and multigene panels all playing a contributive role in the substratification, prognostication and personalization of treatment modalities for each case. The expression of Ki-67 is strongly linked to tumour cell proliferation and growth and is routinely evaluated as a proliferation marker. This review will discuss the clinical utility, current pitfalls, and promising strategies to augment Ki-67 proliferation indices in future breast oncology.

Digital Image Analysis of the Proliferation Markers Ki67 and Phosphohistone H3 in Gastroenteropancreatic Neuroendocrine Neoplasms: Accuracy of Grading Compared With Routine Manual Hot Spot Evaluation of the Ki67 Index

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are rare epithelial neoplasms. Grading is based on mitotic activity or the percentage of Ki67-positive cells in a hot spot. Routine methods have poor intraobserver and interobserver consistency, and objective measurements are lacking. This study aimed to evaluate digital image analysis (DIA) as an objective assessment of proliferation markers in GEP-NENs. A consecutive cohort of patients with automated DIA measurement of Ki67 (DIA Ki67) and phosphohistone H3 (DIA PHH3) on immunohistochemical slides was analyzed using Visiopharm image analysis software (Hoersholm, Denmark). The results were compared with the Ki67 index from routine pathology reports (pathology Ki67). The study included 159 patients (57% males). The median pathology Ki67 was 2.0% and DIA Ki67 was 4.1%. The interclass correlation coefficient of the DIA Ki67 compared with the pathology Ki67 showed an excellent agreement of 0.96 [95% confidence interval (CI): 0.94-0.96]. The observed kappa value was 0.86 (95% CI: 0.81-0.91) when comparing grades based on the same methods. PHH3 was measured in 145 (91.2%) cases. The observed kappa value was 0.74. (95% CI: 0.65-0.83) when comparing grade based on the DIA PHH3 and the pathology Ki67. The DIA Ki67 shows excellent agreement with the pathology Ki67. The DIA PHH3 measurements were more varied and cannot replace other methods for grading GEP-NENs.

Prognostic features of the tumour microenvironment in oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is a disease with an incredibly poor survival rate and a complex makeup. The growth and spread of OAC tumours are profoundly influenced by their surrounding microenvironment and the properties of the tumour itself. Constant crosstalk between the tumour and its microenvironment is key to the survival of the tumour and ultimately the death of the patient. The tumour microenvironment (TME) is composed of a complex milieu of cell types including cancer associated fibroblasts (CAFs) which make up the tumour stroma, endothelial cells which line blood and lymphatic vessels and infiltrating immune cell populations. These various cell types and the tumour constantly communicate through environmental cues including fluctuations in pH, hypoxia and the release of mitogens such as cytokines, chemokines and growth factors, many of which help promote malignant progression. Eventually clusters of tumour cells such as tumour buds break away and spread through the lymphatic system to nearby lymph nodes or enter the circulation forming secondary metastasis. Collectively, these factors need to be considered when assessing and treating patients clinically. This review aims to summarise the ways in which these various factors are currently assessed and how they relate to patient treatment and outcome at an individual level.

QuPath: The global impact of an open source digital pathology system

QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen's University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature: from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.