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

Doğukan, Fatih Mert; Özgüven, Banu Yılmaz; Doğukan, Rabia; Kabukçuoğlu, Fevziye

doi:10.1007/s12022-018-9554-3

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…negative tumor cells on a digital image was shown to have near-perfect agreement with manual counting on a printout image, the technique itself is still labor-intensive [8]. Current North American Neuroendocrine Tumor Society (NANETS) consensus guidelines recommend manual counting of camera-captured digital images over eyeballing when calculating Ki-67 PI for pancreatic NETs [5].…”

Section: Discussionmentioning

confidence: 99%

“…Currently, consensus regarding the best method to assess the Ki‐67 PI in NENs is lacking. Eyeball estimation, manual counting, and digital image analysis are the three main methods for scoring [ 8 , 9 , 10 , 11 , 12 , 13 ]. Eyeballing is not encouraged due to its low reproducibility and high inter‐reader variability [ 11 , 12 , 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…Despite inter‐ and intra‐observer variability, both methods are still preferred over digital image analysis in many clinical pathology departments due to their minimal disruption of the current workflow not pertaining digital pathology. While manual counting of Ki‐67‐positive and Ki‐67‐negative tumor cells on a digital image was shown to have near‐perfect agreement with manual counting on a printout image, the technique itself is still labor‐intensive [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

“…After this, the percentage of tumor cells expressing Ki‐67 is determined by counting at least 500 tumor cells or 0.4 mm 2 of the tumor area in the highest labeling regions (hotspots) [ 3 , 7 ]. Several scoring methods are available including eyeball estimation, manual counting of cells (through microscope eyepiece, using a printed image or from a monitor), and computer‐assisted quantification using digital image analysis [ 8 , 9 , 10 , 11 , 12 , 13 ]. However, to our knowledge, no consensus exists on the best method.…”

Section: Introductionmentioning

confidence: 99%

“…Eyeball estimation has shown to suffer from considerable interobserver variation and is thus discouraged [ 11 , 12 , 13 ]. More reproducible manual counting method is accurate but has typically a long application time and is labor‐intensive [ 8 , 11 , 12 ]. To eliminate manual counting, automated counting methods for Ki‐67 PI utilizing digital images have been developed for clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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

Vesterinen

Säilä

Blom

et al. 2021

APMIS

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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 Ò 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Vesterinen

Säilä

Blom

et al. 2021

APMIS

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

Satturwar

Pantanowitz

Manko

et al. 2020

Cancer Cytopathology

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BackgroundThe 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.MethodsKi‐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).ResultsThe 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.ConclusionsThe 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.

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From Counting Mitoses to Ki67 Assessment: Technical Pitfalls in the New WHO Classification of Endocrine and Neuroendocrine Tumors

Cree

2022

Endocr Pathol

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

Cited by 7 publications

References 11 publications

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

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

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

From Counting Mitoses to Ki67 Assessment: Technical Pitfalls in the New WHO Classification of Endocrine and Neuroendocrine Tumors

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