Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms Article Swipe
Saad Nadeem
,
Matthew G. Hanna
,
Kartik Viswanathan
,
Joseph P. Marino
,
Mahsa Ahadi
,
Bayan Alzumaili
,
Mohamed Amine Bani
,
Federico Chiarucci
,
Angela Chou
,
Antonio De Leo
,
Talia L. Fuchs
,
Daniel Lubin
,
Catherine Luxford
,
Kelly R. Magliocca
,
Germán Iván Martínez
,
Qiuying Shi
,
Stan B. Sidhu
,
Abir Al Ghuzlan
,
Anthony J. Gill
,
Giovanni Tallini
,
Ronald Ghossein
,
Bin Xu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/his.15048
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/his.15048
Aims The International Medullary Thyroid Carcinoma Grading System, introduced in 2022, mandates evaluation of the Ki67 proliferation index to assign a histological grade for medullary thyroid carcinoma. However, manual counting remains a tedious and time‐consuming task. Methods and results We aimed to evaluate the performance of three other counting techniques for the Ki67 index, eyeballing by a trained experienced investigator, a machine learning‐based deep learning algorithm (DeepLIIF) and an image analysis software with internal thresholding compared to the gold standard manual counting in a large cohort of 260 primarily resected medullary thyroid carcinoma. The Ki67 proliferation index generated by all three methods correlate near‐perfectly with the manual Ki67 index, with kappa values ranging from 0.884 to 0.979 and interclass correlation coefficients ranging from 0.969 to 0.983. Discrepant Ki67 results were only observed in cases with borderline manual Ki67 readings, ranging from 3 to 7%. Medullary thyroid carcinomas with a high Ki67 index (≥ 5%) determined using any of the four methods were associated with significantly decreased disease‐specific survival and distant metastasis‐free survival. Conclusions We herein validate a machine learning‐based deep‐learning platform and an image analysis software with internal thresholding to generate accurate automatic Ki67 proliferation indices in medullary thyroid carcinoma. Manual Ki67 count remains useful when facing a tumour with a borderline Ki67 proliferation index of 3–7%. In daily practice, validation of alternative evaluation methods for the Ki67 index in MTC is required prior to implementation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/his.15048
- OA Status
- green
- Cited By
- 5
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386726176
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386726176Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/his.15048Digital Object Identifier
- Title
-
Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platformsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-14Full publication date if available
- Authors
-
Saad Nadeem, Matthew G. Hanna, Kartik Viswanathan, Joseph P. Marino, Mahsa Ahadi, Bayan Alzumaili, Mohamed Amine Bani, Federico Chiarucci, Angela Chou, Antonio De Leo, Talia L. Fuchs, Daniel Lubin, Catherine Luxford, Kelly R. Magliocca, Germán Iván Martínez, Qiuying Shi, Stan B. Sidhu, Abir Al Ghuzlan, Anthony J. Gill, Giovanni Tallini, Ronald Ghossein, Bin XuList of authors in order
- Landing page
-
https://doi.org/10.1111/his.15048Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10840805/pdf/nihms-1930343.pdfDirect OA link when available
- Concepts
-
Thresholding, Medullary cavity, Thyroid carcinoma, Medicine, Proliferation index, Grading (engineering), Thyroid, Pathology, Artificial intelligence, Internal medicine, Computer science, Immunohistochemistry, Biology, Image (mathematics), EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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