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Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer
BACKGROUND: Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers. METHODS: A total of 300 invasive breast cancer specimens were randomly div...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802471/ https://www.ncbi.nlm.nih.gov/pubmed/35094693 http://dx.doi.org/10.1186/s13000-022-01196-6 |
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author | Li, Lina Han, Dandan Yu, Yongqiang Li, Jinze Liu, Yueping |
author_facet | Li, Lina Han, Dandan Yu, Yongqiang Li, Jinze Liu, Yueping |
author_sort | Li, Lina |
collection | PubMed |
description | BACKGROUND: Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers. METHODS: A total of 300 invasive breast cancer specimens were randomly divided into training and validation sets, with each set including 150 cases. Breast cancer Ki-67 standard reference card ranging from 5 to 90% were created. The training set was interpreted by nine pathologists of different ages through microscopic visual assessment (VA), SRC, microscopic manual counting (MC), and AI. The validation set was interpreted by three randomly selected pathologists using SRC and AI. The intra-group correlation coefficient (ICC) were used for consistency analysis. RESULTS: In the homogeneous and heterogeneous groups of validation sets, the consistency among the pathologists that used SRC and AI was very good, with an ICC of>0.905. In the validation set, using SRC and AI, three pathologists obtained results that were very consistent with the gold standard, having an ICC above 0.95, and the inter-observer agreement was also very good, with an ICC of>0.9. CONCLUSIONS: AI has satisfactory inter-observer repeatability, and the true value was closer to the gold standard, which is the preferred method for Ki-67LI reproducibility; While AI software has not been popularized, SRC may be interpreted as breast cancer Ki-67LI’s standard candidate method. |
format | Online Article Text |
id | pubmed-8802471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88024712022-02-02 Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer Li, Lina Han, Dandan Yu, Yongqiang Li, Jinze Liu, Yueping Diagn Pathol Research BACKGROUND: Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers. METHODS: A total of 300 invasive breast cancer specimens were randomly divided into training and validation sets, with each set including 150 cases. Breast cancer Ki-67 standard reference card ranging from 5 to 90% were created. The training set was interpreted by nine pathologists of different ages through microscopic visual assessment (VA), SRC, microscopic manual counting (MC), and AI. The validation set was interpreted by three randomly selected pathologists using SRC and AI. The intra-group correlation coefficient (ICC) were used for consistency analysis. RESULTS: In the homogeneous and heterogeneous groups of validation sets, the consistency among the pathologists that used SRC and AI was very good, with an ICC of>0.905. In the validation set, using SRC and AI, three pathologists obtained results that were very consistent with the gold standard, having an ICC above 0.95, and the inter-observer agreement was also very good, with an ICC of>0.9. CONCLUSIONS: AI has satisfactory inter-observer repeatability, and the true value was closer to the gold standard, which is the preferred method for Ki-67LI reproducibility; While AI software has not been popularized, SRC may be interpreted as breast cancer Ki-67LI’s standard candidate method. BioMed Central 2022-01-30 /pmc/articles/PMC8802471/ /pubmed/35094693 http://dx.doi.org/10.1186/s13000-022-01196-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, Lina Han, Dandan Yu, Yongqiang Li, Jinze Liu, Yueping Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer |
title | Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer |
title_full | Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer |
title_fullStr | Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer |
title_full_unstemmed | Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer |
title_short | Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer |
title_sort | artificial intelligence-assisted interpretation of ki-67 expression and repeatability in breast cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802471/ https://www.ncbi.nlm.nih.gov/pubmed/35094693 http://dx.doi.org/10.1186/s13000-022-01196-6 |
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