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Article: The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification

TitleThe 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification
Authors
Keywordsdigital pathology
international collaboration
mitotic figure mimics
mitotic figures
social media
Issue Date16-Apr-2024
PublisherSAGE Publications
Citation
International Journal of Surgical Pathology, 2024 How to Cite?
AbstractIntroduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.
Persistent Identifierhttp://hdl.handle.net/10722/346321
ISSN
2023 Impact Factor: 0.9
2023 SCImago Journal Rankings: 0.350

 

DC FieldValueLanguage
dc.contributor.authorLin, Sherman-
dc.contributor.authorTran, Christopher-
dc.contributor.authorBandari, Ela-
dc.contributor.authorRomagnoli, Tommaso-
dc.contributor.authorLi, Yueyang-
dc.contributor.authorChu, Michael-
dc.contributor.authorAmirthakatesan, Abinaya S.-
dc.contributor.authorDallmann, Adam-
dc.contributor.authorKostiukov, Andrii-
dc.contributor.authorPanizo, Angel-
dc.contributor.authorHodgson, Anjelica-
dc.contributor.authorLaury, Anna R.-
dc.contributor.authorPolonia, Antonio-
dc.contributor.authorStueck, Ashley E.-
dc.contributor.authorMenon, Aswathy A.-
dc.contributor.authorMorini, Aurélien-
dc.contributor.authorÖzamrak, Birsen-
dc.contributor.authorCooper, Caroline-
dc.contributor.authorTrinidad, Celestine Marie G.-
dc.contributor.authorEisenlöffel, Christian-
dc.contributor.authorSuleiman, Dauda E.-
dc.contributor.authorSuster, David-
dc.contributor.authorDorward, David A.-
dc.contributor.authorAljufairi, Eman A.-
dc.contributor.authorMaclean, Fiona-
dc.contributor.authorGul, Gulen-
dc.contributor.authorSansano, Irene-
dc.contributor.authorErana-Rojas, Irma E.-
dc.contributor.authorMachado, Isidro-
dc.contributor.authorKholova, Ivana-
dc.contributor.authorKarunanithi, Jayanthi-
dc.contributor.authorGibier, Jean Baptiste-
dc.contributor.authorSchulte, Jefree J.-
dc.contributor.authorLi, Joshua J.X.-
dc.contributor.authorKini, Jyoti R.-
dc.contributor.authorCollins, Katrina-
dc.contributor.authorGalea, Laurence A.-
dc.contributor.authorMuller, Louis-
dc.contributor.authorCima, Luca-
dc.contributor.authorNova-Camacho, Luiz M.-
dc.contributor.authorDabner, Marcus-
dc.contributor.authorMuscara, Matthew J.-
dc.contributor.authorHanna, Matthew G.-
dc.contributor.authorAgoumi, Mehdi-
dc.contributor.authorWiebe, Nicholas J.P.-
dc.contributor.authorOswald, Nicola K.-
dc.contributor.authorZahra, Nusrat-
dc.contributor.authorFolaranmi, Olaleke O.-
dc.contributor.authorKravtsov, Oleksandr-
dc.contributor.authorSemerci, Orhan-
dc.contributor.authorPatil, Namrata N.-
dc.contributor.authorMuthusamy Sundar, Preethi-
dc.contributor.authorCharles, Prem-
dc.contributor.authorKumaraswamy Rajeswaran, Priyadarshini-
dc.contributor.authorZhang, Qi-
dc.contributor.authorvan der Griend, Rachael-
dc.contributor.authorPillappa, Raghavendra-
dc.contributor.authorPerret, Raul-
dc.contributor.authorGonzalez, Raul S.-
dc.contributor.authorReed, Robyn C.-
dc.contributor.authorPatil, Sachin-
dc.contributor.authorJiang, Xiaoyin “Sara”-
dc.contributor.authorQayoom, Sumaira-
dc.contributor.authorPrendeville, Susan-
dc.contributor.authorBaskota, Swikrity U.-
dc.contributor.authorTran, Thanh Truc-
dc.contributor.authorSan, Thar Htet-
dc.contributor.authorKukkonen, Tiia Maria-
dc.contributor.authorKendall, Timothy J.-
dc.contributor.authorTaskin, Toros-
dc.contributor.authorRutland, Tristan-
dc.contributor.authorManucha, Varsha-
dc.contributor.authorCockenpot, Vincent-
dc.contributor.authorRosen, Yale-
dc.contributor.authorRodriguez-Velandia, Yessica P.-
dc.contributor.authorOrdulu, Zehra-
dc.contributor.authorCecchini, Matthew J.-
dc.date.accessioned2024-09-14T00:30:33Z-
dc.date.available2024-09-14T00:30:33Z-
dc.date.issued2024-04-16-
dc.identifier.citationInternational Journal of Surgical Pathology, 2024-
dc.identifier.issn1066-8969-
dc.identifier.urihttp://hdl.handle.net/10722/346321-
dc.description.abstractIntroduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.-
dc.languageeng-
dc.publisherSAGE Publications-
dc.relation.ispartofInternational Journal of Surgical Pathology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectdigital pathology-
dc.subjectinternational collaboration-
dc.subjectmitotic figure mimics-
dc.subjectmitotic figures-
dc.subjectsocial media-
dc.titleThe 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification-
dc.typeArticle-
dc.identifier.doi10.1177/10668969241234321-
dc.identifier.scopuseid_2-s2.0-85190647356-
dc.identifier.eissn1940-2465-
dc.identifier.issnl1066-8969-

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