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

Sherman Lin, Christopher Tran, Ela Bandari, Tommaso Romagnoli, Yueyang Li, Michael Chu, Abinaya S. Amirthakatesan, Adam Dallmann, Andrii Kostiukov, Angel Panizo, Anjelica Hodgson, Anna R. Laury, Antonio Polonia, Ashley E. Stueck, Aswathy A. Menon, Aurélien Morini, Birsen Özamrak, Caroline Cooper, Celestine Marie G. Trinidad, Christian EisenlöffelDauda E. Suleiman, David Suster, David A. Dorward, Eman A. Aljufairi, Fiona Maclean, Gulen Gul, Irene Sansano, Irma E. Erana-Rojas, Isidro Machado, Ivana Kholova, Jayanthi Karunanithi, Jean Baptiste Gibier, Jefree J. Schulte, Joshua J.X. Li, Jyoti R. Kini, Katrina Collins, Laurence A. Galea, Louis Muller, Luca Cima, Luiz M. Nova-Camacho, Marcus Dabner, Matthew J. Muscara, Matthew G. Hanna, Mehdi Agoumi, Nicholas J.P. Wiebe, Nicola K. Oswald, Nusrat Zahra, Olaleke O. Folaranmi, Oleksandr Kravtsov, Orhan Semerci, Namrata N. Patil, Preethi Muthusamy Sundar, Prem Charles, Priyadarshini Kumaraswamy Rajeswaran, Qi Zhang, Rachael van der Griend, Raghavendra Pillappa, Raul Perret, Raul S. Gonzalez, Robyn C. Reed, Sachin Patil, Xiaoyin “Sara” Jiang, Sumaira Qayoom, Susan Prendeville, Swikrity U. Baskota, Thanh Truc Tran, Thar Htet San, Tiia Maria Kukkonen, Timothy J. Kendall, Toros Taskin, Tristan Rutland, Varsha Manucha, Vincent Cockenpot, Yale Rosen, Yessica P. Rodriguez-Velandia, Zehra Ordulu, Matthew J. Cecchini

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction. 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.

Original languageEnglish
JournalInternational Journal of Surgical Pathology
DOIs
StateAccepted/In press - 2024
Externally publishedYes

Keywords

  • digital pathology
  • international collaboration
  • mitotic figure mimics
  • mitotic figures
  • social media

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