PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology

Kevin Faust, Min Li Chen, Parsa Babaei Zadeh, Dimitrios G. Oreopoulos, Alberto J. Leon, Ameesha Paliwal, Evelyn Rose Kamski-Hennekam, Marly Mikhail, Xianpi Duan, Xianzhao Duan, Mugeng Liu, Narges Ahangari, Raul Cotau, Vincent Francis Castillo, Nikfar Nikzad, Richard J. Sugden, Patrick Murphy, Safiyh S. Aljohani, Philippe Echelard, Susan J. Done, Kiran Jakate, Zaid Saeed Kamil, Yazeed Alwelaie, Mohammed J. Alyousef, Noor Said Alsafwani, Assem Saleh Alrumeh, Rola M. Saleeb, Maxime Richer, Lidiane Vieira Marins, George M. Yousef and Phedias Diamandis ()
Additional contact information
Kevin Faust: Princess Margaret Cancer Centre
Min Li Chen: Princess Margaret Cancer Centre
Parsa Babaei Zadeh: Princess Margaret Cancer Centre
Dimitrios G. Oreopoulos: Princess Margaret Cancer Centre
Alberto J. Leon: Princess Margaret Cancer Centre
Ameesha Paliwal: Princess Margaret Cancer Centre
Evelyn Rose Kamski-Hennekam: Princess Margaret Cancer Centre
Marly Mikhail: Princess Margaret Cancer Centre
Xianpi Duan: McMaster University
Xianzhao Duan: McMaster University
Mugeng Liu: Princess Margaret Cancer Centre
Narges Ahangari: University of Toronto
Raul Cotau: biochimie et pathologie de l’Université Laval
Vincent Francis Castillo: University of Toronto
Nikfar Nikzad: McMaster University
Richard J. Sugden: Princess Margaret Cancer Centre
Patrick Murphy: University of Toronto
Safiyh S. Aljohani: Taibah University
Philippe Echelard: Université de Sherbrooke
Susan J. Done: Princess Margaret Cancer Centre
Kiran Jakate: University of Toronto
Zaid Saeed Kamil: University of Toronto
Yazeed Alwelaie: King Fahad Medical City
Mohammed J. Alyousef: Imam Abdulrahman Bin Faisal University
Noor Said Alsafwani: Imam Abdulrahman Bin Faisal University
Assem Saleh Alrumeh: 200 Elizabeth Street
Rola M. Saleeb: University of Toronto
Maxime Richer: biochimie et pathologie de l’Université Laval
Lidiane Vieira Marins: Instituto D’Or de Pesquisa e Ensino (IDOR)
George M. Yousef: University of Toronto
Phedias Diamandis: Princess Margaret Cancer Centre

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications.

Date: 2025
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DOI: 10.1038/s41467-024-55780-z

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