A pathology foundation model for cancer diagnosis and prognosis prediction

Xiyue Wang, Junhan Zhao, Eliana Marostica, Wei Yuan, Jietian Jin, Jiayu Zhang, Ruijiang Li, Hongping Tang, Kanran Wang, Yu Li, Fang Wang, Yulong Peng, Junyou Zhu, Jing Zhang, Christopher R. Jackson, Jun Zhang, Deborah Dillon, Nancy U. Lin, Lynette Sholl, Thomas Denize, David Meredith, Keith L. Ligon, Sabina Signoretti, Shuji Ogino, Jeffrey A. Golden, MacLean P. Nasrallah, Xiao Han, Sen Yang () and Kun-Hsing Yu ()
Additional contact information
Xiyue Wang: Harvard Medical School
Junhan Zhao: Harvard Medical School
Eliana Marostica: Harvard Medical School
Wei Yuan: Sichuan University
Jietian Jin: Sun Yat-sen University Cancer Center
Jiayu Zhang: Sichuan University
Ruijiang Li: Stanford University School of Medicine
Hongping Tang: Shenzhen Maternity & Child Healthcare Hospital
Kanran Wang: Chongqing University Cancer Hospital
Yu Li: Chongqing University Cancer Hospital
Fang Wang: The Affiliated Yantai Yuhuangding Hospital of Qingdao University
Yulong Peng: The First Affiliated Hospital of Jinan University
Junyou Zhu: Sun Yat-sen University
Jing Zhang: Sichuan University
Christopher R. Jackson: Harvard Medical School
Jun Zhang: Tencent AI Lab
Deborah Dillon: Brigham and Women’s Hospital
Nancy U. Lin: Dana-Farber Cancer Institute
Lynette Sholl: Brigham and Women’s Hospital
Thomas Denize: Brigham and Women’s Hospital
David Meredith: Brigham and Women’s Hospital
Keith L. Ligon: Brigham and Women’s Hospital
Sabina Signoretti: Brigham and Women’s Hospital
Shuji Ogino: Brigham and Women’s Hospital
Jeffrey A. Golden: Brigham and Women’s Hospital
MacLean P. Nasrallah: Perelman School of Medicine at the University of Pennsylvania
Xiao Han: Tencent AI Lab
Sen Yang: Harvard Medical School
Kun-Hsing Yu: Harvard Medical School

Nature, 2024, vol. 634, issue 8035, 970-978

Abstract: Abstract Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.

Date: 2024
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DOI: 10.1038/s41586-024-07894-z

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