A pathology foundation model for cancer diagnosis and prognosis prediction - PubMed (original) (raw)

doi: 10.1038/s41586-024-07894-z. Online ahead of print.

Junhan Zhao # 1 3, Eliana Marostica 1 4, Wei Yuan 5, Jietian Jin 6, Jiayu Zhang 5, Ruijiang Li 2, Hongping Tang 7, Kanran Wang 8, Yu Li 9, Fang Wang 10, Yulong Peng 11, Junyou Zhu 12, Jing Zhang 5, Christopher R Jackson 1 13 14, Jun Zhang 15, Deborah Dillon 16, Nancy U Lin 17, Lynette Sholl 16 18, Thomas Denize 16 18, David Meredith 16, Keith L Ligon 16 18, Sabina Signoretti 16 18, Shuji Ogino 16 19 20, Jeffrey A Golden 16 21, MacLean P Nasrallah 22, Xiao Han 15, Sen Yang 23 24, Kun-Hsing Yu 25 26 27

Affiliations

A pathology foundation model for cancer diagnosis and prognosis prediction

Xiyue Wang et al. Nature. 2024.

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.

© 2024. The Author(s), under exclusive licence to Springer Nature Limited.

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References

    1. Van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021). - PubMed - DOI
    1. Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat. Cancer 3, 1026–1038 (2022). - PubMed - DOI
    1. Song, A. H. et al. Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1, 930–949 (2023). - DOI
    1. Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019). - PubMed - PMC - DOI
    1. Bejnordi, B. E. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017). - DOI

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