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Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients

Author

Listed:
  • Pei-Chen Tsai

    (Harvard Medical School
    National Cheng Kung University)

  • Tsung-Hua Lee

    (National Cheng Kung University)

  • Kun-Chi Kuo

    (National Cheng Kung University)

  • Fang-Yi Su

    (National Cheng Kung University)

  • Tsung-Lu Michael Lee

    (Southern Taiwan University of Science and Technology)

  • Eliana Marostica

    (Harvard Medical School
    Harvard-Massachusetts Institute of Technology)

  • Tomotaka Ugai

    (Harvard T.H. Chan School of Public Health
    Brigham and Women’s Hospital)

  • Melissa Zhao

    (Brigham and Women’s Hospital)

  • Mai Chan Lau

    (Brigham and Women’s Hospital)

  • Juha P. Väyrynen

    (Oulu University Hospital and University of Oulu)

  • Marios Giannakis

    (Dana Farber Cancer Institute)

  • Yasutoshi Takashima

    (Brigham and Women’s Hospital)

  • Seyed Mousavi Kahaki

    (Brigham and Women’s Hospital)

  • Kana Wu

    (Harvard T.H. Chan School of Public Health)

  • Mingyang Song

    (Harvard T.H. Chan School of Public Health)

  • Jeffrey A. Meyerhardt

    (Dana Farber Cancer Institute)

  • Andrew T. Chan

    (Massachusetts General Hospital
    Brigham and Women’s Hospital)

  • Jung-Hsien Chiang

    (National Cheng Kung University)

  • Jonathan Nowak

    (Brigham and Women’s Hospital)

  • Shuji Ogino

    (Harvard T.H. Chan School of Public Health
    Brigham and Women’s Hospital
    Broad Institute of MIT and Harvard)

  • Kun-Hsing Yu

    (Harvard Medical School
    Brigham and Women’s Hospital)

Abstract

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients’ histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value

Suggested Citation

  • Pei-Chen Tsai & Tsung-Hua Lee & Kun-Chi Kuo & Fang-Yi Su & Tsung-Lu Michael Lee & Eliana Marostica & Tomotaka Ugai & Melissa Zhao & Mai Chan Lau & Juha P. Väyrynen & Marios Giannakis & Yasutoshi Takas, 2023. "Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37179-4
    DOI: 10.1038/s41467-023-37179-4
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    References listed on IDEAS

    as
    1. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Kun-Hsing Yu & Ce Zhang & Gerald J. Berry & Russ B. Altman & Christopher Ré & Daniel L. Rubin & Michael Snyder, 2016. "Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
    3. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
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