IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-43172-8.html
   My bibliography  Save this article

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Author

Listed:
  • Shidan Wang

    (University of Texas Southwestern Medical Center)

  • Ruichen Rong

    (University of Texas Southwestern Medical Center)

  • Qin Zhou

    (University of Texas Southwestern Medical Center)

  • Donghan M. Yang

    (University of Texas Southwestern Medical Center)

  • Xinyi Zhang

    (University of Texas Southwestern Medical Center)

  • Xiaowei Zhan

    (University of Texas Southwestern Medical Center)

  • Justin Bishop

    (University of Texas Southwestern Medical Center)

  • Zhikai Chi

    (University of Texas Southwestern Medical Center)

  • Clare J. Wilhelm

    (Memorial Sloan Kettering Cancer Center)

  • Siyuan Zhang

    (University of Texas Southwestern Medical Center)

  • Curtis R. Pickering

    (Yale School of Medicine)

  • Mark G. Kris

    (Memorial Sloan Kettering Cancer Center)

  • John Minna

    (UT Southwestern Medical Center
    University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Yang Xie

    (University of Texas Southwestern Medical Center
    UT Southwestern Medical Center
    UT Southwestern Medical Center)

  • Guanghua Xiao

    (University of Texas Southwestern Medical Center
    UT Southwestern Medical Center
    UT Southwestern Medical Center)

Abstract

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.

Suggested Citation

  • Shidan Wang & Ruichen Rong & Qin Zhou & Donghan M. Yang & Xinyi Zhang & Xiaowei Zhan & Justin Bishop & Zhikai Chi & Clare J. Wilhelm & Siyuan Zhang & Curtis R. Pickering & Mark G. Kris & John Minna & , 2023. "Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43172-8
    DOI: 10.1038/s41467-023-43172-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-43172-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-43172-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lida Qiu & Deyong Kang & Chuan Wang & Wenhui Guo & Fangmeng Fu & Qingxiang Wu & Gangqin Xi & Jiajia He & Liqin Zheng & Qingyuan Zhang & Xiaoxia Liao & Lianhuang Li & Jianxin Chen & Haohua Tu, 2022. "Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. 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.
    3. Adalberto Claudio Quiros & Nicolas Coudray & Anna Yeaton & Xinyu Yang & Bojing Liu & Hortense Le & Luis Chiriboga & Afreen Karimkhan & Navneet Narula & David A. Moore & Christopher Y. Park & Harvey Pa, 2024. "Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    4. Gil Shamai & Amir Livne & António Polónia & Edmond Sabo & Alexandra Cretu & Gil Bar-Sela & Ron Kimmel, 2022. "Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Igor Dolgalev & Hua Zhou & Nina Murrell & Hortense Le & Theodore Sakellaropoulos & Nicolas Coudray & Kelsey Zhu & Varshini Vasudevaraja & Anna Yeaton & Chandra Goparaju & Yonghua Li & Imran Sulaiman &, 2023. "Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Byungsoo Ahn & Damin Moon & Hyun-Soo Kim & Chung Lee & Nam Hoon Cho & Heung-Kook Choi & Dongmin Kim & Jung-Yun Lee & Eun Ji Nam & Dongju Won & Hee Jung An & Sun Young Kwon & Su-Jin Shin & Hye Ra Jung , 2024. "Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    7. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Stephen F Weng & Luis Vaz & Nadeem Qureshi & Joe Kai, 2019. "Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43172-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.