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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Author

Listed:
  • Kun-Hsing Yu

    (Biomedical Informatics Program, Stanford University
    Stanford University)

  • Ce Zhang

    (Stanford University)

  • Gerald J. Berry

    (Stanford University)

  • Russ B. Altman

    (Biomedical Informatics Program, Stanford University)

  • Christopher Ré

    (Stanford University)

  • Daniel L. Rubin

    (Biomedical Informatics Program, Stanford University)

  • Michael Snyder

    (Stanford University)

Abstract

Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients’ prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12474
    DOI: 10.1038/ncomms12474
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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