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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-08-16

AUTHORS

Kun-Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher Ré, Daniel L. Rubin, Michael Snyder

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<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs. More... »

PAGES

12474

References to SciGraph publications

  • 1997-11. Bayesian Network Classifiers in MACHINE LEARNING
  • 2012-08. Training increases concordance in classifying pulmonary adenocarcinomas according to the novel IASLC/ATS/ERS classification in VIRCHOWS ARCHIV
  • 2008-12. Conditional variable importance for random forests in BMC BIOINFORMATICS
  • 2012-12. Reproducibility of histopathological subtypes and invasion in pulmonary adenocarcinoma. An international interobserver study in MODERN PATHOLOGY
  • 2014-07-09. Comprehensive molecular profiling of lung adenocarcinoma in NATURE
  • 2005-10. Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization in ONCOGENE
  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2011-05. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases in MODERN PATHOLOGY
  • 2009-04. Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading in JOURNAL OF SIGNAL PROCESSING SYSTEMS
  • 1995-09. Support-Vector Networks in MACHINE LEARNING
  • 2002-08. Gene-expression profiles predict survival of patients with lung adenocarcinoma in NATURE MEDICINE
  • 2006-12. Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus in LABORATORY INVESTIGATION
  • 2006-04. CellProfiler: image analysis software for identifying and quantifying cell phenotypes in GENOME BIOLOGY
  • 2012-09. Comprehensive genomic characterization of squamous cell lung cancers in NATURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ncomms12474

    DOI

    http://dx.doi.org/10.1038/ncomms12474

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1000998210

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27527408


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