Defining the biological basis of radiomic phenotypes in lung cancer

Patrick Grossmann, Olya Stringfield, Nehme El-Hachem, Marilyn M. Bui, Emmanuel Rios Velazquez, Chintan Parmar, Ralph T. H. Leijenaar, Benjamin Haibe-Kains, Philippe Lambin, Robert J. Gilles, Hugo J. W. L. Aerts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

198 Citations (Web of Science)

Abstract

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p

Original languageEnglish
Article number23421
Number of pages22
JournalElife
Volume6
DOIs
Publication statusPublished - 21 Jul 2017

Keywords

  • GENE-EXPRESSION DATA
  • INTRATUMOR HETEROGENEITY
  • TUMOR PHENOTYPE
  • TEXTURE ANALYSIS
  • PROBE LEVEL
  • STAGE-I
  • FEATURES
  • SURVIVAL
  • CT
  • GLIOBLASTOMA

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