TY - JOUR
T1 - Defining the biological basis of radiomic phenotypes in lung cancer
AU - Grossmann, Patrick
AU - Stringfield, Olya
AU - El-Hachem, Nehme
AU - Bui, Marilyn M.
AU - Velazquez, Emmanuel Rios
AU - Parmar, Chintan
AU - Leijenaar, Ralph T. H.
AU - Haibe-Kains, Benjamin
AU - Lambin, Philippe
AU - Gilles, Robert J.
AU - Aerts, Hugo J. W. L.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - 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
AB - 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
KW - GENE-EXPRESSION DATA
KW - INTRATUMOR HETEROGENEITY
KW - TUMOR PHENOTYPE
KW - TEXTURE ANALYSIS
KW - PROBE LEVEL
KW - STAGE-I
KW - FEATURES
KW - SURVIVAL
KW - CT
KW - GLIOBLASTOMA
U2 - 10.7554/eLife.23421
DO - 10.7554/eLife.23421
M3 - Article
C2 - 28731408
SN - 2050-084X
VL - 6
JO - Elife
JF - Elife
M1 - 23421
ER -