Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. Nat Commun, 5:4006, 2014.

Paper doi abstract bibtex

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

@article{461,
   author = {Aerts, H. J. and Velazquez, E. R. and Leijenaar, R. T. and Parmar, C. and Grossmann, P. and Carvalho, S. and Bussink, J. and Monshouwer, R. and Haibe-Kains, B. and Rietveld, D. and Hoebers, F. and Rietbergen, M. M. and Leemans, C. R. and Dekker, A. and Quackenbush, J. and Gillies, R. J. and Lambin, P.},
   title = {Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach},
   journal = {Nat Commun},
   volume = {5},
   pages = {4006},
   abstract = {Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.},
   keywords = {Adenocarcinoma/*diagnosis
Carcinoma, Non-Small-Cell Lung/*diagnosis
Carcinoma, Squamous Cell/*diagnosis
Female
Head and Neck Neoplasms/*diagnosis
Humans
Lung Neoplasms/*diagnosis
Male
Multimodal Imaging
Phenotype
Positron-Emission Tomography
Prognosis
Tomography, X-Ray Computed
Tumor Burden},
   ISSN = {2041-1723 (Electronic)
2041-1723 (Linking)},
   DOI = {10.1038/ncomms5006},
   url = {http://www.ncbi.nlm.nih.gov/pubmed/24892406},
   year = {2014},
   type = {Journal Article}
}

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