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. 2018 Oct 16:5:180202.
doi: 10.1038/sdata.2018.202.

A radiogenomic dataset of non-small cell lung cancer

Shaimaa Bakr  1 , Olivier Gevaert  2 , Sebastian Echegaray  3 , Kelsey Ayers  4 , Mu Zhou  2 , Majid Shafiq  5 , Hong Zheng  2 , Jalen Anthony Benson  4 , Weiruo Zhang  3 , Ann N C Leung  3 , Michael Kadoch  6 , Chuong D Hoang  7 , Joseph Shrager  8   9 , Andrew Quon  3 , Daniel L Rubin  3 , Sylvia K Plevritis  3 , Sandy Napel  3
Affiliations

A radiogenomic dataset of non-small cell lung cancer

Shaimaa Bakr et al. Sci Data. .

Abstract

Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.

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Conflict of interest statement

S.N. is a consultant for Carestream Inc, and a member of the scientific advisory boards for EchoPixel, Inc.; Fovia, Inc. and Radlogics, Inc. All other authors declare no competing interests.

References

Data Citations

    1. Bakr S., et al. . 2017. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.7hs46erv - DOI
    1. Napel S., Plevritis S. K. 2014. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014.X7ONY6B1 - DOI
    1. 2012. Gene Expression Omnibus. GSE28827
    1. 2018. Gene Expression Omnibus. GSE103584

References

    1. Lambin P. et al. Predicting outcomes in radiation oncology–multifactorial decision support systems. Nat Rev Clin Oncol 10, 27–40, ; DOI: 10.1038/nrclinonc.2012.196 (2013). - DOI - PMC - PubMed
    1. Segal E. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25, 675–680 (2007). - PubMed
    1. Diehn M. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci USA 105, 5213–5218 (2008). - PMC - PubMed
    1. Tixier F. et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52, 369–378, ; DOI: 10.2967/jnumed.110.082404 (2011). - DOI - PMC - PubMed
    1. El Naqa I. et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 42, 1162–1171, ; DOI: 10.1016/j.patcog.2008.08.011 (2009). - DOI - PMC - PubMed

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