Harvard Medical School - Dana-Farber Cancer Institute

Data

The CIBL shares the following datasets:

NSCLC-Radiomics & NSCLC-Radiomics-Genomics

This collection contains images from non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications [1].

In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was 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.

We share the following datasets :

  • Lung1 Dataset: This dataset contains data from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans and clinical outcome data are available.

    For more information go to the TCIA website: TCIA NSCLC-Radiomic
    Direct download links:
    – Imaging data can be found here: The Cancer Imaging Archive (TCIA)
    – Clinical data can be found here:  Lung1.clinical.csv

  • Lung 3 Dataset: This collection contains images from 89 non-small cell lung cancer (NSCLC) patients that were treated with surgery. For these patients pretreatment CT scans, gene expression, and clinical data are available.

    For more information go to the TCIA website: TCIA NSCLC-Radiomics-Genomics
    Direct download links:
    – Imaging data can be found here: The Cancer Imaging Archive (TCIA)
    – Clinical data can be found here:  Lung3.metadata.xls
    – Gene-expression data can be found here: GEO

heatmap_radiomics

[1]  Aerts, H. J. W. L. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014 (Nature Website)

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