This dataset enhances the ISPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. This dataset includes a) uniformly processed scans that are harmonized to match the intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions.
The segmentations for the ISPY1/ACRIN 6657 dataset currently hosted on TCIA’s website describe a) the tumor volume of interest (VOI) and b) functional tumor volume (FTV).
- The provided tumor VOI is a 3D rectangular box enclosing the enhancing tumor region, while including peritumoral tissue. The VOI provides a general guideline of where the tumor is located within patient anatomy, but it does not delineate tumor boundaries or shape.
- The FTV segmentations describe only enhancing voxels in the tumor, i.e., defined by peak enhancement or signal enhancement ratio criteria.
These currently provided segmentations do not include non-enhancing portions of the tumor volume, which represent a significant portion of the disease burden that needs to be studied to better understand and quantify the disease.
The segmentations in these new analysis results are for the entire 3D primary lesion, including both the enhancing and the non-enhancing tumor regions, therefore defining the structural tumor volume (STV). These STV annotations were generated by manually delineating the primary lesion volume, after confirming the location of the primary lesion from the provided VOI and FTV. The STV annotations were reviewed and approved by a board-certified, fellowship-trained breast radiologist, and are statistically significantly different from FTV.
We believe these STV annotations will allow analyses of the entire disease burden and analyses of tumor heterogeneity regarding contrast uptake, contributing to further expanding our mechanistic understanding of the disease potentially leading to improved patient management.
Research reported in this publication was partly supported by the National Cancer Institute (NCI) of the National Institutes of Health (NIH), under award numbers U01CA242871 and U24CA189523, U01CA151235, R01CA197000, and R01CA132870.
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Images and Segmentations (NIfTI, 6.1 GB)
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Radiomics Features (xlsx)
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CaPTk radiomic feature parameter (CSV, 5KB)
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Collections Used in this Third Party Analysis
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Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Chitalia, R., Pati, S., Bhalerao, M., Thakur, S., Jahani, N., Belenky, J. V., McDonald, E.S., Gibbs, J., Newitt, D., Hylton, N., Kontos, D., & Bakas, S. (2021). Expert tumor annotations and radiomic features for the ISPY1/ACRIN 6657 trial data collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.XC7A-QT20
R.Chitalia, S.Pati, M.Bhalerao, S.P.Thakur, N.Jahani, V.Belenky, E.S.McDonald, J.Gibbs, D.C.Newitt, N.M.Hylton, D.Kontos, S.Bakas, “Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1”, Nature Scientific Data, [In Press], 2022
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
Other Publications Using This Data
Version (Current): 2022/06/01
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|Images and Segmentations (NIfTI, 6.1 GB)|
|Radiomics Features (xlsx)|
|README File (txt)|