This collection of breast dynamic contrast-enhanced (DCE) MRI data contains images from a longitudinal study to assess breast cancer response to neoadjuvant chemotherapy. Images were acquired at four time points: prior to the start of treatment (Visit 1, V1), after the first cycle of treatment (Visit 2, V2), at midpoint of treatment course (Visit 3, V3), and after completion of treatment (prior to surgery) (Visit 4, V4). The value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of breast cancer response to treatment. Data is provided by Oregon Health & Science University, PI Dr. Wei Huang.
The MRI data consist of DCE-MRI images, which were acquired using a Siemens 3T TIM Trio system with the body coil and a four-channel bilateral phased-array breast coil as the transmitter and receiver, respectively. Following pilot scans and pre-contrast T2-weighted MRI with fat-saturation and T1-weighted MRI without fat-saturation, axial bilateral DCE-MRI images with fat-saturation and full breast coverage were acquired with a 3D gradient echo-based TWIST (Time-resolved angiography WIth Stochastic Trajectories) sequence, which employs the strategy of k-space undersampling during acquisition and data sharing during reconstruction. DCE-MRI acquisition parameters included 10o flip angle, 2.9/6.2 ms TE/TR, a parallel imaging acceleration factor of two, 30-34 cm FOV, 320x320 in-plane matrix size, and 1.4 mm slice thickness. The total acquisition time was ~10 minutes for 32-34 image volume sets of 112-120 slices each with 18-20 s temporal resolution. The contrast agent Gd(HP-DO3A) [ProHance] IV injection (0.1 mmol/kg at 2 mL/s) by a programmable power injector was timed to commence after acquisition of two baseline image volumes, followed by a 20-mL saline flush.
A total of 20 data sets from this collection have been used for a multi-QIN center challenge, in which each participating site performed pharmacokinetic analysis of the breast DCE-MRI data using software tools/algorithms available to them. The shared data sets are from the V1 and V2 studies of 10 patients (BreastChemo 1, 5, 6, 8, 10, 12, 13, 14, 15, and 16) – 3 pathologic complete responders (pCRs) and 7 non-pCRs. The goal of the challenge was to evaluate variations in DCE-MRI assessment of breast cancer response to neoadjuvant chemotherapy caused by differences in software tools/algorithms only.
About the NCI QIN
The mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.
Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option.
|Data Type||Download all or Query/Filter|
|Images (DICOM + NIFTI, 15.8GB)|
|Images (Matlab, 8.4GB)|
|Pathological Response (XLS)|
Click the Versions tab for more info about data releases.
Number of Patients
Number of Studies
Number of Series
Number of Images
|Image Size (GB)||49.5|
Matlab and NIFTI Data
As part of the multi-QIN center challenge additional files were created to help facilitate participation. There are essentially two versions of the data available. These files are available inside of TCIA alongside the DICOM data.
- Matlab - contains all relevant information including the raw image data, DICOM header info, and all other relevant parameters necessary to analyze the data for the challenge. There is no need to download the raw DICOM data if you prefer this format.
- DICOM + NIFTI - useful if you'd prefer to work in 3D Slicer or some other application which supports DICOM and NIFTI formats
Pathologic response status for the patients:
- Complete response
- Non-complete response
For inquiries on AIF (Arterial Input Function) used for pharmacokinetic analysis of the breast DCE-MRI data, users are encouraged to contact email@example.com who can direct you to Dr. Huang.
Citations & Data Usage Policy
This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to firstname.lastname@example.org.
Please be sure to include the following citations in your work and acknowledge the award that supported collection and sharing of these data sets (U01 CA154602, PI Wei Huang) if you use this data set:
Huang, Wei, Tudorica, Alina, Chui, Stephen, Kemmer, Kathleen, Naik, Arpana, Troxell, Megan, … Holtorf, Megan. (2014). Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014.A2N1IXOX
Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Fedorov A, Tudorica A, Gupta SN, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Barbodiak DP, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpathy-Cramer J. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Trans Oncol 2014;7:153-166. PubMed PMID: 24772219; PubMed Central PMCID: PMC3998693. (link)
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. (paper)
Other Publications Using This Data
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