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Data Access

Click the Versions tab for more info about data releases.

• Imaging Data Commons (IDC) (Imaging Data)

Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

• SDTM datasets of clinical data and measurements for selected cancer collections to TCIA (DI-Cubed-Reports)
• DICOM SR of clinical data and measurement for breast cancer collections to TCIA (DICOM-SR-Breast-Clinical)

Detailed Description

QIN BMMR Challenge

Subsets of this collection are being used as the training data set for the NCI Quantitative Imaging Network (QIN) Breast MRI Metrics of Response (BMMR) challenge. To download these subsets use the following links:

Included image data

Original and time-of-scan derived images.

Original DCE images are contained in a single series for each study. For the majority of studies (174 of 198) this series contains only the 3 time-points described above. 12 studies have 1 additional post-contrast acquisition for a total of 4 phases, while 2 studies, UCSF_BR_36, visit 1, and UCSF_BR_42, visit 1, have 4 and 6 post-injection phases respectively.

NOTE: Study UCSF_BR_18, Visit 1, has two pre-contrast acquisitions and two post-contrast phases. Use of the 2nd pre-contrast phase for time-point t₀ is recommended. All other studies have a single pre-contrast phase.

Other original image series present in the collection include 3-plane scout images for each study, and for some studies T2 weighted images and diffusion weighted images. In addition, some studies may contain derived images from the DCE series including subtraction images and projection images.

Tumor volumetric analysis images.

The primary predictor variable for the original study, functional tumor volume (FTV), was measured from contrast-enhanced images using the signal enhancement ratio (SER) method. Four derived series per study from this analysis are included in this collection:

Further information on later NACT Breast Cancer MRI studies can be found at:

• ACRIN 6657 Protocol http://www.acrin.org/6657_protocol.aspx
• I-SPY TRIAL http://ncicb.nci.nih.gov/tools/translation_research/ispy (link is deprecated per May 2023)
• CALGB 150007 http://www.cancer.gov/clinicaltrials/search/view?cdrid=69280&version=HealthProfessional  (link is deprecated per May 2023)

Metadata

Excel workbook with clinical data and disease free survival (DFS) data for the 64 patients in this study:

Clinical and DFS Data (xls)

DICOM data dictionary defining fields included in the 0x0117 private group for PE/SER calculation parameters and results, and deduced DCE scan timing information. NOTE: Dictionary contains sections for attributes from the I-SPY 1 TRIAL which are not included in this pilot study.:

ISPY-1 DCE MRI Data Sharing Dictionary_v2

Citations & Data Usage Policy 

Additional Publication Resources

The Collection authors suggest the below will give context to this dataset:

1. Partridge SC, Gibbs JE, Lu Y, Esserman LJ, Tripathy D, Wolverton DS, Rugo HS, Hwang ES, Ewing CA, Hylton NM. MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival. AJR Am J Roentgenol. 2005 Jun;184(6):1774-81. PubMed PMID: 15908529. doi: 10.2214/ajr.184.6.01841774
2. Li KL, Partridge SC, Joe BN, Gibbs JE, Lu Y, Esserman LJ, Hylton NM. Invasive breast cancer: predicting disease recurrence by using high-spatial-resolution signal enhancement ratio imaging. Radiology. 2008 Jul;248(1):79-87. PMID: 18566170. doi: 10.1148/radiol.2481070846
3. Hattangadi J, Park C, Rembert J, Klifa C, Hwang J, Gibbs J, Hylton N. Breast stromal enhancement on MRI is associated with response to neoadjuvant chemotherapy. AJR Am J Roentgenol. 2008 Jun;190(6):1630-6. PubMed PMID: 18492917. doi: 10.2214/AJR.07.2533
4. Li KL, Henry RG, Wilmes LJ, Gibbs J, Zhu X, Lu Y, Hylton NM. Kinetic assessment of breast tumors using high spatial resolution signal enhancement ratio (SER) imaging. Magn Reson Med. 2007 Sep;58(3):572-81. PubMed PMID: 17685424; PubMed Central PMCID: PMC4508009. doi: 10.1002/mrm.21361
5. Jones EF, Sinha SP, Newitt DC, Klifa C, Kornak J, Park CC, Hylton NM. MRI enhancement in stromal tissue surrounding breast tumors: association with recurrence free survival following neoadjuvant chemotherapy. PLoS One. 2013 May 7;8(5):e61969. Print 2013. PMCID: PMC3646993.  doi: 10.1371/journal.pone.0061969 

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact TCIA's Helpdesk.

• Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022). Harnessing multimodal data integration to advance precision oncology. Nature Reviews Cancer, 22(2), 114-126. doi:10.1038/s41568-021-00408-3
• Chui, K. T., Arya, V., Band, S. S., Alhalabi, M., Liu, R. W., & Chi, H. R. (2023). Facilitating innovation and knowledge transfer between homogeneous and heterogeneous datasets: Generic incremental transfer learning approach and multidisciplinary studies. Journal of Innovation & Knowledge, 8(2). doi:https://doi.org/10.1016/j.jik.2023.100313
• Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada.
• Kosareva, A. A., Paulenka, D. A., Snezhko, E. V., Bratchenko, I. A., & Kovalev, V. A. (2022). Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis. Journal of Biomedical Photonics & Engineering, 8(3). doi:https://doi.org/10.18287/JBPE22.08.030307
• Lo, W.-C., Li, W., Jones, E. F., Newitt, D. C., Kornak, J., Wilmes, L. J., . . . Hylton, N. M. (2016). Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes. PLoS One, 11(2), e0142047. doi:10.1371/journal.pone.0142047
• Natsheh, Q., Li, B., & Gale, A. (2016). Security of Multi-frame DICOM Images Using XOR Encryption Approach. Procedia Computer Science, 90, 175-181. doi:10.1016/j.procs.2016.07.018
• Nave, O. (2020). Adding features from the mathematical model of breast cancer to predict the tumour size. International Journal of Computer Mathematics: Computer Systems Theory, 5(3), 159-174. doi:https://doi.org/10.1080/23799927.2020.1792552
• Tianxu Lv, Xiang Pan, & Li, L. (2020). DCE-MRI based Breast Intratumor Heterogeneity Analysis via Dual Attention Deep Clustering Network and its Application in Molecular Typing. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South).
• Wu, Y., Wang, Y., Sun, H., Jiang, C., Li, B., Li, L., & Pan, X. (2022). Joint model- and immunohistochemistry-driven few-shot learning scheme for breast cancer segmentation on 4D DCE-MRI. Applied Intelligence. doi:https://doi.org/10.1007/s10489-022-04272-y

Version 2 (Current): Updated 2016/04/16

 deleted 4 duplicate DICOM series:

1.3.6.1.4.1.14519.5.2.1.7695.2311.274969196687715738621052447111

1.3.6.1.4.1.14519.5.2.1.7695.2311.790802609328661944330528970451

1.3.6.1.4.1.14519.5.2.1.7695.2311.869262000688001356814712906748

1.3.6.1.4.1.14519.5.2.1.7695.2311.914480028549654883806586724682

Version 1: Updated 2016/01/30