This collection contains longitudinal PET/CT and quantitative MR images collected for the purpose of studying treatment assessment in breast cancer in the neoadjuvant setting.
Images were acquired at three time points: prior to the start of treatment (t1), after the first cycle of treatment (t2), and either after the second cycle of treatment or at the completion of all treatments (prior to surgery) (t3). The PET/CT images were acquired with a support device built in-house to allow the patient to be in the prone position to facilitate registration with the MRI data. The value of this collection is to provide clinical imaging data for the development and evaluation of quantitative imaging methods for treatment assessment early in the course of therapy for breast cancer. Data is provided by Vanderbilt University, PI Dr. Thomas E. Yankeelov.
PET/CT data were acquired with a GE Discovery STE scanner (GE Healthcare, Waukesha, WI, USA). A low-mAs CT scan was acquired for attenuation correction of the emission data. The acquisition parameters for the transmission CT scan were the following: the tube current was 80 mAs for a 70-kg patient and scaled accordingly for all patients, the tube voltage was 120 KVp, and the pitch was 1.675/1. The activity of FDG administered was approximately 370 MBq (10 mCi) for a 70-kg patient and scaled according to weight. FDG was administered intravenously via an antecubital vein contra-lateral to the affected breast. After 60 min, emission data was collected in 3D mode for 2 min per bed position. The emission scan was first collected in the prone position over the breast only, and then in the supine position from the skull to mid-femurs. Standard-of-care supine images and research prone images were acquired at times t1 and t3, while only the prone images were acquired at t2.
The MRI data consist of diffusion-weighted images (DWIs), dynamic contrast-enhanced (DCE) images, and multi-flip data for T1-mapping. The MRIs were obtained using a dedicated 16-channel bilateral breast coil at 3.0T (Philips Achieva with the MammoTrak table). DWIs were acquired with a single-shot spin echo (SE) echo planar imaging (EPI) sequence in three orthogonal diffusion encoding directions (x, y, and z). For 14 patients, b = 0 and 500 s/mm2, TR/TE = 2500 ms/45 ms Δ = 21.4 ms, δ = 10.3 ms and 10 signal acquisitions were acquired. For 19 patients, b = 0 and 600 s/mm2, TR/TE = “shortest” (range = 1800 - 3083 ms/43 - 60 ms) Δ = 20.7 - 29 ms, δ = 11.4 - 21 ms and 10 signal acquisitions were acquired. For four patients, b = 50 and 600 s/mm2 for two patients), TR/TE = “shortest” (range = 1840 - 3593 ms/43 - 60 ms) Δ = 20.6 - 29 ms, δ = 11.5 - 21 ms and 10 signal acquisitions were acquired. Prior to the DCE-MRI acquisition, data for constructing a T1 map were acquired with an RF-spoiled 3D gradient echo multi-flip angle approach with ten flip angles from 2 to 20 degrees in 2o increments. For both the T1 map and DCE scans, TR = 7.9 ms, TE = 4.6 ms, and the acquisition matrix was 192x192x20 (full-breast) over a sagittal square field of view (22 cm2) with slice thickness of 5 mm. For the DCE study, each 20-slice set was collected in 16 seconds at 25 time points for just under seven minutes of dynamic scanning. A catheter placed within an antecubital vein delivered 0.1 mmol/kg (9 – 15 mL, depending on patient weight) of gadopentetate dimeglumine, Gd-DTPA, (Magnevist, Wayne, NJ) at 2 mL/sec (followed by a saline flush) via a power injector (Medrad, Warrendale, PA) after the acquisition of the first three dynamic scans (baseline).
Results from a subset of data sets from this collection have been published (see below). If your use of this collection results in a publication, we would appreciate if you cite the appropriate references and acknowledge the award that supported collection and sharing of this dataset (U01 CA142565, PI Thomas E. Yankeelov).
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.
This is a limited access data set. To request access, please contact email@example.com. Once access is granted, you can view and download these images on The Cancer Imaging Archive (TCIA) by logging in and selecting the QIN-Breast collection.
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Related Peer Reviewed Manuscripts
- Li X, Dawant BM, Welch EB, Chakravarthy AB, Freehardt D, Mayer I, Kelley M, Meszoely I, Gore JC, Yankeelov TE. Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms. Medical Physics, 2010; 37:2541-52. PMCID: PMC2881925
- Atuegwa NC, Gore JC, Yankeelov TE. Using Quantitative Imaging Data to Drive Mathematical Models of Tumor Growth and Treatment Response. Physics in Medicine and Biology, 2010; 55:2429-49. PMCID: PMC2897238
- Yankeelov TE, Arlinghaus L, Li X, Gore JC. The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer. Seminars in Oncology, 2011; 38:16-25. PMCID: PMC3073543
- Arlinghaus L, Li X, Levy M, Smith D, Welch WB, Gore JC, Yankeelov TE. Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy. Journal of Oncology, 2010. pii: 919620. Epub 2010 Sep 29. PMCID: PMC2952974
- Arlinghaus LR, Welch EB, Chakravarthy AB, Farley JS, Gore JC, Yankeelov TE. Motion and distortion correction in diffusion-weighted MRI of the breast at 3T. Journal of Magnetic Resonance Imaging, 2011; 33:1063-70. PMCID: PMC3081111
- Gore JC, Manning HC, Quarles CC, Waddell KW, Yankeelov TE. Magnetic Resonance in the Era of Molecular Imaging of Cancer. Magnetic Resonance Imaging, 2011; 29:587-600. PMCID: PMC3285504
- Arlinghaus LR, Li X, Rahman AR, Welch EB, Xu L, Gore JC, Yankeelov TE. On the Relationship Between the Apparent Diffusion Coefficient and Extravascular Extracellular Volume Fraction in Human Breast Cancer. Magnetic Resonance Imaging, 2011; 29:630-8. PMCID: PMC3100356
- Smith DS, Welch EB, Li X, Arlinghaus LD, Loveless ME, Koyama T, Gore JC, Yankeelov TE. Quantitative effects of accelerated dynamic contrast enhanced MRI data using compressed sensing. Physics in Medicine and Biology, 2011; 56:4933-46. PMCID: PMC3192434
- Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 2011; 56:5753-69. PMCID: PMC3176673
- Atuegwu NC, Arlinghaus L, Li X, Welch EB, Chakravarthy AB, Gore JC, Yankeelov TE. Integration of diffusion weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy. Magnetic Resonance in Medicine 2011; 66:1689-96. PMCID: PMC3218213
- Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer. Magnetic Resonance in Medicine, 2012; 68:261-71. PMCID: PMC3291742
- Smith DS, Gambrell JV, Li X, Arlinghaus LA, Quarles CC, Yankeelov TE, Welch EB. Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Acquisitions on Derived Parameters for DCE and DSC-MRI. IEEE Transactions in Medical Imaging, 2012; 31:504-11. PMCID: PMC3289060
- Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-time Compressive Sensing MRI Reconstruction using GPU Computing and Split Bregman Methods. International Journal of Biomedical Imaging, 2012; 2012:864827. PMCID: PMC3296267
- Dula AN, Arlinghaus LR, Dortch RD, Dewey BE, Whisenant JE, Ayers GD, Yankeelov TE, Smith SE. Amide Proton Transfer Imaging of the Breast at 3 T: Establishing reproducibility and possible feasibility for assessing chemotherapy response. Magnetic Resonance in Medicine, 2013; 70: 216-24. PMCID: PMC3505231
- Yankeelov TE, Peterson TE, Abramson RG, Garcia-Izquierdo D, Arlinghaus LR, Li X, Atuegwu NC, Catana C, Manning HC, Fayad ZA, Gore JC. Simultaneous PET-MRI in Oncology: A Solution Looking for a Problem? Magnetic Resonance Imaging, 2012; 30:1342-56. Selected as a Top 25 paper in Magnetic Resonance Imaging, 2012. PMCID: PMC3466373
- Abramson RG, Arlinghaus LR, Weis JA, Li X, Dula AN, Chekmenev EY, Smith SA, Miga MI, Abramson VG, Yankeelov TE. Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy. Breast Cancer: Targets and Therapies, 2012; 4: 139-154. PMCID: PMC3496377
- Li X, Abramson RG, Arlinghaus LR, Chakravarthy AB, Abramson V, Mayer I, Farley J, Delbeke D, Yankeelov TE. An Algorithm for Longitudinal Registration of PET/CT Images Acquired During Neoadjuvant Chemotherapy in Breast Cancer: Preliminary Results. European Journal of Nuclear Medicine and Molecular Imaging Research, 2012; 16:62. PMCID: PMC3520720
- Fluckiger U, Loveless ME, Barnes SL, Lepage M, Yankeelov TE. A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations, and experimental results. Physics in Medicine and Biology, 2013; 58:1983-98. PMCID: PMC3646091
- Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomathematics, 2012; Article ID 287394. PMCID: PMC3729405
- Atuegwu NC, Arlinghaus LR, Li X, Chakravarthy AB, Abramson VG, Sanders ME, Yankeelov TE. Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity During Neoadjuvant Chemotherapy. Translational Oncology, 2013; 6:253-64. PMCID: PMC3660793
- Klomp DWJ, Dula AN, Arlinghaus LR, Italiaander M, Dortch RD, Zu Z, Williams JM, Gochberg DF, Luijten PR, Gore JC, Yankeelov TE, Smith SA. Amide Proton Transfer Imaging of the Human Breast at 7 Tesla: Development and Reproducibility. NMR in Biomedicine, 2013; 26:1271-7. PMCID: PMC3726578
- Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE. Machine Learning for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy. Journal of the American Medical Informatics Association, 2013; 20:688-95. PMCID: PMC3721158
- Li X, Arlinghaus LR, Ayers GD, Chakravarthy AB, Abramson RG, Abramson VG, Atuegwu N, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Bhave SR, Yankeelov TE. DCE-MRI Analysis Methods for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy: Pilot Study Findings. Magnetic Resonance in Medicine, 2014; 71(4):1592-602. PMCID: PMC3742614
- Yankeelov TE, Atuegwu N, Hormuth D, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V. Clinically relevant modeling of tumor growth and treatment response. Science Translational Medicine 2013; 5:187ps9. PMCID: PMC3938952
- Abramson RG, Hoyt TL, Wilson KJ, Li X, Arlinghaus LR, Su P-F, Abramson VG, Chakravarthy AB, Yankeelov TE. Early Assessment of Breast Cancer Response to Neoadjuvant Chemotherapy by Semi- Quantitative Analysis of High Temporal Resolution DCE-MRI: Preliminary Results. Magnetic Resonance Imaging, 2013 ; 31:1457-64. PMCID: PMC3807825
- Weis JA, Miga MI, Arlinghaus LA, Li X, Chakravarthy AB, Abramson VG, Farley J, Yankeelov TE. A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Physics of Medicine and Biology, 2013; 58:5851-66. PMCID: PMC3791925
- Smith DA, Yankeelov TE, Welch EB. Potential of Compressed Sensing in Quantitative MR Imaging of Cancer. Cancer Imaging, 2013; 13:633-44. PMCID: PMC3893904
- Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE. Using dynamic contrast enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. International Journal of Biomedical Imaging, 2013; 2013:576470. PMCID: PMC3814089
- Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Mulkern R, Yankeelov TE, Fennessy FM. A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step Towards Practical Implementation. Magnetic Resonance Imaging, 2014; 32:321-9. PMCID: PMC3965600
- Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational Oncology, 2014; 7:14-22. PMCID: PMC3998687
- Chenevert TL, Malyarenko DI, Newitt D, Hylton N, Huang W, Li X, Tudorica A, Fedorov A, Fennessy F, Kikinis R, Arlinghaus L, Li X, Yankeelov TE, Muzi M, Marro KI, Kinahan PE, Jajamovich GH, Dyvorne HA, Taouli B, Kalpathy-Cramer J, Oborski MJ, Laymon CM, Mountz JM, Ross BD. Error in Quantitative Image Analysis Due to Platform-Dependent Image Scaling. Translational Oncology, 2014; 7:65-71. PMCID: PMC3998685
- Huang W, Li X, Chen Y, Li X, Chang M-C, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Federov A, Tudorica A, Gupta S, Laymon CM, Marro KI, Dyvorne HA, Miller JV, 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. Translational Oncology, 2014; 7:153-66. PMCID: PMC3998693
- Atuegwu NC, Li X, Arlinghaus LR, Abramson RG, Williams JM, Chakravarthy AB, Abramson V, Yankeelov TE. Longitudinal, Inter-modality Registration of Quantitative Breast PET and MRI Data Acquired Before and During Neoadjuvant Chemotherapy: Preliminary Results. Medical Physics, 2014; 41:052302. PMCID: PMC4000383