Child pages
  • Cervical Cancer – Tumor Heterogeneity: Serial Functional and Molecular Imaging Across the Radiation Therapy Course in Advanced Cervical Cancer (CC-Tumor-Heterogeneity)

Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Localtab Group


Localtab
activetrue
titleData Access

Data Access

Data TypeDownload all or Query/Filter

Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB)

<< latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >>


Tcia button generator



Tcia button generator
labelSearch



(Download requires the NBIA Data Retriever)

Tissue Slide Images (SVS, XX.X GB)


Tcia button generator



Tcia button generator
labelSearch



Clinical data (CSV)


Tcia button generator



Genomics (web)


Tcia button generator
labelSearch



Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.


Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities


Number of Patients


Number of Studies


Number of Series


Number of Images


Images Size (GB)

Purpose:  The understanding of biological processes occurring within the tumor environment during the ongoing radiation therapy course remains a major knowledge gap in radiation oncology for cervical cancer and other malignancies.  The CCTH collection seeks to help fill this gap by providing functional/molecular tumor imaging data sets – spatially to assess tumor heterogeneity and temporally across the radiation therapy course in advanced cervical cancer patients.

CCTH Collection:  The CCTH collection shares functional/molecular imaging data sets (performed on an NCI funded R01 award1) that were prospectively acquired in clinical patients with advanced stage IB2 – IVA cervical cancer, who were treated with standard combined radiation therapy with concurrent Cisplatin-based chemotherapy.  Both functional MRI, consisting of T1- and T2-weighted, dynamic contrast enhanced (DCE), diffusion-weighted (DWI) and post-contrast MRI, and 18FDG PET/CT were obtained in parallel and prospectively timed with the radiation therapy course.  Imaging was performed, according to a standardized multi-institutional protocol, at three time points/radiation dose levels: before treatment start (dose 0), early during the treatment course (2-2.5 weeks after treatment start/dose 20-25 Gy) and at mid-treatment (4-5 weeks after treatment start/dose 45-50 Gy).  Contours (regions of interest) of the tumor volumes, as defined by the gold standard of T2-weighetd MRI and coregistration with PET/CT, are included in the CCTH for each case and each imaging time point. 

The resulting prospective multi-variable imaging data sets, obtained from the various imaging modalities at different treatment time points, allow detailed study of the evolution of functional/molecular tumor properties before and during radiation/chemotherapy course.  These include voxel-wise heterogeneity assessment of the structural properties (tumor volume on T2-weighted MRI) and, in parallel, the functional characteristics on DCE, DWI MRI and 18FDG PET. 

These intra-treatment functional phenomena from various functional imaging modalities may have the potential for clinical translation into important actionable early imaging prognosticators and predictors for long-term treatment outcome.  For example, vascular tumor properties (as reflected by DCE MRI) are highly significant for tumor oxygenation, which in turn profoundly influences radiation response. For example, our early investigations suggest that dynamic contrast enhancement (indicative of tumor microvasculature) improves early in the treatment course and heterogeneity decreases, particularly in responders [refs], while FDG heterogeneity tends to show reduced metabolic activity, which commonly becomes evident later in the radiation treatment course1.  Our pilot data from a prior NCI/NIH award that laid the foundation for this work 2-10, have also suggested that functional tumor properties early during treatment (2-4 weeks into treatment) have significant predictive value for ultimate tumor control and survival in cervical cancer 2- 4, 6.  If unfavorable tumor properties, which correlate with poor treatment outcome, can be identified in patients early during treatment 4, 6, the therapy regimen may be individually adapted accordingly to improve the outcome. 

Translation to clinical practice: The complex data sets, from multi-modality and multi-parametric imaging studies at various treatment time points, were designed to be shared with the scientific community with interest and related specialties to advance the functional imaging-based tumor heterogeneity assessment in cervical cancer patients.  The current data set allows further extraction of radiomics data that may help optimize potential and efficacy of functional imaging in improving the management of the patients with advanced cervical cancer.  

The ultimate goal is to facilitate the clinical translation that is readily available to community settings in the U.S. and abroad.  Therefore, the imaging sequences used in this research are widely available and applicable in community settings.  This is important because advanced cervical cancer is largely treated in the community settings and is highly prevalent cancer worldwide, particularly among women in underserved and economically disadvantaged regions. 

Future outlook:  We thank the National Cancer Institute and National Institute of Health for their support of this imaging research; and the members of CIP and TCIA for their deep expertise and their help in establishing the CCTH collection.  While our initial studies  have established important principles on the use of functional and molecular imaging in cervical cancer, it has been our intent and hope when donating this data, that the data sets of this collection may serve as a resource to investigators into the future as image analysis and radiomics techniques will continue to advance, well beyond the time course of our own studies. Because cervical cancer remains a common cancer in the women, particularly in low- and middle-income countries worldwide, future studies will need to address the feasibility and accessibility of imaging to these patients, such as affordable cost, availability of imaging modalities involved, technical processing requirements (radiomic feature extraction, data reduction and statistical analysis/learning models) and education to investigators and clinicians.  

  1. Mayr NA, Wang JZ, Zhang D, et al. Longitudinal changes in tumor perfusion pattern during the radiation therapy course and its clinical impact in cervical cancer. Int J Radiat Oncol Biol Phys 2010;77(2):502-8. doi: 10.1016/j.ijrobp.2009.04.084 [published Online First: 20090921]
  2. Mayr NA, Wang JZ, Zhang D, et al. Synergistic effects of hemoglobin and tumor perfusion on tumor control and survival in cervical cancer. Int J Radiat Oncol Biol Phys 2009;74(5):1513-21. doi: 10.1016/j.ijrobp.2008.09.050 [published Online First: 20090313]
  3. Mayr NA, Yuh WT, Jajoura D, et al. Ultra-early predictive assay for treatment failure using functional magnetic resonance imaging and clinical prognostic parameters in cervical cancer. Cancer 2010;116(4):903-12. doi: 10.1002/cncr.24822
  4. Prescott JW, Zhang D, Wang JZ, et al. Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation. J Digit Imaging 2010;23(3):342-57. doi: 10.1007/s10278-009-9179-7 [published Online First: 20090127]
  5. Yuh WT, Mayr NA, Jarjoura D, et al. Predicting control of primary tumor and survival by DCE MRI during early therapy in cervical cancer. Invest Radiol 2009;44(6):343-50. doi: 10.1097/RLI.0b013e3181a64ce9
  6. Huang Z, Mayr NA, Yuh WT, et al. Predicting outcomes in cervical cancer: a kinetic model of tumor regression during radiation therapy. Cancer Res 2010;70(2):463-70. doi: 10.1158/0008-5472.Can-09-2501 [published Online First: 20100112]
  7. Huang Z, Mayr NA, Gao M, et al. Onset time of tumor repopulation for cervical cancer: first evidence from clinical data. Int J Radiat Oncol Biol Phys 2012;84(2):478-84. doi: 10.1016/j.ijrobp.2011.12.037 [published Online First: 20120302]
  8. Mayr NA, Wang JZ, Lo SS, et al. Translating response during therapy into ultimate treatment outcome: a personalized 4-dimensional MRI tumor volumetric regression approach in cervical cancer. Int J Radiat Oncol Biol Phys 2010;76(3):719-27. doi: 10.1016/j.ijrobp.2009.02.036 [published Online First: 20090723]
  9. Wang JZ, Mayr NA, Zhang D, et al. Sequential magnetic resonance imaging of cervical cancer: the predictive value of absolute tumor volume and regression ratio measured before, during, and after radiation therapy. Cancer 2010;116(21):5093-101. doi: 10.1002/cncr.25260



Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia license 4 international


Info
titleData Citation

DOI goes here. Create using Datacite with information from Collection Approval form


Info
titlePublication Citation

Bowen SR, Yuh WTC, Hippe DS, Wu W, Partridge SC, Elias S, Jia G, Huang Z, Sandison GA, Nelson D, Knopp MV, Lo SS, Kinahan PE, Mayr NA. Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. J Magn Reson Imaging. 2018 May;47(5):1388-1396. doi: 10.1002/jmri.25874


Info
titleTCIA Citation

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

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


Localtab
titleVersions

Version X (Current): Updated yyyy/mm/dd

Data TypeDownload all or Query/Filter
Images (DICOM, xx.x GB)


Tcia button generator



Tcia button generator
labelSearch



(Requires NBIA Data Retriever.)

Clinical Data (CSV)Link
Other (format)


Tcia button generator
labelSearch



<< One or two sentences about what you changed since last version.  No note required for version 1. >> 


...