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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 parametric 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 [¹⁸F]FDG 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 PET heterogeneity tends to show reduced metabolic activity, which commonly becomes evident later in the radiation treatment course¹.  Our prior data and results 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. 

Additional Publication Resources:

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

1. Bowen SR, Yuh WTC, Hippe DS, et al. Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. J Magn Reson Imaging 2018;47(5):1388-96. doi: 10.1002/jmri.25874 [published Online First: 20171016]

2. 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]

3. 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]
4. 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
5. 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]
6. 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
7. 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]
8. 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]
9. 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]
10. 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

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