Radiotherapy is the principal treatment modality for advanced cervical cancer, but local control is frequently not achieved. The failure rate may be reduced by treating high-risk patients with more intense therapies including higher doses of radiation, chemotherapy, and/or surgery. However, there is no well-established predictor to identify patients whose high risk for failure justified the increased morbidity of more aggressive therapy. The investigators seek to identify those at high risk early, such that more aggressive treatment can be rendered that may improve outcome. Quantitative tumor volume and enhancement pattern analysis based on sequential MRI examination were shown to provide very early signals of failure in plot studies. Tumor size and dynamic enhancement pattern judged by the MRI prior to radiation therapy and temporal changes during the early course of radiation therapy appear to be sensitive predictors of tumor response; consistent with the notion that tumor blood supply and or oxygenation status strongly influence radiation response. The overall goal of this project is to test the hypothesis that MR-based measurements predict the likelihood of tumor control in patients with advanced cervical cancer treated by conventional radiation therapy. This will be achieved by three specific aims: (1) further develop, test, and refine predictive metrics of advanced cervical cancer radio-responsiveness based on contrast enhanced MRI and MR-based tumor volumetry, (2) apply MRI in a clinical population through their course of therapy and correlate tumor response with image-based metrics, and (3) determine predictive value (positive and negative) of MRI-based metrics. On completion, this project will provide a clinically validated MR protocol for prediction of tumor radio-responsiveness in advanced cervical carcinoma treated with radio-therapy. A prognostic index using MRI in a clinical setting to identify the high-risk patients who require more aggressive multi-modality therapy will be developed. The pixel signal distribution within the entire tumor between the radiosensitive and resistant groups will be further defined using multi-spectral and multi- temporal analysis, and characterized to discern subgroups contributing to treatment failure within the heterogeneous tumor.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA071906-03
Application #
6173403
Study Section
Diagnostic Radiology Study Section (RNM)
Program Officer
Croft, Barbara
Project Start
1998-09-21
Project End
2001-06-30
Budget Start
2000-08-01
Budget End
2001-06-30
Support Year
3
Fiscal Year
2000
Total Cost
$143,334
Indirect Cost
Name
University of Iowa
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
041294109
City
Iowa City
State
IA
Country
United States
Zip Code
52242
Huang, Zhibin; Yuh, Kevin A; Lo, Simon S et al. (2014) Validation of optimal DCE-MRI perfusion threshold to classify at-risk tumor imaging voxels in heterogeneous cervical cancer for outcome prediction. Magn Reson Imaging 32:1198-205
Xu-Welliver, Meng; Yuh, William T C; Fielding, Julia R et al. (2014) Imaging across the life span: innovations in imaging and therapy for gynecologic cancer. Radiographics 34:1062-81
Huang, Zhibin; Mayr, Nina A; Lo, Simon S et al. (2012) Characterizing at-Risk Voxels by Using Perfusion Magnetic Resonance Imaging for Cervical Cancer during Radiotherapy. J Cancer Sci Ther 4:254-259
Huang, Zhibin; Mayr, Nina A; Gao, Mingcheng et al. (2012) Onset time of tumor repopulation for cervical cancer: first evidence from clinical data. Int J Radiat Oncol Biol Phys 84:478-84
Mayr, Nina A; Huang, Zhibin; Wang, Jian Z et al. (2012) Characterizing tumor heterogeneity with functional imaging and quantifying high-risk tumor volume for early prediction of treatment outcome: cervical cancer as a model. Int J Radiat Oncol Biol Phys 83:972-9
Mayr, Nina A; Wang, Jian Z; Zhang, Dongqing et al. (2010) Longitudinal changes in tumor perfusion pattern during the radiation therapy course and its clinical impact in cervical cancer. Int J Radiat Oncol Biol Phys 77:502-8
Mayr, Nina A; Wang, Jian Z; Lo, Simon S et al. (2010) 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 76:719-27
Mayr, Nina A; Yuh, William T C; Jajoura, David et al. (2010) Ultra-early predictive assay for treatment failure using functional magnetic resonance imaging and clinical prognostic parameters in cervical cancer. Cancer 116:903-12
Wang, Jian Z; Mayr, Nina A; Zhang, Dongqing et al. (2010) 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 116:5093-101
Huang, Zhibin; Mayr, Nina A; Yuh, William T C et al. (2010) Predicting outcomes in cervical cancer: a kinetic model of tumor regression during radiation therapy. Cancer Res 70:463-70

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