In clinical oncology and drug development there is genuine need for sensitive and reproducible quantitative imaging methods for early prediction of therapy response and accurate assessment of post-therapy residual cancer. Such methods have the potential to spare non-responding patients from ineffective and/or toxic treatments, improve clinical management, and accelerate efficacy evaluation of novel therapies. Dynamic- Contrast-Enhanced (DOE) MRI can provide an excellent measure of therapy-induced tumor vascular changes. The Standard-Model (SM) for pharmacokinetic DCE-MRl analysis incorrectly assumes effectively infinitely fast equilibrium inter-compartmental water exchange kinetics and, as a result, often underestimates the pharmacokinetic parameters Ktrans and Ve. The Shutter-Speed Model (SSM) accounts for finite water exchange kinetics effects and corrects the imaging biomarker underestimations. It has proven more sensitive to vascular changes than the SM. In addition, SSM DCE-MRl allows quantification of novel imaging biomarkers, such as ?Ktrans(= Ktrans (SSM) - Ktrans (SM)] - a measure of precisely the water exchange (shutter-speed) effect on Ktrans estimation. ?Ktransis not only a more sensitive biomarker for therapeutic response, but also less prone to other systematic errors often observed in DCE-MRl quantification.
In Specific Aim 1, the Shutter-Speed Model will be applied to phase l/ll clinical trials in two disease areas (breast cancer and soft tissue sarcoma), SSM DCE-MRl will be compared/combined with SM DCE-MRl, diffusion-weighted MRI and tumor size measurement for assessment of therapy response.
In Aim 2, the effects of data acquisition and processing schemes on DCE-MRl biomarker values will be investigated within the context of therapeutic monitoring.
In Aims 1 and 2, pathology results will be used as endpoints for correlation with imaging results and statistical analyses.
In Aim 3, caBIG-compliant software tools will be developed that utilize a single and/or a set of imaging biomarkers to aid clinical research.
|Thibault, Guillaume; Tudorica, Alina; Afzal, Aneela et al. (2017) DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. Tomography 3:23-32|
|Ger, Rachel B; Mohamed, Abdallah S R; Awan, Musaddiq J et al. (2017) A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations. Sci Rep 7:11185|
|Huang, Wei; Beckett, Brooke R; Tudorica, Alina et al. (2016) Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Tomography 2:308-316|
|Tudorica, Alina; Oh, Karen Y; Chui, Stephen Y-C et al. (2016) Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI. Transl Oncol 9:8-17|
|Malyarenko, Dariya I; Newitt, David; J Wilmes, Lisa et al. (2016) Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials. Magn Reson Med 75:1312-23|
|Yankeelov, Thomas E; Mankoff, David A; Schwartz, Lawrence H et al. (2016) Quantitative Imaging in Cancer Clinical Trials. Clin Cancer Res 22:284-90|
|Li, Xin; Cai, Yu; Moloney, Brendan et al. (2016) Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling. J Magn Reson 269:104-112|
|Malyarenko, Dariya I; Wilmes, Lisa J; Arlinghaus, Lori R et al. (2016) QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials. Tomography 2:396-405|
|Huang, Wei; Chen, Yiyi; Fedorov, Andriy et al. (2016) The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography 2:56-66|
|Kalpathy-Cramer, Jayashree; de Herrera, Alba García Seco; Demner-Fushman, Dina et al. (2015) Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013. Comput Med Imaging Graph 39:55-61|
Showing the most recent 10 out of 24 publications