Image-based analyses as a means of modifying Radiation Therapy to customize treatment based on an individual patient's prognosis or early response to treatment is a research area crucial to the success of this program project. Project 3 investigates the hypothesis that, given the underlying heterogeneity of tumors and normal organs and their response to treatment, anatomic, metabolic and physiologic images (as acquired in the clinical projects of this program) are best utilized for individualizing therapy by robustly identifying subvolumes that are more predictive than individual voxels or the entire tumor/normal organ as a w/hole, and providing these subvolumes as a spatial guide for radiation dose redistributions such as focal boosting.
Aim 1 will improve reconstruction of physiological images.
Aim 2 is to investigate methods of Identifying subvolumes predictive of response.
Aim 3 will improve the accuracy of mapping subvolumes to patients to guide modification of radiation therapy dose distributions. Pattern recognition techniques will be developed for subvolume identification, and correlated with outcomes (e.g. sites of local failure). Complementary and redundant information from different imaging methods (e.g. 11C Methionine PET and DCE MRI in gliomas) for local response prediction will be studied, as well as the reproducibility of imaging signals and extracted subvolumes (from test-retest data). Methods to improve physiological imaging and analysis, including direct analysis of pharmacokinetics via pattern recognition, and sparsely sampled parallel MR! reconstructed using compressed sensing to resolve organ movement and improve the temporal resolution and/or volumetric coverage, will be investigated. To guide therapy individualization, subvolumes need to be mapped geometrically to the treated patient, with uncertainties due to the limits of image registration accuracy. Finite element models will be investigated as atlases to regularize the intensity-based deformable alignment methods used to place these subvolumes in the space of treatment planning CT scans acquired for the purposes of planning and/or modifying treatment.
While MRI, PET, and SPECT scans can help predict how an individual patient may respond differently to treatment than others with the same disease, methods to identify poorly responding parts of tumors or normal organs from these images are not yet developed. This project investigates ways to find the regions of a tumor that need further treatment or parts of normal organs that should be spared.
|Johansson, Adam; Balter, James M; Cao, Yue (2018) Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Med Phys 45:4529-4540|
|Tseng, Huan-Hsin; Luo, Yi; Ten Haken, Randall K et al. (2018) The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy. Front Oncol 8:266|
|Jochems, Arthur; El-Naqa, Issam; Kessler, Marc et al. (2018) A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol 57:226-230|
|Rosen, Benjamin S; Hawkins, Peter G; Polan, Daniel F et al. (2018) Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1319-1329|
|Luo, Yi; McShan, Daniel L; Matuszak, Martha M et al. (2018) A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys :|
|Simeth, Josiah; Johansson, Adam; Owen, Dawn et al. (2018) Quantification of liver function by linearization of a two-compartment model of gadoxetic acid uptake using dynamic contrast-enhanced magnetic resonance imaging. NMR Biomed 31:e3913|
|Mendiratta-Lala, Mishal; Masch, William; Shankar, Prasad R et al. (2018) MR Imaging Evaluation of Hepatocellular Carcinoma Treated with Stereotactic Body Radiation Therapy (SBRT): Long Term Imaging Follow-Up. Int J Radiat Oncol Biol Phys :|
|Ohri, Nitin; Tomé, Wolfgang A; Méndez Romero, Alejandra et al. (2018) Local Control After Stereotactic Body Radiation Therapy for Liver Tumors. Int J Radiat Oncol Biol Phys :|
|Mendiratta-Lala, Mishal; Gu, Everett; Owen, Dawn et al. (2018) Imaging Findings Within the First 12 Months of Hepatocellular Carcinoma Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1063-1069|
|Wang, Shulian; Campbell, Jeff; Stenmark, Matthew H et al. (2018) A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer. Radiother Oncol 126:506-510|
Showing the most recent 10 out of 289 publications