The goal of Core B Is to provide the clinically-focused infrastructure, support, and continuity necessary to implement individualized adaptive radiation therapy for patients treated in Projects 1 and 2. Gore C personnel provide the logistical support to acquire high fidelity imaging of patients during imaging studies spanning from simulation through pre-treatment imaging and treatment assessment. Cone beam GT or other data acquired as part of individual treatment fractions is collected by Core B and processed utilizing tools from Gores G and D to create an up-to-date patient model of the accumulated dose distribution based on the number of treated fractions. Gore B personnel will re-optimize individual patient plans using the up-to date model of accumulated dose based on anatomic imaging (CBCT) combined with what has been learned with physiologic imaging to tailor a new treatment plan based on the subvolumes analysis by Gore G. Gore B personnel will implement initial and new treatment plans and monitor the patient's progress. The gathered information will also be supplied to Gore D to support their development of decision support tools for clinical use. Gore B personnel will also develop tools and techniques to verify the integrity of imaging systems across multiple hardware and software platforms by utilizing novel phantoms to assess the spatial and signal accuracy of different imaging systems. This information will be used to estimate the baseline uncertainty that applies to all imaging studies when customized fiducials are utilized across imaging systems. Core B personnel will also develop methods to verify the accuracy of the calculated delivered dose by utilizing portal imaging technology and other methods (such as machine log files) to simulate the delivered dose for specific treatment fractions as appropriate. By validating estimates of the delivered dose, Core B can provide information that can be used to evaluate the need for such information. Determining the validity of this approach is critical for patient treatments where there are gross anatomical changes such as tumor shrinkage.These methods have the potential to streamline quality assurance processes so that all adaptations to the patient's treatments are performed in a safe, timely and efficient manner.

Public Health Relevance

A streamlined workflow is needed to support timely implementation of adaptive radiation therapy. This Core will support acquisition of individual patient data from simulation, treatment imaging, and treatment assessment information to develop adaptive plans for safe and robust implementation. This Gore will develop and perform quality assurance across imaging systems and in support of adaptive radiation therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA059827-16A1
Application #
8609645
Study Section
Special Emphasis Panel (ZCA1-RPRB-C (O1))
Project Start
1997-02-01
Project End
2019-04-30
Budget Start
2014-05-15
Budget End
2015-04-30
Support Year
16
Fiscal Year
2014
Total Cost
$280,543
Indirect Cost
$98,724
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Luo, Yi; El Naqa, Issam; McShan, Daniel L et al. (2017) Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis. Radiother Oncol 123:85-92
Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao et al. (2017) Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging. IEEE Trans Med Imaging 36:1116-1128
Hawkins, Peter G; Boonstra, Philip S; Hobson, Stephen T et al. (2017) Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship. Radiother Oncol 125:66-72
Tseng, Huan-Hsin; Luo, Yi; Cui, Sunan et al. (2017) Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys 44:6690-6705
Soni, Payal D; Boonstra, Philip S; Schipper, Matthew J et al. (2017) Lower Incidence of Esophagitis in the Elderly Undergoing Definitive Radiation Therapy for Lung Cancer. J Thorac Oncol 12:539-546
Jochems, Arthur; Deist, Timo M; El Naqa, Issam et al. (2017) Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. Int J Radiat Oncol Biol Phys 99:344-352
Le, Mai; Fessler, Jeffrey A (2017) Efficient, Convergent SENSE MRI Reconstruction for Nonperiodic Boundary Conditions via Tridiagonal Solvers. IEEE Trans Comput Imaging 3:11-21
Hawkins, Peter G; Boonstra, Philip S; Hobson, Stephen T et al. (2017) Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels. Transl Oncol 11:102-108
El Naqa, Issam; Kerns, Sarah L; Coates, James et al. (2017) Radiogenomics and radiotherapy response modeling. Phys Med Biol 62:R179-R206
Dess, Robert T; Sun, Yilun; Matuszak, Martha M et al. (2017) Cardiac Events After Radiation Therapy: Combined Analysis of Prospective Multicenter Trials for Locally Advanced Non-Small-Cell Lung Cancer. J Clin Oncol 35:1395-1402

Showing the most recent 10 out of 259 publications