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 #
5P01CA059827-20
Application #
9489176
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2018-05-01
Budget End
2019-04-30
Support Year
20
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
El Naqa, Issam; Ruan, Dan; Valdes, Gilmer et al. (2018) Machine learning and modeling: Data, validation, communication challenges. Med Phys 45:e834-e840
El Naqa, Issam; Johansson, Adam; Owen, Dawn et al. (2018) Modeling of Normal Tissue Complications Using Imaging and Biomarkers After Radiation Therapy for Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 100:335-343
Wang, Weili; Huang, Lei; Jin, Jian-Yue et al. (2018) IDO Immune Status after Chemoradiation May Predict Survival in Lung Cancer Patients. Cancer Res 78:809-816
Suresh, Krithika; Owen, Dawn; Bazzi, Latifa et al. (2018) Using Indocyanine Green Extraction to Predict Liver Function After Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 100:131-137
Feng, Mary; Suresh, Krithika; Schipper, Matthew J et al. (2018) Individualized Adaptive Stereotactic Body Radiotherapy for Liver Tumors in Patients at High Risk for Liver Damage: A Phase 2 Clinical Trial. JAMA Oncol 4:40-47
Owen, Daniel Rocky; Boonstra, Phillip S; Viglianti, Benjamin L et al. (2018) Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage. Int J Radiat Oncol Biol Phys 102:1265-1275
Deist, Timo M; Dankers, Frank J W M; Valdes, Gilmer et al. (2018) Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys 45:3449-3459
Johansson, Adam; Balter, James; Cao, Yue (2018) Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI. Magn Reson Med 79:1345-1353
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

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