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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-RPRB-C (O1))
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University of Michigan Ann Arbor
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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

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