This project will study the incorporation of individual patient related geometric uncertainties in the calculation,compilation and treatment of optimized radiotherapy dose distributions. Even advanced treatment planningtechniques nearly always rely on population-based rules to ensure that the majority of patients receiveadequate dose in the face of positioning errors and organ motion. This 'one size fits all' approach potentiallypenalizes patients who exhibit exceptionally large variation in position or motion, requiring more intensivemeasures to ensure adequate target volume coverage. However, it also penalizes a substantial proportion ofpatients with less uncertainty in their target position, who through the use of smaller margins would be atlesser risk for treatment related toxicity or eligible for higher tumor dose. We believe that a new combinedapproach involving a) dose computation strategies that already include the effects of geometric uncertainties,b) rigorous in-room methodologies for rapidly assessing target and patient configuration and c) accountingfor delivered dose and its influence on subsequent treatment delivery optimization will yield improvementsin efficient and accurate dose delivery, optimally tailored for each patient. Thus, the project's specific aims areto 1) implement general clinical frameworks for inclusion of patient related setup uncertainties and organmotion into the computation of dose distributions, 2) assess improvements in accuracy achieved through inroom,on-treatment measurement and action, 3) investigate human anatomic changes over short and longtime periods and how to accumulate dose to date using this information, and 4) determine the best ways toreact to differences between what is seen at treatment and what had been planned. In addition to bettertailoring overall treatments, these investigations will help determine how much complexity (in modeling,measurement and intervention) is actually beneficial for a given patient, thus helping to establish the mostefficient use of advanced in-room imaging resources within the radiotherapy community.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Subcommittee G - Education (NCI)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Ann Arbor
United States
Zip Code
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
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

Showing the most recent 10 out of 289 publications