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Predictive Models to Determine Clinically Relevant Deviations in Delivered Dose for Head and Neck Cancer.
Molly M McCulloch1, Choonik Lee2, Benjamin S Rosen2
1Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan; Department of Nuclear Engineering and Radiological Sciences, The University of Michigan, Ann Arbor, Michigan; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Practical Radiation Oncology
|March 6, 2019
Summary
A new metric can predict the need for re-planning in radiation therapy (RT) by fraction 15. This threshold deviation accurately identifies patients requiring midtreatment adjustments for head and neck cancer, improving treatment accuracy.
Area of Science:
- Radiation Oncology
- Medical Physics
- Cancer Treatment
Background:
- Fractionated radiation therapy (RT) requires precise dose delivery.
- Deviations between planned and delivered doses can impact treatment efficacy.
- Midtreatment assessment is crucial for identifying potential replanning needs.
Purpose of the Study:
- To understand dose deviations in fractionated RT.
- To establish metrics for predicting significant dosimetric deviations midtreatment.
- To evaluate the need for replanning during head and neck cancer RT.
Main Methods:
- Retrospective analysis of 100 head and neck cancer patients.
- Deformable image registration to map contours from planning CT to cone beam CT.
- Dose accumulation calculation based on mapped contours and constraints.
- Development and validation of a predictive threshold deviation model.
Main Results:
- A threshold deviation was established for submandibular glands, predicting re-planning needs by fraction 15 with high sensitivity and specificity.
- The model showed 100% sensitivity and 98.0% specificity in an independent cohort.
- Dose deviations exceeding the threshold were observed in other organs, highlighting the potential for automated dose assessment.
Conclusions:
- A midtreatment threshold deviation metric can predict the necessity of replanning for submandibular glands by fraction 15.
- This metric aids in timely treatment adjustments during fractionated RT.
- Automated dose assessment using deformable image registration shows promise for improving RT planning.
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