Title:
Prediction of Plan Adaptation in Head andNeck Cancer Proton Therapy Using Clinical, Radiographic, and Dosimetric Features
Prediction of Plan Adaptation in Head andNeck Cancer Proton Therapy Using Clinical, Radiographic, and Dosimetric Features
Author(s)
Bohannon, Duncan Henry
Advisor(s)
Wang, C.-K. Chris
Zhou, Jun
Biegalski, Steven R.
Zhou, Jun
Biegalski, Steven R.
Editor(s)
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Abstract
Proton therapy has great advantage over photon therapy in head and neck cancer radiotherapy due to the absence of exit dose from the proton beams. However, proton therapy is much more sensitive to anatomical changes and setup uncertainties, which results in up to 40% re-planned rates in head and neck cancers (H&N). Using clinical data from over one hundred H&N patients treated with proton therapy, this project aims to create a neural network which will be trained with patients’ clinical, radiographic, and dosimetric information, and will be able to evaluate plan quality and predict the probability of plan adaption. This will be a great tool to guide clinical practice in proton therapy for not only head and neck cancer treatments, but also other treatment sites that suffer frequent plan adaptations.
Sponsor
Date Issued
2021-08-02
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Resource Type
Text
Resource Subtype
Thesis