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Title: TH-CD-209-09: Quickly Identifying Good Candidates for Proton Therapy From Geometric Considerations

Abstract

Purpose: We developed a knowledge-based model that can predict the patient-specific benefits of proton therapy based upon geometric considerations. The model could also aid patient selection in model-based clinical trials or help justify clinical decisions to insurance companies. Methods: The knowledge-based method trains a model upon existing proton treatment plans, exploiting correlations between dose and distance-to-target. Each OAR is split into concentric subvolumes surrounding the target volume, and a skew-normal PDF is fit to the dose distribution found within each shell. The model learns from shared trends in how the best-fit skew-normal parameters depend upon distance-to-target. It can then predict feasible OAR DVHs for a new patient (without a proton plan) based upon their geometry. The expected benefits of proton therapy are assessed by comparing the predicted DVHs to those of an IMRT plan, using a metric such as the equivalent uniform dose (EUD). Results: A model was trained for clival chordoma, owing to its geometric complexity and the multitude of nearby OARs. The model was trained using 20 patients and validated with a further 20 patients, and considers several different OARs. The predicted EUD was in good agreement with that of the actual proton plan. The coefficient of determinationmore » (R-squared) was 85% overall, 92% for cochleas, 80% for optic chiasm and 79% for spinal cord. The model exhibited no signs of bias or overfitting. When compared to an IMRT plan, the model could classify whether a patient will experience a gain or a loss with an accuracy between 75% and 95%, depending upon the OAR. Conclusion: We developed a model that can quickly and accurately predict the patient-specific benefits of proton therapy in clival chordoma patients, though models could be trained for other tumor sites. This work is funded by National Cancer Institute grant U19 CA 021239.« less

Authors:
; ; ; ;  [1]
  1. Massachusetts General Hospital, Boston, MA (United States)
Publication Date:
OSTI Identifier:
22679336
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; CLINICAL TRIALS; GEOMETRY; PATIENTS; PROTON BEAMS; RADIATION DOSE DISTRIBUTIONS; RADIOTHERAPY; SPINAL CORD

Citation Formats

Hall, D, Trofimov, A, Winey, B, Liebsch, N, and Paganetti, H. TH-CD-209-09: Quickly Identifying Good Candidates for Proton Therapy From Geometric Considerations. United States: N. p., 2016. Web. doi:10.1118/1.4958203.
Hall, D, Trofimov, A, Winey, B, Liebsch, N, & Paganetti, H. TH-CD-209-09: Quickly Identifying Good Candidates for Proton Therapy From Geometric Considerations. United States. doi:10.1118/1.4958203.
Hall, D, Trofimov, A, Winey, B, Liebsch, N, and Paganetti, H. Wed . "TH-CD-209-09: Quickly Identifying Good Candidates for Proton Therapy From Geometric Considerations". United States. doi:10.1118/1.4958203.
@article{osti_22679336,
title = {TH-CD-209-09: Quickly Identifying Good Candidates for Proton Therapy From Geometric Considerations},
author = {Hall, D and Trofimov, A and Winey, B and Liebsch, N and Paganetti, H},
abstractNote = {Purpose: We developed a knowledge-based model that can predict the patient-specific benefits of proton therapy based upon geometric considerations. The model could also aid patient selection in model-based clinical trials or help justify clinical decisions to insurance companies. Methods: The knowledge-based method trains a model upon existing proton treatment plans, exploiting correlations between dose and distance-to-target. Each OAR is split into concentric subvolumes surrounding the target volume, and a skew-normal PDF is fit to the dose distribution found within each shell. The model learns from shared trends in how the best-fit skew-normal parameters depend upon distance-to-target. It can then predict feasible OAR DVHs for a new patient (without a proton plan) based upon their geometry. The expected benefits of proton therapy are assessed by comparing the predicted DVHs to those of an IMRT plan, using a metric such as the equivalent uniform dose (EUD). Results: A model was trained for clival chordoma, owing to its geometric complexity and the multitude of nearby OARs. The model was trained using 20 patients and validated with a further 20 patients, and considers several different OARs. The predicted EUD was in good agreement with that of the actual proton plan. The coefficient of determination (R-squared) was 85% overall, 92% for cochleas, 80% for optic chiasm and 79% for spinal cord. The model exhibited no signs of bias or overfitting. When compared to an IMRT plan, the model could classify whether a patient will experience a gain or a loss with an accuracy between 75% and 95%, depending upon the OAR. Conclusion: We developed a model that can quickly and accurately predict the patient-specific benefits of proton therapy in clival chordoma patients, though models could be trained for other tumor sites. This work is funded by National Cancer Institute grant U19 CA 021239.},
doi = {10.1118/1.4958203},
journal = {Medical Physics},
issn = {0094-2405},
number = 6,
volume = 43,
place = {United States},
year = {2016},
month = {6}
}