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Updated: May 25, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

A dynamic programming approach to adaptive fractionation.

Jagdish Ramakrishnan1, David Craft, Thomas Bortfeld

  • 1Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. jagdish@mit.edu

Physics in Medicine and Biology
|February 15, 2012
PubMed
Summary
This summary is machine-generated.

Dynamic programming (DP) provides a framework for adaptive radiation therapy, enabling evaluation of adaptive fractionation methods. Heuristic methods developed in this study are near-optimal for reducing organ-at-risk (OAR) dose.

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Last Updated: May 25, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Adaptive fractionation in radiation therapy aims to minimize dose to organs-at-risk (OAR) while delivering prescribed tumor dose.
  • Standard fractionation may lead to suboptimal OAR dose delivery due to anatomical variations during treatment.
  • Dynamic anatomical changes necessitate adaptive strategies for optimizing radiation delivery.

Purpose of the Study:

  • To theoretically evaluate solution methods for adaptive fractionation in radiation therapy.
  • To establish dynamic programming (DP) as a benchmark for assessing optimality in adaptive fractionation.
  • To develop and validate near-optimal heuristic methods for adaptive fractionation that are practical for clinical use.

Main Methods:

  • A dynamic programming (DP) algorithm was used to establish an exact solution and characterize optimal policies.
  • Two intuitive, numerically near-optimal heuristic policies were developed for adaptive fractionation.
  • One heuristic was designed to utilize motion probability distribution statistics for realistic application.

Main Results:

  • Dynamic programming (DP) serves as a valuable framework for adaptive radiation therapy, particularly adaptive fractionation.
  • Proposed heuristic methods demonstrate near-optimal performance, allowing evaluation of adaptive fraction size benefits.
  • Significant OAR dose reductions (5-85%) were observed, influenced by motion, fraction number, and allowed fraction size variations.

Conclusions:

  • Adaptive fractionation, guided by DP and heuristic methods, can substantially reduce OAR dose compared to standard fractionation.
  • The developed heuristic policies are suitable for complex, high-dimensional adaptive radiation therapy problems.
  • Optimized adaptive fractionation strategies are most effective with high probabilities of favorable anatomy, numerous fractions, and flexible fraction size adjustments.