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Updated: Jun 25, 2026

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Published on: October 6, 2023

Bi-level Multi-criteria Optimization for Risk-informed Radiotherapy.

Mara Schubert1, Katrin Teichert1, Zhongxing Liao2

  • 1Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer-Platz 1, Kaiserslautern, RP, 67663, Germany.

Physics in Medicine and Biology
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces risk-guided multi-criteria optimization (MCO) for radiation therapy (RT) planning, integrating patient-specific risk factors to reduce radiation pneumonitis (RP) risk while maintaining treatment effectiveness.

Keywords:
NSCLCbiological risk modelsmulti-criteria optimizationpersonalized medicineradiation pneumonitis

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Computational Biology

Background:

  • Multi-criteria optimization (MCO) in radiation therapy (RT) planning conventionally uses population-wide dosimetric criteria.
  • This approach overlooks patient-specific biological risk factors, potentially compromising outcomes for high-risk individuals.
  • Integrating biological risk factors into RT planning is crucial for personalized treatment.

Purpose of the Study:

  • To introduce a novel one-shot method, risk-guided MCO, for integrating biological risk factors into conventional MCO.
  • To enable interactive plan navigation between dosimetric and biological endpoints in RT treatment planning.
  • To demonstrate the feasibility of optimizing RT plans based on individual patient risk profiles.

Main Methods:

  • Retrospective analysis of non-small cell lung cancer (NSCLC) patients undergoing proton/photon RT.
  • Modeling the risk of symptomatic radiation pneumonitis (RP) using logistic regression with dosimetric and patient-specific factors.
  • Fusion of MCO with bi-level optimization to create risk-optimized plans within user-defined trade-offs.

Main Results:

  • Risk-guided MCO plans showed an average reduction of 8.0% in total lung V20Gy and 9.5% in right lung dose.
  • This translated to an average RP risk reduction of 7.7 percentage points (range 0.3%-20.1%).
  • Target coverage was minimally affected (mean -1.2% D98 for CTV), with a modest increase in heart dose (mean +1.74 Gy).

Conclusions:

  • This study presents the first proof-of-concept for integrating biological risk models directly into multi-criteria RT planning.
  • Risk-guided MCO allows a balance between population-wide dose protocols and individualized outcome prediction.
  • The proposed method effectively reduces RP risk while preserving target coverage in NSCLC patients.