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An optimized knowledge-based planning method for craniospinal irradiation integrated with auto-contouring.

Yihua Zhong1, Mingyuan Pan2, Jiyou Peng3

  • 1Radiation Oncology Center, Huashan Hospital, Fudan University, Shanghai, China.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

An automated workflow for craniospinal irradiation (CSI) significantly reduces planning time and maintains clinical quality. This AI-driven approach enhances efficiency for complex cancer treatments.

Keywords:
Auto-contouringCraniospinal irradiationKnowledge-based planningMachine learning

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Craniospinal irradiation (CSI) is a complex radiotherapy technique requiring precise target and organ delineation.
  • Traditional CSI planning is time-consuming and expertise-intensive, limiting efficiency.
  • AI applications in CSI planning are hindered by generalizability and outlier sensitivity challenges.

Purpose of the Study:

  • To develop and evaluate an automated workflow for craniospinal irradiation (CSI) planning.
  • To integrate deep learning auto-contouring and machine learning-enhanced rapid planning.
  • To assess the efficiency and plan quality improvements of the automated workflow.

Main Methods:

  • Developed a DPNUNet-based auto-contouring model trained on 91 CSI patients.
  • Integrated machine learning for iterative refinement of a knowledge-based planning (KBP) model.
  • Compared 20 automated rapid plans (RP) against 20 manual plans (MP) using dosimetric indices.

Main Results:

  • Auto-contouring model achieved Dice coefficients of 0.93 for PTV and >0.85 for OARs, reducing contouring time by 75% (1-2 hours to 10 minutes).
  • RapidPlan (RP) met clinical targets and improved OAR sparing but had higher PTV hotspots and monitor units.
  • The automated workflow reduced overall contouring and planning time by 75% (6-8 hours to 1 hour).

Conclusions:

  • The proposed AI-driven framework significantly enhances efficiency in CSI planning.
  • Clinical plan quality is maintained while achieving substantial time savings.
  • The automated workflow demonstrates feasibility for standardized craniospinal irradiation planning.