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Related Experiment Video

Updated: Jun 10, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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Comparing tumor burden-based classification with traditional methods for knowledge-based VMAT planning in multiple

Zhenzhen Lai1, Houjin Zhang2, Ruilian Xie2

  • 1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, 341000, China.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study compared three knowledge-based models for brain metastasis VMAT planning. Model-V optimized dose gradient, while Model-CA balanced conformity and OAR sparing, improving dose distribution.

Keywords:
Adverse radiation effectIntracranial tumor burdenKnowledge-based planningMultiple brain metastasesVMAT

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Radiotherapy Planning

Background:

  • Accurate dose prediction is crucial for effective volumetric modulated arc therapy (VMAT) in treating multiple brain metastases (multi-BM).
  • Knowledge-based planning (KBP) offers potential for improving treatment plan quality and consistency.

Purpose of the Study:

  • To systematically compare the dose prediction performance of three distinct knowledge-based RapidPlan models: Broad-Spectrum Model (Model-BS), K-means Clustering-Based Classification Model (Model-CA), and Volume Stratification Model (Model-V).
  • To evaluate these models in the context of VMAT planning for patients with multi-BM.

Main Methods:

  • Retrospective analysis of 138 multi-BM patients treated with VMAT (30 Gy/5 Fr).
  • Development of Model-BS (general), Model-CA (clustering based on lesion count/volume), and Model-V (volume stratification).
  • Comparison of automated plans (BS-plans, CA-plans, V-plans) against manual plans (MP-plans) using dose metrics (CI, GI, OAR doses) in a test group of 16 patients.

Main Results:

  • All KBP models demonstrated comparable or improved target coverage versus manual plans.
  • Model-CA and Model-V significantly reduced high-dose volumes in the brain compared to Model-BS (p < 0.05).
  • Model-V achieved the lowest gradient index (GI=5.55) and highest conformity index (CI=0.924), while Model-CA showed a better balance for OAR sparing (e.g., optic nerve Dmax).

Conclusions:

  • Classification-based KBP models, utilizing tumor lesion count and total volume, significantly enhance dose distribution for multi-BM VMAT.
  • Model-V excels in dose gradient optimization, whereas Model-CA provides a superior overall balance of target conformity, dose fall-off, and organ-at-risk sparing.