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Related Experiment Video

Updated: Sep 21, 2025

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A prediction model for dosimetric-based lung adaptive radiotherapy.

Chaoqiong Ma1,2, Zhen Tian2,3, Ruoxi Wang1

  • 1Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.

Medical Physics
|June 1, 2022
PubMed
Summary

This study developed a prediction model to identify lung cancer patients needing adaptive radiotherapy (ART) due to anatomical changes during treatment. The model helps clinicians decide when to adapt radiation plans for better outcomes.

Keywords:
adaptive radiotherapylung cancermachine learningreplanning prediction

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Area of Science:

  • Radiation Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Anatomical changes during lung cancer radiation therapy can cause significant dosimetric deviations.
  • Adaptive radiotherapy (ART) aims to correct these deviations through plan adaptation.
  • Identifying the optimal time for plan adaptation is crucial for ART's effectiveness.

Purpose of the Study:

  • To develop a predictive model for identifying lung cancer patients who would benefit from adaptive radiotherapy.
  • To aid clinical decision-making regarding plan adaptations during treatment.

Main Methods:

  • Utilized 71 weekly cone-beam CT (CBCT) and planning CT (pCT) pairs from 17 lung cancer patients.
  • Extracted 16 morphological features from the region of interest (ROI) and 8 overlapped volume histogram (OVH) features.
  • Developed a nonlinear support vector machine model using 24 features to predict the need for plan adaptation.

Main Results:

  • The prediction model achieved an AUC of 0.929 and accuracy of 0.851 using six selected features (5 ROI, 1 OVH).
  • Excluding OVH features reduced model performance (AUC 0.826, accuracy 0.779).
  • Univariate models showed significantly poorer performance (AUC 0.76).

Conclusions:

  • A prediction model was successfully developed using on-treatment CBCT imaging features to guide plan adaptation in lung ART.
  • The model effectively links anatomical changes to dosimetric impacts, assisting clinicians in treatment decisions.
  • This tool shows promise for optimizing adaptive radiotherapy for lung cancer patients.