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Related Experiment Video

Updated: Jun 13, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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Machine Learning-Based Support for Monitor Unit and Lung Shielding Estimation in Conventional Total Body Irradiation.

Christian Fiandra1, Francesca Romana Giglioli2, Elena Gallio2

  • 1Department of Oncology, University of Turin, 10126 Turin, Italy.

Cancers
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict total body irradiation (TBI) parameters like monitor units and lung shielding. This approach enhances efficiency and reduces variability in hematopoietic stem cell transplantation workflows.

Keywords:
artificial intelligencedecision support systemsradiotherapy planningtotal body irradiation

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Machine Learning in Healthcare

Background:

  • Total body irradiation (TBI) is a critical component of conditioning regimens prior to hematopoietic stem cell transplantation.
  • Conventional TBI planning involves manual determination of monitor units and lung shielding, introducing operator variability.
  • Developing automated methods for TBI parameter prediction is essential for improving treatment consistency.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting monitor unit (MU) calculations and lung shielding thickness in conventional opposed-field TBI.
  • To assess the performance of ML models using routinely available clinical and imaging data.
  • To evaluate the potential of ML to reduce operator-dependent variability in TBI treatment planning.

Main Methods:

  • Retrospective analysis of 80 patients undergoing conventional opposed-field TBI for MU prediction.
  • Utilized LASSO and Ridge regression for feature selection and final MU modeling.
  • Developed a Random Forest regression model for lung shielding thickness prediction using planning CT data from 66 patients.
  • Employed nested 5-fold cross-validation and Mean Absolute Error (MAE) for performance assessment.

Main Results:

  • The optimized Ridge regression model for MU prediction achieved an MAE of 74.0 ± 6.9 MU, outperforming the full-feature model (115.6 ± 44.0 MU).
  • A Random Forest benchmark model yielded an MAE of 81.1 ± 10.3 MU for MU prediction.
  • The Random Forest model for lung shielding thickness prediction achieved an MAE of 0.60 mm, within clinical tolerance.
  • Predicted uncertainties aligned with accepted in vivo dosimetric tolerances.

Conclusions:

  • Machine learning models show significant potential for supporting the accurate estimation of critical TBI treatment parameters.
  • ML-based prediction can enhance workflow efficiency and minimize operator-dependent variability in TBI planning.
  • These ML models serve as valuable adjuncts to standard treatment planning and verification processes in radiation oncology.