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Knowledge-based treatment planning in breast cancer radiotherapy: comparing different machine learning algorithms.

Mostafa Robatjazi1,2, Saba Ordibeheshti3, Atefeh Rostami1,2

  • 1Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran.

Physical and Engineering Sciences in Medicine
|June 8, 2026
PubMed
Summary

Machine learning accurately predicts radiotherapy outcomes for breast cancer patients, improving treatment planning efficiency. The KNN algorithm showed the most reliable predictions, enhancing consistency in 3D-CRT plans.

Keywords:
3D-conformal radiotherapyBreast cancerKnowledge based treatment planningMachine learning

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Area of Science:

  • Medical Physics
  • Radiotherapy Oncology
  • Machine Learning in Healthcare

Background:

  • Knowledge-based planning (KBP) leverages prior clinical data to enhance radiotherapy consistency and efficiency.
  • Manual trial-and-error in treatment planning can be time-consuming and lead to variability.
  • Predicting dosimetric parameters is crucial for optimizing radiation delivery and minimizing toxicity.

Purpose of the Study:

  • To evaluate machine learning (ML) algorithms for predicting key dosimetric parameters in 3D conformal radiotherapy (3D-CRT) for left-sided breast cancer.
  • To assess the potential of ML-driven KBP to reduce manual optimization efforts.
  • To identify the most effective ML algorithm for this predictive task.

Main Methods:

  • A retrospective dataset of 75 breast cancer patients treated with 3D-CRT was analyzed.
  • Ten supervised regression ML algorithms were trained to predict dosimetric outcomes (e.g., D_mean, V_x, HI) using geometric and organ-at-risk (OAR) features.
  • Model performance was evaluated using cross-validation and an independent test set with metrics like RMSE and MAPE.

Main Results:

  • The K-Nearest Neighbors (KNN) algorithm demonstrated the most consistent and reliable predictive performance across all evaluated dosimetric endpoints.
  • KNN achieved the lowest Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for heart and lung D_mean.
  • Geometric parameters and OAR volumes were identified as the most significant predictors of dosimetric outcomes.

Conclusions:

  • ML-based KBP can accurately predict radiotherapy dosimetric outcomes before dose calculation, improving planning efficiency and consistency for breast 3D-CRT.
  • The KNN algorithm shows high reliability and is suitable for integration into clinical decision-support systems.
  • This approach has the potential to optimize treatment planning and reduce inter-planner variability.