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Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.

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Standardizing radiotherapy nomenclature is crucial for big data in healthcare. This study used multi-modal artificial neural networks to accurately classify all breast cancer radiotherapy structures, achieving 99.4% accuracy.

Keywords:
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Area of Science:

  • Medical Physics
  • Artificial Intelligence in Healthcare
  • Radiotherapy Oncology

Background:

  • Standardized nomenclature for target volumes (TV) and organs-at-risk (OAR) is essential for big data analysis in radiotherapy.
  • Current methods for standardizing volume nomenclature using machine learning (ML) often target only subsets of structures.

Purpose of the Study:

  • To propose and evaluate a novel approach for standardizing all radiotherapy structure nomenclature using multi-modal artificial neural networks.
  • To investigate the impact of different feature types (textual, geometric, dosimetry, imaging) on nomenclature standardization performance.

Main Methods:

  • A cohort of 1613 breast cancer patients treated with radiotherapy was analyzed.
  • Multi-modal artificial neural networks were employed, integrating textual, geometric, dosimetry, and imaging features.
  • Multiple datasets representing subsets and the complete list of volumes were created and tested with various feature combinations.

Main Results:

  • The best model achieved 99.416% classification accuracy in standardizing all breast cancer radiotherapy plan nomenclatures into 21 classes.
  • The study demonstrated the effectiveness of including multiple data modalities for comprehensive volume representation and standardization.

Conclusions:

  • Machine learning-based automation, particularly with multi-modal data, offers a robust solution for standardizing radiotherapy planning nomenclature.
  • This approach facilitates improved data pooling and analysis, advancing the use of big data in health systems.