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Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language: A Pilot Study.

Rohit Singh Chauhan1,2, Anirudh Pradhan3, Anusheel Munshi2

  • 1Department of Physics, GLA University, Mathura, Uttar Pradesh, India.

Journal of Medical Physics
|June 21, 2023
PubMed
Summary

An automated data mining approach using a Python script significantly speeds up data extraction from treatment planning systems (TPS). This method is over 6000 times faster and more accurate than manual data extraction for radiation oncology applications.

Keywords:
Data miningprogramming languagesradiotherapysoftwaretime management

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Area of Science:

  • Data Science in Healthcare
  • Radiation Oncology Informatics
  • Medical Physics

Background:

  • Data science is increasingly integrated into healthcare, including radiation oncology.
  • Manual data extraction from treatment planning systems (TPS) is time-consuming and prone to errors.
  • There is a need for efficient and accurate automated data extraction methods in TPS.

Purpose of the Study:

  • To develop and evaluate an automated data mining approach for extracting patient and treatment data from TPS.
  • To compare the time efficiency and accuracy of automated data extraction versus manual methods.

Main Methods:

  • A Python script was developed to extract 25 specified features from TPS using an application programming interface.
  • The script was applied to data from 427 patients undergoing external beam radiation therapy.
  • Data extraction time and accuracy were compared between the automated script and manual extraction.

Main Results:

  • The Python script extracted 25 features for 427 patients in 0.28 ± 0.03 minutes with 100% accuracy.
  • Manual extraction averaged 4.5 ± 0.33 minutes per patient, with errors and missing data.
  • The automated approach was over 6850 times faster than manual extraction.
  • Scaling the number of features had a minimal impact on the script's extraction time compared to manual methods.

Conclusions:

  • An in-house Python script provides a highly efficient and accurate method for extracting plan data from TPS.
  • Automated data mining significantly outperforms manual extraction in speed and accuracy for radiation oncology data.
  • This approach has the potential to streamline research and clinical workflows in radiation oncology.