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Mining pattern sequences in respiratory tumor motion data.

Arvind Balasubramanian1, Balakrishnan Prabhakaran, Amit Sawant

  • 1The University of Texas, Dallas, TX 75080, USA. arvind@utdallas.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study introduces a novel method for analyzing tumor motion patterns during radiation therapy. It helps predict irregular movements and group patients based on motion similarities for better treatment.

Area of Science:

  • Medical Physics
  • Bioinformatics
  • Radiation Oncology

Background:

  • Effective radiation therapy requires precise management of respiration-induced tumor motion.
  • Tumor motion signals contain valuable patterns for analyzing and predicting irregular movements.
  • Understanding these patterns is key to improving treatment accuracy.

Purpose of the Study:

  • To develop an approach for mining pattern sequences in respiratory tumor motion data.
  • To utilize pattern sequence distributions as representations of motion characteristics.
  • To identify similarities between individual tumor motion instances and group patients.

Main Methods:

  • Mining of pattern sequences within respiratory tumor motion data.
  • Analysis of pattern sequence distributions to characterize motion.

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  • Comparison of individual tumor motion instances to find similarities.
  • Clustering patients based on shared respiratory motion patterns.

    • Main Results:

    • Demonstrated the effectiveness of pattern sequence distributions in representing motion characteristics.
    • Identified similarities between different tumor motion instances.
    • Successfully grouped patients based on their respiratory motion trace patterns.

    Conclusions:

    • Pattern sequence mining offers a valuable tool for analyzing respiratory tumor motion.
    • This approach can enhance the understanding and prediction of irregular tumor motion.
    • Patient grouping based on motion patterns may inform personalized radiation therapy strategies.