Mining Pattern Sequences in Respiratory Tumor Motion Data

Management of respiration induced tumor motion during radiation therapy is crucial to effective treatment. Pattern sequences in the tumor motion signals can be valuable features in the analysis and prediction of irregular tumor motion. In this study, we put forward an approach towards mining pattern sequences in respiratory tumor motion data. We discuss the use of pattern sequence distributions as effective representations of motion characteristics, and find similarities between individual tumor motion instances. We also explore grouping of patients based on similarities in pattern sequence distributions exhibited by their respiratory motion traces.