Calculating Patient Similarity Based on Respiration Induced Tumor Motion

In stereo tactic radiotherapy for thoracic and abdominal tumors, respiratory motion management is crucial for improving efficacy of treatment, while minimizing risk to heathy tissue and organs. Since tumor motion exhibits dynamic variation in characteristics, analyzing the behavior distribution of tumor motion can improve treatment planning. Identifying similarities among patients is an intuitive step towards patient profiling for optimal treatment strategy. This paper investigates how patients, undergoing radiotherapy, can be clustered into five treatment groups, which are identified by medical physicists. And in order to do so, we propose a combination of an adaptive segmentation technique and couple it with a set wise clustering approach for measuring similarity between patients. Our methods aim to group the patients solely based on their tumor motion traces brought about by respiration. Through a series of empirical and statistical analyses, we explore the impact of different feature sets and processing methodologies on the proposed patient grouping approach.