Big Data Meets Medical Physics Dosimetry

نویسندگان

  • Fumbeya Marungo
  • Hilary Paisley
  • John Rhee
چکیده

Medical physicists face a trade-off when planning oncology radiotherapy treatments. The dosimetrist must deliver sufficient dose to kill the diseased tissue while managing the risk of a variety of possible toxicities that may arise from damage to normal tissue. In attempting to address this trade-off, many questions naturally arise. Are there critical sections within organs that should be spared? More generally, what are the likely complication profiles for a variety of treatment regiments? These questions are ever more pressing as oncology are multi-modal, often involving combinations chemo and radiation therapy, and as increased survival rates translates into patients who, while treated successfully, will permanently suffer decreased quality of life due to complications[1]. A natural approach to answer the questions above is to create a multi-factored method to assess the probability of various complications, and use the method to assess a variety of treatment plans. However, traditional models for calculating what is known as normal tissue complication probability (NTCP) are not sufficient for such a task. For example, the Lyman-Kutcher-Burman (LKB) model only uses two factors — the maximum dose delivered, and the variation of dosage uniformly across the volume. The model accounts for neither a patient’s medical history, nor other treatments the patients receives. Further, by assuming an equivalence between higher doses over small volumes and lower doses over large volumes, LKB does not account for dose placement. The goal of Project IX is to create a platform that allows for the development of data-driven multifactor models that can improve NTCP calculation and address the questions above. Such models can allow previous experience to inform treatment planning (Figure 1.1), and to obtain insights into the factors leading to treatment complications.

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تاریخ انتشار 2014