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Exploring Baseline Shift Prediction in Respiration Induced Tumor Motion

机译：探索呼吸肿瘤运动中基线移位预测

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Effective management of respiratory motion is essential for achieving the clinical goals of stereo tactic thoracic and abdominal radiotherapy, where highly potent radiation beams are precisely directed in order to ablate the tumor, while minimizing radiation damage to normal tissue and critical organs. Due to cycle-to-cycle variations in respiratory motion, it is important to be able to predict imminent anomalous or irregular tumor motion ahead of its occurrence. Such information can then be used to pause the radiation delivery, or to track the moving tumor. However, predicting tumor motion anomalies presents a challenge as the occurrence of these anomalies can vary from patient to patient and from day to day for the same patient. In this paper, we explore the use of observed data in predicting baseline trends, and baseline shifts, in particular. Using a tumor motion dataset obtained from 143 treatment fractions from 42 patients treated with Cyber knife Synchrony System, we execute multifaceted analyses, including offline and online scenarios. Given the variation in tumor motion patterns and the absence of standardized baselines and adequate personalized prior data, we compare performances of standard prediction algorithms with and without training on prior data. Our analyses yield promising results for baseline shift prediction, and real-time baseline trend estimation in general.
机译：有效管理呼吸运动对于实现立体声胸胸和腹部放射疗法的临床目标至关重要，其中高效的辐射束精确指导以烧蚀肿瘤，同时最小化对正常组织和临界器官的辐射损伤。由于呼吸运动的循环到周期变化，重要的是能够在其发生之前预测迫在眉睫的异常或不规则的肿瘤运动。然后可以使用这些信息来暂停辐射递送，或跟踪移动肿瘤。然而，预测肿瘤运动异常存在挑战，因为这些异常的发生可以从患者因患者而且每天为同一患者的日常活动。在本文中，我们探讨了在预测基线趋势中的观察数据以及基线班次的使用。使用从通过网络刀同步系统治疗的42名患者获得的143例治疗组分中获得的肿瘤运动数据集，我们执行多方面的分析，包括离线和在线情景。鉴于肿瘤运动模式的变化和缺乏标准化的基线和足够的个性化的先前数据，我们可以比较标准预测算法的性能，而不在不培训之前的数据。我们的分析产生了基线移位预测的有希望的结果，以及一般的实时基线趋势估计。

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