Kiely Janid P Blanco, Olszanski Arthur, Both Stefan, Low Daniel A, White Benjamin M
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California 90025.
Med Phys. 2015 Oct;42(10):5654-60. doi: 10.1118/1.4929636.
To develop a quantitative early decision making metric for prediction of breathing pattern and irregular breathing and validate the metric in a large patient population receiving clinical phase-sorted four-dimensional computed tomography (4DCT).
This study employed three patient cohorts. The first cohort contained 47 patients, imaged with a nonclinical tidal volume metric. The second cohort contained a sample of 256 patients who received a clinical 4DCT. The third cohort contained 86 patients who received three 4DCT scans at 1-week increment during the course of radiotherapy. The second and third cohorts did not have tidal volume measurements, as per standard radiation oncology clinical practice. Based on a previously published technique that used a single abdominal surrogate, the ratio of extreme inhalation tidal volume to normal inhalation tidal volume (κ) metric was calculated and the patient breathing pattern was characterized. The use of a single surrogate precluded the use of a κ determined by tidal volume, so a κ(rel) was defined based on the amplitude of the surrogate. Patients were classified as either Type 1 or Type 2, based on a previously published technique, where Type 1 patients were apneic at end of exhalation and Type 2 patients exhibited forced respiration. The Ansari-Bradley test was used to determine the statistical similarity between the Type 1 and Type 2 distributions. A Kruskal-Wallis one way analysis of variance was used to determine the statistical similarities among the classified breathing types, κ(rel), and the qualified medical physicist denoted breathing classification (regular or irregular). Receiver operator characteristic curves were used to quantitatively determine optimal cutoff value j(κ) and efficiency cutoff value (τ(κ)) κ(rel) to provide a quantitative early warning of irregular breathing during 4DCT procedures.
The statistical tests show a significant consistency for the breathing pattern classifications between the physiologically measured cohort #1 and the remaining cohorts. The classification types were statistically different between Type 1 and Type 2 patients over all cohorts. Values of κ(rel) in excess of 1.72 indicated a substantial presence of irregular breathing that could negatively affect the quality of a 4DCT image dataset. Values of κ(rel) in lower than 1.45 indicated minimal presence of irregular breathing. For values of κ(rel) such that j(κ) ≤ κ(rel) ≤ τ(κ), the decision to reacquire the 4DCT would be at the discretion of the physician. This accounted for only 11.9% of the patients in this study. The magnitude of κ(rel) held consistent over three weeks of treatment for 73% of the patients in cohort #3.
The decision making metric based on κ was shown to be an accurate classifier of regular and irregular breathing patterns in a large patient population. Breathing type, as defined in a previous published work, was accurately classified by κ(rel) with the use of a single respiratory surrogate compared to the physiological use of multiple respiratory surrogates. This work provided a quantitative early decision making metric to quickly and accurately assess breathing patterns as well as the presence and magnitude of irregular breathing during 4DCT.
开发一种用于预测呼吸模式和不规则呼吸的定量早期决策指标，并在接受临床相位排序四维计算机断层扫描（4DCT）的大量患者群体中验证该指标。
本研究采用了三个患者队列。第一个队列包含47名患者，使用非临床潮气量指标进行成像。第二个队列包含256名接受临床4DCT的患者样本。第三个队列包含86名在放射治疗过程中每隔1周接受三次4DCT扫描的患者。根据标准放射肿瘤学临床实践，第二和第三个队列没有潮气量测量值。基于先前发表的使用单个腹部替代指标的技术，计算了极端吸气潮气量与正常吸气潮气量的比值（κ）指标，并对患者的呼吸模式进行了特征描述。使用单个替代指标排除了由潮气量确定的κ的使用，因此基于替代指标的幅度定义了κ（rel）。根据先前发表的技术，患者被分类为1型或2型，其中1型患者在呼气末呼吸暂停，2型患者表现出强制呼吸。使用Ansari-Bradley检验来确定1型和2型分布之间的统计相似性。使用Kruskal-Wallis单因素方差分析来确定分类呼吸类型、κ（rel）和合格医学物理学家指定的呼吸分类（规则或不规则）之间的统计相似性。使用受试者操作特征曲线来定量确定最佳截断值j（κ）和效率截断值（τ（κ））κ（rel），以在4DCT程序期间提供不规则呼吸的定量早期预警。
统计检验表明，生理测量的队列1与其余队列之间的呼吸模式分类具有显著一致性。在所有队列中，1型和2型患者的分类类型在统计学上存在差异。κ（rel）值超过1.72表明存在大量可能对4DCT图像数据集质量产生负面影响的不规则呼吸。κ（rel）值低于1.45表明不规则呼吸的存在最少。对于j（κ）≤κ（rel）≤τ（κ）的κ（rel）值，重新获取4DCT的决定将由医生自行决定。这仅占本研究中患者的11.9%。对于队列3中73%的患者，κ（rel）的大小在三周的治疗过程中保持一致。
基于κ的决策指标被证明是大量患者群体中规则和不规则呼吸模式的准确分类器。与使用多个呼吸替代指标的生理学方法相比，先前发表的工作中定义的呼吸类型通过使用单个呼吸替代指标的κ（rel）进行了准确分类。这项工作提供了一种定量早期决策指标，以快速准确地评估4DCT期间的呼吸模式以及不规则呼吸的存在和程度。
