Quantitative early decision making metric for identifying irregular breathing in 4DCT

Purpose: 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). Methods: 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. Results: 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. Conclusions: 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.