Supplementary Material for: Quantitative Evaluation of Changes in Three-Dimensional CT Density Distributions in Pulmonary Alveolar Proteinosis after GM-CSF Inhalation

<b><i>Background:</i></b> A previous clinical trial for autoimmune pulmonary alveolar proteinosis (APAP) demonstrated that granulocyte-macrophage colony-stimulating factor (GM-CSF) inhalation reduced the mean density of the lung field on computed tomography (CT) across 18 axial slice planes at a two-dimensional level. In contrast, in this study, we challenged three-dimensional analysis for changes in CT density distribution using the same datasets. <b><i>Methods:</i></b> As a sub-study of the trial, CT data of 31 and 27 patients who received GM-CSF and placebo, respectively, were analyzed. To overcome the difference between various shooting conditions, a newly developed automatic lung field segmentation algorithm was applied to CT data to extract the whole lung volume, and the accuracy of the segmentation was evaluated by five pulmonary physicians independently. For normalization, the percent pixel (PP) in a certain density range was calculated as a percentage of the total number of pixels from −1,000 to 0 HU. <b><i>Results:</i></b> The automatically segmented images revealed that the lung field was accurately extracted except for 7 patients with minor deletion or addition. Using the change in PP from baseline to week 25 (ΔPP) as the vertical axis, we created a histogram with 143 HU bins set for each patient. The most significant difference in ΔPP between GM-CSF and placebo groups was observed in two ranges: from −1,000 to −857 and −143 to 0 HU. <b><i>Conclusion:</i></b> Whole lung extraction followed by density histogram analysis of ΔPP may be an appropriate evaluation method for assessing CT improvement in APAP.

**背景：** 此前一项针对自身免疫性肺泡蛋白沉积症（autoimmune pulmonary alveolar proteinosis, APAP）的临床试验表明，吸入粒细胞-巨噬细胞集落刺激因子（granulocyte-macrophage colony-stimulating factor, GM-CSF）可在二维层面上降低18个轴位断层层面的计算机断层扫描（computed tomography, CT）肺野平均密度。与之相对，本研究针对同一数据集，尝试开展CT密度分布变化的三维分析。
**方法：** 作为该临床试验的亚组研究，本研究对分别接受GM-CSF治疗与安慰剂治疗的31例及27例患者的CT数据进行分析。为消除不同扫描条件带来的差异，本研究采用新开发的全自动肺野分割算法对CT数据进行处理以提取全肺容积，并由5名呼吸科医师独立评估分割精度。为实现归一化，本研究将特定密度范围内的像素百分比（percent pixel, PP）以-1000至0亨氏单位（HU）区间内的总像素数为基准进行百分比计算。
**结果：** 自动分割结果显示，除7例患者存在轻微的肺野漏分割或过度分割外，其余患者的肺野均被准确提取。本研究以基线至第25周的PP变化量（ΔPP）为纵轴，为每位患者构建了以143 HU为组距的直方图。GM-CSF治疗组与安慰剂组的ΔPP差异最显著的区间为-1000至-857 HU以及-143至0 HU。
**结论：** 全肺容积提取联合ΔPP密度直方图分析，或可成为评估APAP患者CT影像改善情况的合适评估方法。