This paper introduces a new objective measure for assessing treatment response in asthmatic patients using computed tomography (CT) imaging data. For each patient, CT scans were obtained before and after one year of monoclonal antibody treatment. Following image segmentation, the Hounsfield unit (HU) values of the voxels were encoded through quantile functions. It is hypothesized that patients with improved conditions after treatment will exhibit better expiration, reflected in higher HU values and an upward shift in the quantile curve. To objectively measure treatment response, a novel linear regression model on quantile functions is developed, drawing inspiration from Verde and Irpino (2010). Unlike their framework, the proposed model is parametric and incorporates distributional assumptions on the errors, enabling statistical inference. The model allows for the explicit calculation of regression coefficient estimators and confidence intervals, similar to conventional linear regression. The corresponding data and R code are available on GitHub to facilitate the reproducibility of the analyses presented.

翻译：本文提出了一种新的客观指标，用于基于计算机断层扫描（CT）影像数据评估哮喘患者的治疗反应。每位患者在接受单克隆抗体治疗一年前后分别接受了CT扫描。经过图像分割后，体素的亨斯菲尔德单位（HU）值通过分位数函数进行编码。假设治疗后病情改善的患者将表现出更好的呼气功能，这反映在更高的HU值以及分位数曲线的上移。为了客观衡量治疗反应，受Verde和Irpino（2010）的启发，我们开发了一种新颖的分位数函数线性回归模型。与他们的框架不同，所提出的模型是参数化的，并包含对误差的分布假设，从而能够进行统计推断。该模型允许显式计算回归系数估计量和置信区间，类似于传统的线性回归。相关数据和R代码已在GitHub上提供，以促进所述分析的可复现性。