用AI分析肺部CT，发现狼疮肺病患者气道异常扩张
Deep Learning-Based Airway Segmentation in Systemic Lupus Erythematosus Patients with Interstitial Lung Disease (SLE-ILD): A Comparative High-Resolution CT Analysis
- 基于U-Net的深度学习模型自动分割气道结构
- 上叶及特定段落气道体积显著增大（如右上叶p=0.009）
- 为狼疮相关肺病提供可量化的早期影像标志物
本研究采用基于U-Net架构的深度学习方法，对106例系统性红斑狼疮（SLE）患者（27例伴间质性肺病，79例不伴）的非增强胸部高分辨率CT（HRCT）进行回顾性分析。通过定制化深度学习框架，实现了肺叶与肺段水平的气道结构自动分割。对比两组患者的气道体积，发现SLE-ILD患者在右上叶（p=0.009）和左上叶（p=0.039）的气道体积显著增大；在肺段水平，右上段（R1，p=0.016）、右中段（R3，p<0.001）及左上段（L3，p=0.038）差异显著，以肺上区改变最明显，下区则无统计学差异。结果表明，该自动化方法能有效量化气道体积，揭示了狼疮相关肺病具有特定区域分布特征的气道扩张现象，提示其可能作为疾病存在的潜在影像生物标志物。该人工智能驱动的定量影像指标有望提升SLE人群肺病的早期检测与监测能力。
原文摘要 · Abstract (English)
To characterize lobar and segmental airway volume differences between systemic lupus erythematosus (SLE) patients with interstitial lung disease (ILD) and those without ILD (non-ILD) using a deep learning-based approach on non-contrast chest high-resolution CT (HRCT). Methods: A retrospective analysis was conducted on 106 SLE patients (27 SLE-ILD, 79 SLE-non-ILD) who underwent HRCT. A customized deep learning framework based on the U-Net architecture was developed to automatically segment airway structures at the lobar and segmental levels via HRCT. Volumetric measurements of lung lobes and segments derived from the segmentations were statistically compared between the two groups using two-sample t-tests (significance threshold: p < 0.05). Results: At lobar level, significant airway volume enlargement in SLE-ILD patients was observed in the right upper lobe (p=0.009) and left upper lobe (p=0.039) compared to SLE-non-ILD. At the segmental level, significant differences were found in segments including R1 (p=0.016), R3 (p<0.001), and L3 (p=0.038), with the most marked changes in the upper lung zones, while lower zones showed non-significant trends. Conclusion: Our study demonstrates that an automated deep learning-based approach can effectively quantify airway volumes on HRCT scans and reveal significant, region-specific airway dilation in patients with SLE-ILD compared to those without ILD. The pattern of involvement, predominantly affecting the upper lobes and specific segments, highlights a distinct topographic phenotype of SLE-ILD and implicates airway structural alterations as a potential biomarker for disease presence. This AI-powered quantitative imaging biomarker holds promise for enhancing the early detection and monitoring of ILD in the SLE population, ultimately contributing to more personalized patient management.
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