[论文解读] Deep Learning-Based Airway Segmentation in Systemic Lupus Erythematosus Patients with Interstitial Lung Disease (SLE-ILD): A Comparative High-Resolution CT Analysis

Sirong Piao, Ying Ming+12 more|arXiv (Cornell University)|Mar 18, 2026

Systemic Lupus Erythematosus Research被引用 0

一句话总结

研究开发基于深度学习（U-Net）的非增强 HRCT 自动气道分割框架，以比较 SLE 患者有ILD与无ILD之间的气道体积，揭示 SLE-ILD 的区域特异性气道扩张。相较于无ILD，SLE-ILD 在上叶显著且在某些分段气道有扩张。

ABSTRACT

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.

研究动机与目标

• 通过自动化基于 HRCT 的分割，表征 SLE-ILD 与 SLE-non-ILD 之间的 lobar 与 segmental 气道体积差异。
• 开发基于 U-Net 的框架，在 lobar 与 segmental 级别自动分割气道结构。
• 利用分割结果量化肺部气道体积测量，并使用统计检验在患者组之间进行比较。

提出的方法

• 对 106 例 SLE 患者（27 例 SLE-ILD，79 例 SLE-non-ILD）进行非增强 HRCT 的回顾性分析。
• 基于 U-Net 的定制深度学习框架在 lobar 与 segmental 级别自动分割气道结构。
• 从分割结果推导体积测量，并通过两独立样本 t 检验在组间比较（p<0.05）。
• 统计重点在区域差异，特别是上叶和特定段。

实验结果

研究问题

• RQ1 SLE-ILD 患者在 HRCT 上是否显示与 SLE-non-ILD 不同的 lobar 气道体积？
• RQ2 是否存在在 segmental 级别的气道差异可将 SLE-ILD 与非ILD 区分开来，且最显著的区域在哪里？
• RQ3 基于 DL 的气道分割方法是否能可靠量化区域性气道扩张，作为 SLE 中 ILD 存在的潜在生物标志物？

主要发现

• SLE-ILD 的右上叶气道显著扩大（p=0.009），左上叶也显著扩大（p=0.039），与 SLE-non-ILD 相比。
• R1（p=0.016）、R3（p<0.001）、L3（p=0.038）等分段存在显著差异，上部肺区域变化最显著。
• 下肺区气道差异趋势非显著。
• 基于 DL 的自动气道分割在 HRCT 上有效量化气道体积并检测区域性变化。
• 结果提示 SLE-ILD 存在明显的拓扑表型，并且存在作为疾病存在生物标志物的潜在气道基础。