Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.17547v1</id>\n    <title>Deep Learning-Based Airway Segmentation in Systemic Lupus Erythematosus Patients with Interstitial Lung Disease (SLE-ILD): A Comparative High-Resolution CT Analysis</title>\n    <updated>2026-03-18T09:52:17Z</updated>\n    <link href='https://arxiv.org/abs/2603.17547v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.17547v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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 &lt; 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&lt;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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='eess.IV'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <published>2026-03-18T09:52:17Z</published>\n    <arxiv:primary_category term='eess.IV'/>\n    <author>\n      <name>Sirong Piao</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Ying Ming</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Ruijie Zhao</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Jiaru Wang</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Ran Xiao</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Rui Zhao</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Zicheng Liao</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Qiqi Xu</name>\n      <arxiv:affiliation>Research and Development Center</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Shaoze Luo</name>\n      <arxiv:affiliation>Research and Development Center</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Bing Li</name>\n      <arxiv:affiliation>Research and Development Center</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Lin Li</name>\n      <arxiv:affiliation>Research and Development Center</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Zhuangfei Ma</name>\n      <arxiv:affiliation>Canon Medical Systems</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Fuling Zheng</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Wei Song</name>\n      <arxiv:affiliation>Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China</arxiv:affiliation>\n    </author>\n  </entry>"
}