SLE脉络丛自动评估：深度学习研究
多中心回顾性研究纳入137例无重大神经精神症状的SLE患者和159例健康对照，用Swin-UNETR在3D T1WI上自动分割脉络丛。SLE伴处理速度与注意力缺陷（PSAD）患者脉络丛体积增大，与系统炎症指数相关，并介导39.2%的SII-PSAD关系。
要点
- SLE-PSAD患者脉络丛体积较SLE-IPSA和健康对照显著增大。
- 脉络丛体积增大、SII升高和C3降低是PSAD独立危险因素。
- ChP体积+C3+SII三标志物模型鉴别PSAD的AUC为0.79（训练）/0.77（验证）。
关键影像表现/征象
- SLE-PSAD患者脉络丛体积显著大于SLE-IPSA患者和健康对照（P < 0.001）。
- 脉络丛体积与系统炎症指数（SII）呈正相关（r = 0.34，P < 0.001）。
诊断标准与关键数值
- Swin-UNETR分割脉络丛的中位DSC为内部0.89、外部0.82（P < 0.001）。
- 贝叶斯 logistic 回归显示脉络丛体积增大（aOR = 2.57）、SII升高（aOR = 2.47）、C3降低（aOR = 0.47）为PSAD独立危险因素。
- LASSO回归三标志物模型（ChP体积+C3+SII）鉴别PSAD的AUC为训练0.79、验证0.77。
临床应用与阅片要点
- 在SLE患者3D T1WI上可自动分割脉络丛并量化体积，辅助PSAD风险分层。
- 报告脉络丛体积时需结合SII和C3等炎症指标综合解读。
局限性
- 回顾性研究设计。
- 外部验证样本量未在原文摘要中报告。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
PURPOSE: This study developed a deep learning model for automated choroid plexus (ChP) segmentation and examined its relationship with systemic inflammation and processing speed and attention deficits (PSAD) in SLE patients without major neuropsychiatric syndromes.
METHODS: In this multicenter retrospective study, 137 SLE patients without major neuropsychiatric syndromes and 159 healthy controls (HCs) were enrolled. The Swin-UNETR model was trained for ChP segmentation on 3D T1-weighted MR images. SLE patients were classified as with processing speed and attention deficits (SLE-PSAD, n = 43) or intact processing speed and attention (SLE-IPSA, n = 94). Clinical, laboratory, and imaging data were compared among groups. Correlation, mediation, and LASSO regression analyses were performed.
RESULTS: Swin-UNETR achieved high segmentation accuracy (median DSC = 0.89 internal, 0.82 external, P < 0.001). ChP volume was significantly greater in SLE-PSAD patients than in SLE-IPSA patients and HCs (P < 0.001) and positively correlated with systemic inflammation index (SII, r = 0.34, P < 0.001). Bayesian logistic regression identified increased ChP volume (aOR = 2.57), elevated SII (aOR = 2.47), and low complement component 3 (C3, aOR = 0.47) as independent PSAD risk factors. ChP volume mediated 39.2% of the SII-PSAD relationship (P < 0.001). LASSO regression identified a minimal three-biomarker model (ChP volume + C3 + SII) with excellent discriminative ability (AUC = 0.79 training, 0.77 validation).
CONCLUSION: Enlarged ChP volume is a biomarker and mediator of PSAD in SLE patients. The Swin-UNETR model enables accurate ChP quantification, and the three-biomarker panel provides a practical tool for SLE-PSAD risk stratification.
引用
- Chang JQ, Hao JC, Zhang XD, et al. Automatic choroid plexus assessment in SLE: a deep learning-enabled study. Neuroradiology. 2026. PMID: 42138734. doi:10.1007/s00234-026-04028-2
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。