Urashima Kayoko, Ichinose Kunihiro, Ishimaru Hideki, Kumazaki Hirokazu, Kawakami Atsushi, Ueki Masao
Department of Neuropsychiatry, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Department of Immunology and Rheumatology, Division of Advanced Preventive Medical Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
PLoS One. 2025 Aug 13;20(8):e0329859. doi: 10.1371/journal.pone.0329859. eCollection 2025.
Diagnosing neuropsychiatric systemic lupus erythematosus (NPSLE) and differentiating it from systemic lupus erythematosus (SLE) without neuropsychiatric manifestations remains a substantial clinical challenge due to the absence of specific biomarkers. Topological data analysis (TDA) is a novel computational technique that enables the visualization, exploration, and analysis of complex data structures. This study aimed to identify distinct neuroimaging biomarkers in patients with NPSLE (NPSLE group) and differentiate them from patients with SLE without neuropsychiatric symptoms (non-NPSLE group) by employing TDA.
We conducted a retrospective cohort study involving 30 patients with NPSLE and 30 without neuropsychiatric symptoms between 2005 and 2020. TDA was utilized to extract topological features, specifically connected components and holes, from fluid-attenuated inversion recovery (FLAIR) sequences obtained via brain magnetic resonance imaging (MRI). Summary statistics, including critical point count, persistence lifetime, centroid coordinates, perimeter, area, and filamentarity, were derived from persistence diagrams.
Multiple logistic regression analyses, adjusted for age, cerebrovascular comorbidities, and 50% hemolytic unit of complement levels, demonstrated a significant association between NPSLE and the perimeter of the holes (odds ratio [OR]: 1.67, 95% confidence interval [CI]: 1.07-2.63, p = 0.025) and the area of the holes (OR: 4.42, 95% CI: 1.35-19.6, p = 0.026) of the identified topological features. Additionally, both areas under the receiver operating characteristic curve (AUC) exceeded 0.8, indicating good diagnostic accuracy.
This study identified novel neuroimaging biomarkers for the diagnosis of NPSLE. The application of TDA to brain MRI features in patients with SLE proved to be a valuable diagnostic tool, particularly through the analysis of persistence diagrams.
由于缺乏特异性生物标志物，诊断神经精神性系统性红斑狼疮（NPSLE）并将其与无神经精神表现的系统性红斑狼疮（SLE）区分开来仍然是一项重大的临床挑战。拓扑数据分析（TDA）是一种新颖的计算技术，能够对复杂数据结构进行可视化、探索和分析。本研究旨在通过运用TDA来识别NPSLE患者（NPSLE组）中独特的神经影像学生物标志物，并将其与无神经精神症状的SLE患者（非NPSLE组）区分开来。
我们进行了一项回顾性队列研究，纳入了2005年至2020年间的30例NPSLE患者和30例无神经精神症状的患者。运用TDA从通过脑磁共振成像（MRI）获得的液体衰减反转恢复（FLAIR）序列中提取拓扑特征，特别是连通分量和空洞。从持久图中得出包括临界点计数、持久寿命、质心坐标、周长、面积和丝状度在内的汇总统计量。
在对年龄、脑血管合并症和补体水平的50%溶血单位进行校正的多因素逻辑回归分析中，显示NPSLE与所识别拓扑特征的空洞周长（优势比[OR]：1.67，95%置信区间[CI]：1.07 - 2.63，p = 0.025）和空洞面积（OR：4.42，95% CI：1.35 - 19.6，p = 0.026）之间存在显著关联。此外，受试者工作特征曲线（AUC）下的面积均超过0.8，表明诊断准确性良好。
本研究识别出了用于诊断NPSLE的新型神经影像学生物标志物。TDA应用于SLE患者的脑MRI特征被证明是一种有价值的诊断工具，特别是通过对持久图的分析。
