Han Rui, Hou Juan, Xia Ping, Xing Yan, Liu Wenya
Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Liyushan South Road 137, Urumqi, 830011, China.
Dermatology Unit, Wuhan No.1 Hospital, Wuhan, China.
BMC Med Imaging. 2025 Jul 1;25(1):240. doi: 10.1186/s12880-025-01755-5.
Psoriasis patients frequently present with cardiovascular comorbidities, which maybe associated with abnormal epicardial adipose tissue (EAT). This study aimed to evaluate the predictive value of radiomics features derived from non-contrast chest CT (NCCT) combined with serological parameters for identifying abnormal EAT in psoriasis.
In this retrospective case-control study, we enrolled consecutive psoriasis patients who underwent chest NCCT between September 2021 and February 2024, along with a matched healthy control group. Psoriasis patients were stratified into mild-to-moderate (PASI ≤ 10) and severe (PASI > 10) groups based on the Psoriasis Area and Severity Index (PASI). Using TIMESlice, we extracted EAT volume, CT values, and 86 radiomics features. The cohort was randomly divided into a training (70%) and test (30%) set. LASSO regression selected radiomic features to calculate the Rad_Score. Serum uric acid (UA) and C-reactive protein (CRP) levels were collected. We compared EAT volume, CT values, Rad_Score, UA, and CRP between groups and developed three models: Model A (UA, CRP, EAT CT values), Model B (Rad_Score), and Model C (UA, CRP, EAT CT values, Rad_Score). Model accuracy was evaluated using ROC curves (P < 0.05).
The study included 77 psoriasis patients and 76 matched controls. Psoriasis patients had higher UA and CRP levels than controls (both P < 0.001). EAT CT value was higher in psoriasis (P = 0.020), with no volume difference. Eight radiomics features and Rad_Score significantly differed between groups (P < 0.001), and Rad_Score also higher in severe group than that in mild-to-moderate group (P < 0.001). Model C showed the highest AUC in both sets: training 0.947 and test 0.895, indicating superior predictive performance.
Combining radiomics features, EAT CT values, UA, and CRP in a predictive model accurately predicts EAT abnormalities in psoriasis, potentially improving cardiovascular comorbidity diagnosis.
Not applicable.
银屑病患者常伴有心血管合并症，这可能与心外膜脂肪组织（EAT）异常有关。本研究旨在评估非增强胸部CT（NCCT）衍生的影像组学特征结合血清学参数对识别银屑病患者EAT异常的预测价值。
在这项回顾性病例对照研究中，我们纳入了2021年9月至2024年2月期间连续接受胸部NCCT检查的银屑病患者以及匹配的健康对照组。根据银屑病面积和严重程度指数（PASI），将银屑病患者分为轻度至中度（PASI≤10）和重度（PASI>10）组。使用TIMESlice软件，我们提取了EAT体积、CT值和86个影像组学特征。该队列被随机分为训练集（70%）和测试集（30%）。通过LASSO回归选择影像组学特征来计算Rad_Score。收集血清尿酸（UA）和C反应蛋白（CRP）水平。我们比较了各组之间的EAT体积、CT值、Rad_Score、UA和CRP，并建立了三个模型：模型A（UA、CRP、EAT CT值）、模型B（Rad_Score）和模型C（UA、CRP、EAT CT值、Rad_Score）。使用ROC曲线评估模型准确性（P<0.05）。
该研究纳入了77例银屑病患者和76例匹配的对照组。银屑病患者的UA和CRP水平高于对照组（均P<0.001）。银屑病患者的EAT CT值较高（P=0.020），但体积无差异。8个影像组学特征和Rad_Score在各组之间存在显著差异（P<0.001），且重度组的Rad_Score也高于轻度至中度组（P<0.001）。模型C在两个数据集中的AUC最高：训练集为0.947，测试集为0.895，表明其具有卓越的预测性能。
在预测模型中结合影像组学特征、EAT CT值、UA和CRP能够准确预测银屑病患者的EAT异常，可能改善心血管合并症的诊断。
不适用。
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