{"title":"通过整合非对比胸部CT放射组学与血清学生物标志物预测银屑病患者心外膜脂肪组织异常。","authors":"Rui Han, Juan Hou, Ping Xia, Yan Xing, Wenya Liu","doi":"10.1186/s12880-025-01755-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>Combining radiomics features, EAT CT values, UA, and CRP in a predictive model accurately predicts EAT abnormalities in psoriasis, potentially improving cardiovascular comorbidity diagnosis.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"240"},"PeriodicalIF":2.9000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting abnormal epicardial adipose tissue in psoriasis patients by integrating radiomics from non-contrast chest CT with serological biomarkers.\",\"authors\":\"Rui Han, Juan Hou, Ping Xia, Yan Xing, Wenya Liu\",\"doi\":\"10.1186/s12880-025-01755-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>Combining radiomics features, EAT CT values, UA, and CRP in a predictive model accurately predicts EAT abnormalities in psoriasis, potentially improving cardiovascular comorbidity diagnosis.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>\",\"PeriodicalId\":9020,\"journal\":{\"name\":\"BMC Medical Imaging\",\"volume\":\"25 1\",\"pages\":\"240\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12880-025-01755-5\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01755-5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Predicting abnormal epicardial adipose tissue in psoriasis patients by integrating radiomics from non-contrast chest CT with serological biomarkers.
Background: 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.
Methods: 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).
Results: 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.
Conclusions: Combining radiomics features, EAT CT values, UA, and CRP in a predictive model accurately predicts EAT abnormalities in psoriasis, potentially improving cardiovascular comorbidity diagnosis.
期刊介绍：
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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