109814
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
利用磁共振成像（MRI）影像组学和临床特征预测代谢综合征患者重症急性胰腺炎
Authors Wang Y, Wan X, Zhang Y, Liu Z, Liu Z, Tang M, Huang X
Received 11 May 2025
Accepted for publication 11 November 2025
Published 15 November 2025 Volume 2025:18 Pages 15959—15971
DOI https://doi.org/10.2147/JIR.S539688
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Ning Quan
Yuan Wang,* Xiyao Wan,* Yawen Zhang, Ziyan Liu, Ziyi Liu, Mengyue Tang, Xiaohua Huang
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaohua Huang, Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, Sichuan, 637000, People’s Republic of China, Tel +86150-8279-7553, Email 15082797553@163.com
Background: Early identification of severe acute pancreatitis (SAP) in patients with metabolic syndrome (MetS) is crucial for improving prognosis and guiding timely intervention. Conventional scoring systems such as the bedside index for severity in acute pancreatitis (BISAP) and magnetic resonance severity index (MRSI) show limited accuracy for early prediction. MRI–based radiomics offers a noninvasive approach to quantify subtle image features that may reflect underlying disease heterogeneity. Integrating radiomics with clinical indicators may enhance prediction of SAP progression in MetS patients.
Purpose: To develop and validate a predictive model combining MRI T2WI radiomics and clinical features to predict SAP occurrence in MetS patients.
Patients and Methods: This retrospective study included 188 patients with acute pancreatitis (AP) and MetS, classified into severe (31 patients) and non-severe (157 patients) groups according to the 2012 revised Atlanta consensus. Regions of interest were delineated using 3D Slicer, and radiomics features were extracted via PyRadiomics. Features were normalized and selected using select K-best and least absolute shrinkage and selection operator (LASSO). A random forest classifier constructed the radiomics model, while binary logistic regression identified independent clinical predictors to form a combined model. Model performance and clinical utility were evaluated using the area under the curve (AUC), the DeLong test, and decision curve analysis (DCA).
Results: Seven radiomics features were selected following dimensionality reduction. Binary logistic regression identified length of hospital stay and serum calcium as independent clinical risk factors. The combined model achieved AUCs of 0.97 and 0.979 in training and test sets, respectively, outperforming the clinical, radiomics, BISAP, and MRSI models.
Conclusion: The combined model integrating MRI T2WI radiomics with clinical features provides robust and clinically valuable prediction of SAP in MetS patients, supporting its potential value for early clinical intervention.
Keywords: acute pancreatitis, severity, metabolic syndrome, radiomics, magnetic resonance imaging
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaohua Huang, Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, Sichuan, 637000, People’s Republic of China, Tel +86150-8279-7553, Email 15082797553@163.com
Background: Early identification of severe acute pancreatitis (SAP) in patients with metabolic syndrome (MetS) is crucial for improving prognosis and guiding timely intervention. Conventional scoring systems such as the bedside index for severity in acute pancreatitis (BISAP) and magnetic resonance severity index (MRSI) show limited accuracy for early prediction. MRI–based radiomics offers a noninvasive approach to quantify subtle image features that may reflect underlying disease heterogeneity. Integrating radiomics with clinical indicators may enhance prediction of SAP progression in MetS patients.
Purpose: To develop and validate a predictive model combining MRI T2WI radiomics and clinical features to predict SAP occurrence in MetS patients.
Patients and Methods: This retrospective study included 188 patients with acute pancreatitis (AP) and MetS, classified into severe (31 patients) and non-severe (157 patients) groups according to the 2012 revised Atlanta consensus. Regions of interest were delineated using 3D Slicer, and radiomics features were extracted via PyRadiomics. Features were normalized and selected using select K-best and least absolute shrinkage and selection operator (LASSO). A random forest classifier constructed the radiomics model, while binary logistic regression identified independent clinical predictors to form a combined model. Model performance and clinical utility were evaluated using the area under the curve (AUC), the DeLong test, and decision curve analysis (DCA).
Results: Seven radiomics features were selected following dimensionality reduction. Binary logistic regression identified length of hospital stay and serum calcium as independent clinical risk factors. The combined model achieved AUCs of 0.97 and 0.979 in training and test sets, respectively, outperforming the clinical, radiomics, BISAP, and MRSI models.
Conclusion: The combined model integrating MRI T2WI radiomics with clinical features provides robust and clinically valuable prediction of SAP in MetS patients, supporting its potential value for early clinical intervention.
Keywords: acute pancreatitis, severity, metabolic syndrome, radiomics, magnetic resonance imaging