115563
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
基于临床数据、超声特征和深度学习的痛风综合风险预测模型：一项回顾性多中心研究
Authors Xiao L, Zhao Y, Li Y, Yan M, Liu Y, Li C, Liu M, Ning C
Received 28 May 2025
Accepted for publication 24 December 2025
Published 8 January 2026 Volume 2026:19 543363
DOI https://doi.org/10.2147/JIR.S543363
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Ujjwol Risal
Lishan Xiao,1,* Yizhe Zhao,2,3,* Yuchen Li,1 Mengmeng Yan,1 Yongming Liu,4 Changgui Li,5,6 Manhua Liu,2,3 Chunping Ning1
1Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 2The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 3MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 4Department of Ultrasound, Shandong Province Chronic Disease Hospital, Qingdao, Shandong, People’s Republic of China; 5Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 6Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Chunping Ning, Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email xls715@outlook.com
Purpose: To develop and validate a combined model for predicting gout risk by integrating ultrasound (US) features as novel risk factors with clinical data and predictions from deep learning (DL) models.
Patients and Methods: This retrospective study included 609 cases who underwent first metatarsophalangeal (MTP1) joint US at two centers. Data from Center 1 were divided into a training group (70%, n = 355) and an internal testing cohort (ITC) (30%, n = 162). Data from Center 2 served as an external testing cohort (ETC) (n = 92). A DL diagnostic model based on MTP1 US images was developed to obtain diagnostic predictions. Clinical data, US features, and DL predictions were integrated, and logistic regression analysis was performed to identify independent risk factors. Various models were constructed (clinical, US, clinical-US, clinical-DL, and combined), and the best model was interpreted with a nomogram. Multicollinearity was assessed using the variance inflation factor. Model performance was evaluated using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
Results: The combined model, incorporating clinical data (gender, serum uric acid [SUA]), US features (tophus, double contour sign (DCs), bone erosion), and DL predictions, exhibited the best performance. For the ITC, the area under the curve (AUC) and Brier scores were 0.904 (95% CI: 0.843~0.965) and 0.100 (0.066~0.140), respectively. For the ETC, they were 0.881 (95% CI: 0.815~0.947) and 0.160 (0.107~0.221). DCA confirmed the clinical utility of the combined nomogram.
Conclusion: A nomogram was constructed based on seven risk predictors (gender, SUA, estimated glomerular filtration rate (eGFR), tophus, bone erosion, DCs, and DL prediction) to predict and quantify gout risk.
Plain Language Summary: We developed a new method to predict gout risk by combining clinical data, ultrasound features, and deep learning technology.We created a simple-to-use scoring tool that doctors can use to assess the likelihood of patients developing gout.Three key ultrasound features (tophus, double contour sign, and bone erosion) can help predict gout risk.
Keywords: gout, first metatarsophalangeal joint, ultrasonography, risk assessment, nomogram
1Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 2The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 3MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 4Department of Ultrasound, Shandong Province Chronic Disease Hospital, Qingdao, Shandong, People’s Republic of China; 5Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 6Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Chunping Ning, Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email xls715@outlook.com
Purpose: To develop and validate a combined model for predicting gout risk by integrating ultrasound (US) features as novel risk factors with clinical data and predictions from deep learning (DL) models.
Patients and Methods: This retrospective study included 609 cases who underwent first metatarsophalangeal (MTP1) joint US at two centers. Data from Center 1 were divided into a training group (70%, n = 355) and an internal testing cohort (ITC) (30%, n = 162). Data from Center 2 served as an external testing cohort (ETC) (n = 92). A DL diagnostic model based on MTP1 US images was developed to obtain diagnostic predictions. Clinical data, US features, and DL predictions were integrated, and logistic regression analysis was performed to identify independent risk factors. Various models were constructed (clinical, US, clinical-US, clinical-DL, and combined), and the best model was interpreted with a nomogram. Multicollinearity was assessed using the variance inflation factor. Model performance was evaluated using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
Results: The combined model, incorporating clinical data (gender, serum uric acid [SUA]), US features (tophus, double contour sign (DCs), bone erosion), and DL predictions, exhibited the best performance. For the ITC, the area under the curve (AUC) and Brier scores were 0.904 (95% CI: 0.843~0.965) and 0.100 (0.066~0.140), respectively. For the ETC, they were 0.881 (95% CI: 0.815~0.947) and 0.160 (0.107~0.221). DCA confirmed the clinical utility of the combined nomogram.
Conclusion: A nomogram was constructed based on seven risk predictors (gender, SUA, estimated glomerular filtration rate (eGFR), tophus, bone erosion, DCs, and DL prediction) to predict and quantify gout risk.
Plain Language Summary: We developed a new method to predict gout risk by combining clinical data, ultrasound features, and deep learning technology.We created a simple-to-use scoring tool that doctors can use to assess the likelihood of patients developing gout.Three key ultrasound features (tophus, double contour sign, and bone erosion) can help predict gout risk.
Keywords: gout, first metatarsophalangeal joint, ultrasonography, risk assessment, nomogram