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<p>Radiomics is increasingly utilized in medical image analysis. This study evaluates the use of infrared thermography, a technique well-suited for radiomic analysis, in diagnosing metabolic syndrome (MS). Facial and palmar thermographs from 200 males (100 healthy controls and 100 MS patients) were analyzed. The dataset was split into a training cohort (<i>n</i> = 140) and a validation cohort (<i>n</i> = 60). All participants underwent laboratory testing on the same day as infrared thermography imaging. A total of 1656 radiomic features were extracted from each participant’s thermographs and refined using Pearson correlation coefficients, two-sample t-tests, and LASSO regression. A binary random forest (RF) classification model was then constructed and evaluated based on its calibration, discrimination, and clinical utility. The RF model demonstrated strong diagnostic performance, achieving an AUC of 0.91 in the validation cohort. Calibration and decision curve analyses confirmed the model’s clinical applicability. Infrared thermography-based radiomics offers a promising, non-invasive method for early screening of MS, highlighting its potential clinical utility.</p>

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Infrared thermography-based radiomics for early detection of metabolic syndrome

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月22日

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  • Jiayang Guo,
  • Huizhong Xue,
  • Yu Chen,
  • Xiaoran Li,
  • Yiyun Chen,
  • Xianhui Zhang,
  • Yanhong An,
  • Hua Zhang,
  • Yimeng Yang,
  • Luqi Cai,
  • Wenzheng Zhang,
  • Yonghua Xiao

摘要

Radiomics is increasingly utilized in medical image analysis. This study evaluates the use of infrared thermography, a technique well-suited for radiomic analysis, in diagnosing metabolic syndrome (MS). Facial and palmar thermographs from 200 males (100 healthy controls and 100 MS patients) were analyzed. The dataset was split into a training cohort (n = 140) and a validation cohort (n = 60). All participants underwent laboratory testing on the same day as infrared thermography imaging. A total of 1656 radiomic features were extracted from each participant’s thermographs and refined using Pearson correlation coefficients, two-sample t-tests, and LASSO regression. A binary random forest (RF) classification model was then constructed and evaluated based on its calibration, discrimination, and clinical utility. The RF model demonstrated strong diagnostic performance, achieving an AUC of 0.91 in the validation cohort. Calibration and decision curve analyses confirmed the model’s clinical applicability. Infrared thermography-based radiomics offers a promising, non-invasive method for early screening of MS, highlighting its potential clinical utility.