Scholarlink

Objectives <p>Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is primarily classified into usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP) patterns, which exhibit significant differences in prognosis and treatment response. This study aimed to differentiate UIP and NSIP patterns in RA-ILD by integrating HRCT-based radiomics with plasma metabolomics.</p> Methods <p>We included 350 RA-ILD patients who were assigned to training, internal validation, and external validation sets. Radiomics features were extracted from HRCT images, and optimal features were selected using variance threshold, univariate selection, and LASSO regression. Three machine learning algorithms—random forest (RF), logistic regression (LR), and multi-layer perceptron (MLP)—were each used to construct three categories of models: clinical models, radiomics models, and combined clinical–radiomics models. This design resulted in a total of 9 models (3 algorithms × 3 model categories). A nomogram integrating the radiomics score (Rad-score) and clinical variables was developed. Model performance was assessed via AUC, calibration curves, and decision curve analysis (DCA). Plasma metabolomics analysis was performed using liquid chromatography–tandem mass spectrometry, and Spearman correlation was used to explore the relationship between radiomics features and metabolites.</p> Results <p>Twenty-five optimal radiomics features were selected, and 77 metabolites were significantly correlated with radiomics features (<i>R</i> &gt; 0.4, <i>P</i> &lt; 0.05). <span>L</span>-Glutamate and alpha-ketoglutarate were co-enriched in key metabolic pathways (e.g., arginine biosynthesis, <span>D</span>-glutamine/<span>D</span>-glutamate metabolism). Radiomics and combined models outperformed clinical models: the MLP combined model showed more favorable discrimination in the training set (AUC = 0.915; 95% CI 0.884–0.940) and internal validation set (AUC = 0.828; 95% CI 0.728–0.916), while the RF combined model showed the best discrimination in the external validation set (AUC = 0.841; 95% CI 0.729–0.916, specificity = 67%). The nomogram exhibited excellent calibration (Hosmer–Lemeshow: <i>P</i> = 0.051, 0.382, 0.073 for training, internal, external sets) and clinical utility (DCA: high net benefits across thresholds).</p> Conclusions <p>The combined clinical–radiomics model is a promising auxiliary tool for RA-ILD subtype differentiation. Preliminary data suggest it may aid early, more accurate identification to inform clinical decision-making. Moreover, the Rad-score enhances subtype discrimination, and <span>L</span>-glutamate/alpha-ketoglutarate merit further multi-center validation as potential metabolic markers.</p> <p><i>Chinese Clinical Trial Registry</i>: <a href="http://www.chictr.org.cn">http://www.chictr.org.cn</a> (ChiCTR2400084726).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Combining radiomics based on high-resolution computed tomography with plasma metabolomics for diagnosing subtypes of rheumatoid arthritis-associated interstitial lung disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月18日

查看全文
  • Hongya Liu,
  • Jianghua Chen,
  • Jie Zhu,
  • Dandan Wu,
  • Kui Du,
  • Fanxin Zeng,
  • Qinghua Zou

摘要

Objectives

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is primarily classified into usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP) patterns, which exhibit significant differences in prognosis and treatment response. This study aimed to differentiate UIP and NSIP patterns in RA-ILD by integrating HRCT-based radiomics with plasma metabolomics.

Methods

We included 350 RA-ILD patients who were assigned to training, internal validation, and external validation sets. Radiomics features were extracted from HRCT images, and optimal features were selected using variance threshold, univariate selection, and LASSO regression. Three machine learning algorithms—random forest (RF), logistic regression (LR), and multi-layer perceptron (MLP)—were each used to construct three categories of models: clinical models, radiomics models, and combined clinical–radiomics models. This design resulted in a total of 9 models (3 algorithms × 3 model categories). A nomogram integrating the radiomics score (Rad-score) and clinical variables was developed. Model performance was assessed via AUC, calibration curves, and decision curve analysis (DCA). Plasma metabolomics analysis was performed using liquid chromatography–tandem mass spectrometry, and Spearman correlation was used to explore the relationship between radiomics features and metabolites.

Results

Twenty-five optimal radiomics features were selected, and 77 metabolites were significantly correlated with radiomics features (R > 0.4, P < 0.05). L-Glutamate and alpha-ketoglutarate were co-enriched in key metabolic pathways (e.g., arginine biosynthesis, D-glutamine/D-glutamate metabolism). Radiomics and combined models outperformed clinical models: the MLP combined model showed more favorable discrimination in the training set (AUC = 0.915; 95% CI 0.884–0.940) and internal validation set (AUC = 0.828; 95% CI 0.728–0.916), while the RF combined model showed the best discrimination in the external validation set (AUC = 0.841; 95% CI 0.729–0.916, specificity = 67%). The nomogram exhibited excellent calibration (Hosmer–Lemeshow: P = 0.051, 0.382, 0.073 for training, internal, external sets) and clinical utility (DCA: high net benefits across thresholds).

Conclusions

The combined clinical–radiomics model is a promising auxiliary tool for RA-ILD subtype differentiation. Preliminary data suggest it may aid early, more accurate identification to inform clinical decision-making. Moreover, the Rad-score enhances subtype discrimination, and L-glutamate/alpha-ketoglutarate merit further multi-center validation as potential metabolic markers.

Chinese Clinical Trial Registry: http://www.chictr.org.cn (ChiCTR2400084726).