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<p>To develop and validate an interpretable HRCT radiomics-based machine learning model for early risk stratification and identification of invasive fungal infection (IFI) in patients with community-acquired pneumonia (CAP). A total of 570 CAP patients who underwent HRCT from July 2022 to August 2024 in Center 1 and Center 2 were recruited. A VB-net pneumonia automatic segmentation algorithm was employed. Three models, a radiomics model (HRCT-derived radiomics features), a clinical model (clinical variables), and a combined model (integrating both), were developed. The performance of the model was evaluated through receiver operating characteristic analysis with respect to the area under the curve (AUC). Clinical utility was evaluated by using decision curve analysis. The Shapley Additive Explanations tool was employed. A total of&#xa0;239 (mean age: 62.1 ± 19.3 years; 134 male), 101 (mean age: 57.5 ± 17.3 years; 44 male), and 230 (mean age: 68.4 ± 15.3 years; 153 male) patients were included in the training, internal validation, and external validation datasets, respectively. Based on the linear discriminant analysis classifier, the AUCs of the clinical, radiomics, and combined models were 0.719, 0.724, and 0.808, respectively, in the internal validation dataset; and 0.707, 0.709, and 0.786, respectively, in the external validation dataset. The combined model yielded a superior net benefit relative to both the clinical and radiomics models alone. Age exerted the greatest influence on the predictions of the combined model, while the top three influential radiomics features were all high-order texture features. A radiomics-based machine learning model provides moderate incremental value as an adjunctive tool for early IFI risk stratification in patients with CAP before definitive microbiological or pathological confirmation, with favorable interpretability.</p>

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An interpretable radiomics–machine learning model for early risk stratification of invasive fungal infections in community-acquired pneumonia: a dual-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月14日

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  • Wenzhang He,
  • Yulin Xiong,
  • Xuan Huang,
  • Haoran Luo,
  • Shaoquan Zhou,
  • Yongbo Tu,
  • Tie Deng,
  • Min Gu

摘要

To develop and validate an interpretable HRCT radiomics-based machine learning model for early risk stratification and identification of invasive fungal infection (IFI) in patients with community-acquired pneumonia (CAP). A total of 570 CAP patients who underwent HRCT from July 2022 to August 2024 in Center 1 and Center 2 were recruited. A VB-net pneumonia automatic segmentation algorithm was employed. Three models, a radiomics model (HRCT-derived radiomics features), a clinical model (clinical variables), and a combined model (integrating both), were developed. The performance of the model was evaluated through receiver operating characteristic analysis with respect to the area under the curve (AUC). Clinical utility was evaluated by using decision curve analysis. The Shapley Additive Explanations tool was employed. A total of 239 (mean age: 62.1 ± 19.3 years; 134 male), 101 (mean age: 57.5 ± 17.3 years; 44 male), and 230 (mean age: 68.4 ± 15.3 years; 153 male) patients were included in the training, internal validation, and external validation datasets, respectively. Based on the linear discriminant analysis classifier, the AUCs of the clinical, radiomics, and combined models were 0.719, 0.724, and 0.808, respectively, in the internal validation dataset; and 0.707, 0.709, and 0.786, respectively, in the external validation dataset. The combined model yielded a superior net benefit relative to both the clinical and radiomics models alone. Age exerted the greatest influence on the predictions of the combined model, while the top three influential radiomics features were all high-order texture features. A radiomics-based machine learning model provides moderate incremental value as an adjunctive tool for early IFI risk stratification in patients with CAP before definitive microbiological or pathological confirmation, with favorable interpretability.