Objectives: To develop a high-resolution computed tomography (HRCT) radiomics-based interpretable machine learning model for diagnosing invasive fungal infection (IFI) in community-acquired pneumonia (CAP) patient.

Methods: 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 these models 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 Explanation tool was employed.

Results: 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. Based on 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 three most important radiomics features were all higher-order texture features.

Conclusions: A radiomics-based machine learning model can effectively diagnose IFI in CAP patients, demonstrating favorable interpretability.