CT image-based machine learning models for predicting blood eosinophil levels in acute exacerbation of chronic obstructive pulmonary disease

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is characterized by a significant worsening of respiratory symptoms. Blood eosinophil levels are a key predictor of glucocorticoid efficacy in AECOPD patients; however, their stability can present challenges. Predicting stable eosinophil levels from CT images is essential for optimal patient management. This study utilized CT images from 482 AECOPD patients across two hospitals. Dataset 1 comprised 193 patients for model development, while Dataset 2 included 289 patients for external validation. A threshold of 2% eosinophil was used to differentiate between high and low eosinophil levels. A machine learning model was developed to predict eosinophil levels using CT radiomics and quantitative computed tomography (QCT) features. Radiomics features were extracted, and feature selection was performed using random forest (RF) algorithms. Segmentation of pulmonary lobes, airways, and blood vessels yielded 20 QCT features. A Gradient Boosting (GB) classifier was then trained on the fused features. The GB classifier with radiomics features demonstrated strong performance, achieving an accuracy (ACC) of 0.734 and an area under the curve (AUC) of 0.838 on the test set of Dataset 1. In external validation, the ACC and AUC were 0.624 and 0.671, respectively. After fusing QCT features, the ACC and AUC improved to 0.786 and 0.843, respectively, with external validation results of 0.673 and 0.697. The CT image-based machine learning model can predict blood eosinophil levels in AECOPD patients, providing a noninvasive and stable assessment. It has potential for future clinical application following further validation and external testing.