March 13, 2026Open Access

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

Q: What does this research mean for the field?

CT image-based machine learning models can predict blood eosinophil levels in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

Puntos clave

The research aims to predict stable eosinophil levels in acute exacerbation of chronic obstructive pulmonary disease using CT images.
Utilized CT images from 482 AECOPD patients for model development and external validation.
Extracted radiomics and quantitative computed tomography features for analysis.
Developed a machine learning model using random forest for feature selection and gradient boosting for classification.
Performed external validation of the model to assess its predictive accuracy.
Achieved 0.734 accuracy and 0.838 area under the curve on Dataset 1.
External validation resulted in 0.624 accuracy and 0.671 area under the curve.
Fused features improved accuracy to 0.786 and area under the curve to 0.843.
External validation of the fused model showed accuracy of 0.673 and area under the curve of 0.697.

Resumen

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.

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