Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational...

Area of Science:

• Pulmonary Medicine
• Biomarker Discovery
• Artificial Intelligence in Healthcare

Background:

• Pulmonary nodule detection is improving with imaging, but malignancy assessment often requires invasive methods or radiation.
• There is a need for safer, noninvasive alternatives to diagnose pulmonary nodules.
• Exhaled volatile organic compounds (VOCs) show potential, but their role in assessing pulmonary nodule malignancy is underexplored.

Purpose of the Study:

• To investigate the effectiveness of exhaled VOCs and machine learning (ML) in assessing pulmonary nodule malignancy.
• To develop and evaluate noninvasive predictive models for pulmonary nodule malignancy.

Main Methods:

• A prospective study enrolled 267 participants, collecting exhaled breath samples, lifestyle data, and health examination data.
• Five ML algorithms were applied to develop predictive models for pulmonary nodule malignancy.
• Models were evaluated using metrics like AUC, sensitivity, and specificity, with a focus on moderate-risk nodules.

Main Results:

• 11 exhaled VOCs, along with lifestyle factors and nodule diameter, were identified as independent predictors of malignancy in moderate-risk nodules.
• A random forest model using VOCs achieved an AUC of 0.99, outperforming models using only lifestyle and clinical data (AUC 0.91).
• Calibration curves and decision curve analysis confirmed the models' accuracy and clinical utility, with a developed nomogram for practical application.

Conclusions:

• Integrating ML algorithms with exhaled biomarkers and clinical data offers a robust, noninvasive framework for pulmonary nodule assessment.
• These models present a safer alternative to traditional methods, potentially improving early detection and management.
• Larger, multicenter studies are recommended to validate and generalize these findings.