Developing Predictive Models for Classifying Infection vs. Malignancy in PET/CT Imaging of Lung, Head and Neck, and Breast Cancer

This study aims to develop a hybrid predictive model to differentiate infection from malignancy using clinical data, radiomic features, and PET CT metabolic parameters. A comprehensive dataset will be collected, preprocessed, and analyzed. Machine learning methods will be used to identify key features and build a diagnostic model. The performance of the model will be validated and its ability to improve diagnostic accuracy and clinical decision making will be assessed across lung, breast, and head and neck cancer cases.