Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Purpose: To develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET Methods：Machine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases(Mts), adenocarcinoma (Adc), and squamous cell carcinoma(Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership filter method. The performance metrics of the four class classification model include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics. Results: The combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR four-class classification model showed 91% Acc and 0.89 Kappa. Conclusion: The proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.