AUTHOR=Yan Mengmeng , Wang Weidong 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.555514

DOI=10.3389/fonc.2020.555514

ISSN=2234-943X

ABSTRACT=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.