Original Article   

The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2022 Apr 14

DOI: 10.23736/S1824-4785.22.03441-0

Copyright © 2022 EDIZIONI MINERVA MEDICA

lingua: Inglese

Efficient 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer

Dan RUAN ✉, Janyao FANG, Xinyu TENG

Department of Nuclear Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Fujian, China

BACKGROUND: Beyond the human eye's limitations, radiomics provides more information that can be used for diagnosis. We develop a personalized and efficient model based on 18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET/CT) to predict epidermal growth factor receptor (EGFR) mutations to help identify which non-small cell cancer (NSCLC) patients are candidates for EGFR-tyrosine kinase inhibitors (TKIs) therapy.
METHODS: We retrospectively included 100 patients with NSCLC and randomized them according to 70 patients in the training group and 30 patients in the validation group. The least absolute shrinkage and selection operator logistic regression (LLR) algorithm and Support Vector Machine (SVM) classifier were used to build the models and predict whether EGFR is mutated or not. The predictive efficacy of the LLR algorithm-based model and the SVM classifier-based model was evaluated by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC).
RESULTS: The AUC, sensitivity and specificity of our radiomics model by LLR algorithm were 0.792, 0.967, and 0.600 for the training group and 0.643, 1.00, and 0.378 for the validation group, respectively, in predicting EGFR mutations. The AUC was 0.838 for the training group and 0.696 for the validation group after combining radiomics features with clinical features. The prediction results based on the SVM classifier showed that the validation group had the best performance when based on radial kernel function with AUC, sensitivity, and specificity of 0.741, 0.667, and 0.825, respectively.
CONCLUSIONS: Radiomics models based on 18F-FDG PET/CT modeled with different machine learning algorithms can improve the predictive efficacy of the models. Models that combine clinical features are more clinically valuable.

KEY WORDS: Positron Emission Tomography/Computed Tomography; Machine learning; Radiomics; NSCLC; EGFR mutation