Quantitative Analysis of TP53-Related Lung Cancer Based on Radiomics

BACKGROUND: The role of TP53 mutations in the diagnosis and treatment of lung cancer has attracted increasing attention from experts worldwide. This study aimed to explore the expression of TP53 gene in lung cancer and its correlation with radiomics quantitative features. METHODS: A total of 93 cases of lung cancer confirmed by pathology were selected, including 44 cases with TP53 mutations and 49 cases with TP53 wild-type. ITK-SNAP software was used to segment the pulmonary nodules, AK software was used to extract radiomic features, and a model was established to predict the type of TP53 gene mutation in lung cancer lesions. RESULTS: A total of 852 features were extracted, and 10 features remained after feature selection. The accuracy, areas under the curve, specificity, sensitivity, positive predictive value, and negative predictive value of the logistic regression model were 0.80, 0.86, 0.89, 0.74, 0.90, and 0.71, respectively. CONCLUSION: TP53 gene mutations are correlated with radiomic features in lung cancer, which may have application value for TP53 therapy in the future.