Abstract
BACKGROUND: Epidermal growth factor receptor (EGFR) co-mutated with TP53 could reduce responsiveness to tyrosine kinase inhibitors (TKIs) and worsen patients' prognosis compared to TP53 wild type patients in. EGFR: mutated lung adenocarcinomas (LUAD). To identify this genetically unique subset prior to treatment through computed tomography (CT) images had not been reported yet. METHODS: Stage III and IV LUAD with known mutation status of EGFR and TP53 from The First Affiliated Hospital of Sun Yat-sen University (May 1, 2017 to June 1, 2020) were collected. Characteristics of pretreatment enhanced-CT images were analyzed. One-versus-one was used as the multiclass classification strategy to distinguish the three subtypes of co-mutations: EGFR (+) & TP53 (+), EGFR (+) & TP53 (-), EGFR (-). The clinical model, semantic model, radiomics model and integrated model were built. Area under the receiver-operating characteristic curves (AUCs) were used to evaluate the prediction efficacy. RESULTS: A total of 199 patients were enrolled, including 83 (42%) cases of EGFR (-), 55 (28%) cases of EGFR (+) & TP53 (+), 61 (31%) cases of EGFR (+) & TP53 (-). Among the four different models, the integrated model displayed the best performance for all the three subtypes of co-mutations: EGFR (-) (AUC, 0.857; accuracy, 0.817; sensitivity, 0.998; specificity, 0.663), EGFR (+) & TP53 (+) (AUC, 0.791; accuracy, 0.758; sensitivity, 0.762; specificity, 0.783), EGFR (+) & TP53 (-) (AUC, 0.761; accuracy, 0.813; sensitivity, 0.594; specificity, 0.977). The radiomics model was slightly inferior to the integrated model. The results for the clinical and the semantic models were dissatisfactory, with AUCs less than 0.700 for all the three subtypes. CONCLUSIONS: CT imaging based artificial intelligence (AI) is expected to distinguish co-mutation status involving TP53 and EGFR. The proposed integrated model may serve as an important alternative marker for preselecting patients who will be adaptable to and sensitive to TKIs.