A computed tomography (CT)-derived radiomics approach for predicting primary co-mutations involving and epidermal growth factor receptor () in patients with advanced lung adenocarcinomas (LUAD).

BACKGROUND

Epidermal growth factor receptor () co-mutated with could reduce responsiveness to tyrosine kinase inhibitors (TKIs) and worsen patients' prognosis compared to 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 and 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: & , & , . 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 , 55 (28%) cases of & , 61 (31%) cases of & . Among the four different models, the integrated model displayed the best performance for all the three subtypes of co-mutations: (AUC, 0.857; accuracy, 0.817; sensitivity, 0.998; specificity, 0.663), & (AUC, 0.791; accuracy, 0.758; sensitivity, 0.762; specificity, 0.783), & (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 and . The proposed integrated model may serve as an important alternative marker for preselecting patients who will be adaptable to and sensitive to TKIs.