Detection of epithelial growth factor receptor () mutations on CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks.

BACKGROUND

We aim to analyze the ability to detect epithelial growth factor receptor () mutations on chest CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks (MCNNs).

METHODS

We retrospectively collected 1,010 consecutive patients in Shanghai Chest Hospital from 2013 to 2017, among which 510 patients were -mutated and 500 patients were wild-type. The patients were randomly divided into a training set (810 patients) and a validation set (200 patients) according to a balanced distribution of clinical features. The CT images and the corresponding EGFR status measured by Amplification Refractory Mutation System (ARMS) method of the patients in the training set were utilized to construct both a radiomics-based model (M) and MCNNs-based model (M). The M and M were combined to build the Model (M). Clinical data of gender and smoking history constructed the clinical features-based model (M). M was then added into M, M, and M to establish the Model (M), the Model (M) and the Model (M). All the seven models were tested in the validation set to ascertain whether they were competent to detect mutations. The detection efficiency of each model was also compared in terms of area under the curve (AUC), sensitivity and specificity.

RESULTS

The AUC of the M, M and M to predict mutations was 0.740, 0.810 and 0.811 respectively. The performance of M was better than that of M (P=0.0225). The addition of clinical features did not improve the AUC of the M (P=0.623), the M (P=0.114) and the M (P=0.058). The M demonstrated the highest AUC value of 0.834. The M did not demonstrate any inferiority when compared with the M (P=0.742) and the M (P=0.056).

CONCLUSIONS

Both of the M and the M could predict mutations on CT images of patients with lung adenocarcinoma. The M outperformed the M in the detection of mutations. The combination of these two models, even added with clinical features, is not significantly more efficient than M alone.