Deep learning analysis to predict mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT.

BACKGROUND

Epidermal growth factor receptor-tyrosine kinase inhibitors (-TKIs) showed potency as a non-invasive therapeutic approach in pure ground-glass opacity nodule (pGGN) lung adenocarcinoma. However, optimal methods of extracting information about mutation from pGGN lung adenocarcinoma images remain uncertain. We aimed to develop, validate, and evaluate the clinical utility of a deep learning model for predicting mutation status in lung adenocarcinoma manifesting as pGGN on computed tomography (CT).

METHODS

We included 185 resected pGGN lung adenocarcinomas in the primary cohort. The patients were divided into training (n = 125), validation (n = 23), and test sets (n = 37). A preoperative CT-based deep learning model with clinical factors as well as clinical and radiomics models was constructed and applied to the test set. We evaluated the clinical utility of the deep learning model by applying it to 83 GGNs that received -TKI from an independent cohort (clinical validation set), and treatment response was regarded as the reference standard.

RESULTS

The prediction efficiencies of each model were compared in terms of area under the curve (AUC). Among the 185 pGGN lung adenocarcinomas, 122 (65.9%) were -mutant and 63 (34.1%) were -wild type. The AUC of the clinical, radiomics, and deep learning with clinical models to predict mutations were 0.50, 0.64, and 0.85, respectively, for the test set. The AUC of deep learning with the clinical model in the validation set was 0.72.

CONCLUSIONS

Deep learning approach of CT images combined with clinical factors can predict mutations in patients with lung adenocarcinomas manifesting as pGGN, and its clinical utility was demonstrated in a real-world sample.