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(AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA) was used in this study. We trained and validated our deep learning system on 523 nodules and tested its performance on 128 nodules. An observer study with 2 groups of radiologists, 2 senior and 2 junior, was also investigated. We merged AAH and AIS into one single category AAH-AIS, comprising a 3-category classification in our study. The proposed deep learning system achieved better classification performance than the radiologists; in terms of 3-class weighted average F1 score, the model achieved 63.3% while the radiologists achieved 55.6%, 56.6%, 54.3%, and 51.0%, respectively. These results suggest that deep learning methods improve the yield of discriminative results and hold promise in the CADx application domain, which could help doctors work efficiently and facilitate the application of precision medicine. SIGNIFICANCE: Machine learning tools are beginning to be implemented for clinical applications. This study represents an important milestone for this emerging technology, which could improve therapy selection for patients with lung cancer.
Zhao et al. (Tue,) studied this question.