Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules

Background: Clinical management of subsolid nodules (SSNs) is defined by the suspicion of tumor invasiveness. We sought to develop an artificial intelligent (AI) algorithm for invasiveness assessment of lung adenocarcinoma manifesting as radiological SSNs. We investigated the performance of this algorithm in classification of SSNs related to invasiveness. Methods: A retrospective chest computed tomography (CT) dataset of 1,589 SSNs was constructed to develop (85%) and internally test (15%) the proposed AI diagnostic tool, SSNet. Diagnostic performance was evaluated in the hold-out test set and was further tested in an external cohort of 102 SSNs. Three thoracic surgeons and three radiologists were required to evaluate the invasiveness of SSNs on both test datasets to investigate the clinical utility of the proposed SSNet. Results: In the differentiation of invasive adenocarcinoma (IA), SSNet achieved a similar area under the curve [AUC; 0.914, 95% confidence interval (CI): 0.813-0.987] with that of the 6 doctors (0.900, 95% CI: 0.867-0.922). When interpreting with the assistance of SSNet, the sensitivity of junior doctors, specificity of senior doctor, and their accuracy were significantly improved. In the external test, SSNet (AUC: 0.949, 95% CI: 0.884-1.000) achieved a better AUC than doctors (AUC: 0.883, 95% CI: 0.826-0.939) whose AUC increased (AUC: 0.908: 95% CI: 0.847-0.982) with SSNet assistance. In the histological subtype classifications, SSNet achieved better performance than practicing doctors. The AUCs of doctors were significantly improved with the assistance of SSNet in both 4-category and 3-category classifications to 0.836 (95% CI: 0.811-0.862) and 0.852 (95% CI: 0.825-0.882), respectively. Conclusions: The AI diagnostic system achieved non-inferior performance to doctors, and will potentially improve diagnostic performance and efficiency in SSN evaluation.

Automated summary

The AI diagnostic system SSNet showed comparable performance to doctors in differentiating invasive adenocarcinoma, improving sensitivity for junior doctors and specificity for senior doctors. In the external test, SSNet outperformed doctors in AUC, with doctors' AUC improving with SSNet assistance. Furthermore, SSNet achieved better performance than doctors in histological subtype classifications, enhancing the overall diagnostic efficiency in evaluating SSNs.