Diagnostic accuracy of deep learning for the invasiveness assessment of ground-glass nodules with fine segmentation: a systematic review and meta-analysis

Abstract

Background: Accurate recognition of invasive lung adenocarcinoma (IAC) presenting as ground-glass nodules (GGNs) is crucial for guiding clinical decision-making and timely surgical intervention. This study aimed to systematically evaluate the diagnostic accuracy of deep learning (DL) models via fine nodule segmentation in assessing the invasiveness of lung adenocarcinoma. Methods: Literature from the inception of the PubMed, Embase, Cochrane Library, and Web of Science databases was searched. Studies related to DL and nodule segmentation in diagnosing IAC were evaluated and included. Titles and abstracts were screened, and the Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess the quality of the selected studies. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria of diagnostic tests were used to assess the certainty of evidence. Results: Eight studies involving 5,281 nodules and 4,676 patients were included and analyzed. Meta-analysis showed that the combined sensitivity of DL for the diagnosis of IAC was 0.81 [95% confidence interval (CI): 0.73–0.87], while the specificity was 0.86 (95% CI: 0.80–0.90). The area under the summary receiver operating characteristic (SROC) curve was 0.90 (95% CI: 0.88–0.93), but the overall quality of the evidence was suboptimal. Conclusions: DL and nodule segmentation demonstrated high accuracy in assessing lung adenocarcinoma invasiveness, but the certainty of the associated evidence was low. More large-scale, multicenter, high-quality diagnostic accuracy studies are needed to validate the performance and usefulness of DL in the assessment of lung adenocarcinoma invasiveness.