Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.

ABSTRACT: Background/Objectives: Detecting lung nodules on computed tomography (CT) images is critical for diagnosing thoracic cancers. Deep learning models, particularly convolutional neural networks (CNNs), show promise in automating this process. This systematic review and meta-analysis aim to evaluate the diagnostic accuracy of these models, focusing on lesion-wise sensitivity as the primary metric. Methods: A comprehensive literature search was conducted, identifying 48 studies published up to 7 November 2023. The pooled diagnostic performance was assessed using a random-effects model, with lesion-wise sensitivity as the key outcome. Factors influencing model performance, including participant demographics, dataset privacy, and data splitting methods, were analyzed. Methodological

SUBMITTER: Wang TW 

PROVIDER: S-EPMC11853243 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature