Lung cancer remains a major global health burden, and deep learning, especially convolutional neural networks, has become a widely used approach for CT-based detection and diagnostic support. This scientometric study maps how this research area has developed and how its main themes and collaborations are organized, using a workflow that combines bibliometrix (R) with VOSviewer. From a broader Scopus query result set, an exported core subset of 200 journal articles published between 2017 and 2025 was selected for in-depth bibliometric analysis, spanning 139 sources and including 1,659 cited references. The citation overview reports 7,167 total citations across 175 cited documents, corresponding to a corpus h-index of 42. Thematic mapping and keyword co-occurrence analyses show a stable CT-centric backbone that connects most studies, alongside faster-growing streams focused on model generalization and optimization, and smaller niche lines such as 3D CNN–driven lung nodule detection. Collaboration indicators point to moderate internationalization, with 43 multi-country publications (21.5%). Overall, the field is consolidating around CT-based deep learning pipelines, while a key gap remains in translation-ready evidence, multi-center external validation, robustness to protocol heterogeneity, and more consistent reporting practices to support reproducibility and clinical adoption.