Deep Learning-Based Lung Cancer Segmentation and Volumetric Analysis Using CT Imaging: A Comprehensive Survey

Lung cancer is one of the most prevalent causes of cancer-related deaths in the world, and in this context, accurate estimation of tumor burden and treatment response is critical in clinical decision-making. Traditional radiological assessment techniques, especially linear measurements characterized by the Response Evaluation Criteria in Solid Tumors (RECIST), lack the capacity to reflect the three-dimensional morphology and longitudinal changes of tumors. This has led to a growing clinical need for more detailed and reproducible volumetric analysis frameworks. In parallel with this clinical requirement, deep learning has become an important part of lung cancer imaging, making it possible to achieve automated, reproducible, and volumetrically consistent tumor segmentation of computed tomography scans. Over the past ten years, deep learning architectures, including U-Net-based models and attention-focused variants, hybrid CNN-Transformer models, and multimodal PET/CT strategies, have been proposed. However, even in the context of rapid methodological development, there remains an obvious gap between segmentation accuracy, the ability to extract reliable volumetric biomarkers, and their translation into clinically meaningful assessment models. This survey provides a detailed and systematic review of efficient segmentation and volumetric analysis of lung cancer on CT with the aid of deep learning. Existing research is categorized by architectural design, learning strategy, dataset characteristics, and intended clinical use. Special attention is paid to the relationship between segmentation performance, volumetric reliability, and downstream clinical use of the segments, including measurement of treatment response and longitudinal monitoring of tumors. Publicly accessible datasets such as LIDC-IDRI, RIDER Lung CT, and NSCLC-Radiomics are examined in relation to nodule detection tasks, test-retest reproducibility, and prognostic volumetric modeling. In summary, although deep learning-based volumetric analysis has shown great potential in addressing the shortcomings of linear tumor assessment, several challenges still impede clinical implementation. These include limited data availability, variability in annotation, sensitivity to scanners and acquisition protocols, poor interpretability, and multimodal integration. New research directions, including transformer-based volumetric models, self- and semi-supervised learning, generative modeling of tumor dynamics, and uncertainty-aware systems, are discussed as pathways toward more robust and clinically useful AI-driven lung cancer imaging models.