Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques

• Published in: Expert systems with applications Vol. 255; p. 124665
• Main Authors: Atmakuru, Anirudh, Chakraborty, Subrata, Faust, Oliver, Salvi, Massimo, Datta Barua, Prabal, Molinari, Filippo, Acharya, U.R., Homaira, Nusrat
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Classification; Deep learning; Non-small cell lung cancer; Segmentation; Therapy outcome prediction; Transfer learning; Deep learning; Segmentation; Transfer learning; Therapy outcome prediction; Non-small cell lung cancer; Classification
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.124665

•First comprehensive, systematic review of deep learning in radiology for lung cancer.•Review 153 papers for segmentation, survival prediction, subtype classification, etc.•Categorize state-of-the-art models on different imaging modalities (PET, CT, PET/CT).•Present the best models across each image modality and lung cancer-related task.•Review current challenges, future directions for efficient lung cancer investigation. This study presents a comprehensive systematic review focusing on the applications of deep learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 scientific publications following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we selected 153 papers for an in-depth analysis. These papers were categorized based on imaging modality, deep learning model type, and practical applications in lung cancer, such as detection and survival prediction. We specifically emphasized deep learning models and examined their strengths and limitations for each application and imaging modality. Furthermore, we identified potential limitations within the field and proposed future research directions. This study serves as a pioneering resource, being the first comprehensive and systematic review of deep learning techniques, specifically in the context of lung cancer-related applications. Our primary objective was to provide a reference for future research, encouraging the advancement of deep learning techniques in the diagnosis and treatment of lung cancer. By suggesting the most effective deep learning tools for specific application areas, we offer a benchmark for future studies. In summary, this study consolidates and expands existing knowledge on deep learning and radiomics applications in lung cancer. It provides a foundation for further research and serves as a guide for developing and evaluating deep learning models in lung cancer-related applications.