Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects

Non-small cell lung cancer (NSCLC), a major form of pulmonary malignancy and a leading global cause of cancer-related mortality, highlights the urgent need for advanced precision treatment approaches. This article comprehensively reviews the significant progress and future directions of deep learning techniques in revolutionizing the precise diagnosis and therapeutic management of NSCLC. It demonstrates how deep learning methods have the potential to surpass traditional tumor treatment paradigms, significantly enhancing diagnostic accuracy, personalizing treatment selection, and predicting patient outcomes with greater precision. The article traces the evolution of deep learning models in this field, from basic analyses relying on single data modalities, such as imaging or genomics alone, to more sophisticated architectures capable of multimodal data fusion. It emphasizes the crucial role of integrating radiological, pathological, genomic, and clinical data in uncovering deeper biological insights. Furthermore, it outlines the typical workflow involved in developing and deploying deep learning applications for NSCLC and lists some currently used models, including convolutional neural networks for image analysis and complex architectures for multi-omics data integration. These models show considerable potential for improving diagnostic accuracy and optimizing therapeutic interventions. However, translating these computational tools into routine clinical practice faces several challenges. The review candidly addresses key issues, including the need for large-scale, high-quality, and standardized datasets; the "black box" nature of complex models, which requires improved interpretability to gain clinicians' trust and provide actionable insights; and profound ethical considerations regarding data privacy, algorithmic bias, and equitable access. Despite these obstacles, deep learning has emerged as a powerful instrument in the oncological arsenal, significantly enhancing the precision and efficiency of NSCLC care. Finally, the article offers a dialectical perspective on the future of deep learning in NSCLC, exploring emerging trends and providing recommendations to overcome current limitations, with the goal of maximizing its potential to improve patient survival and quality of life.