Machine learning–driven analysis of MRI images for lung cancer detection
Abstract
Lung cancer remains a leading cause of cancer-related mortality worldwide, largely due to delayed diagnosis and limitations in existing screening modalities. While computed tomography is widely used for lung cancer detection, concerns regarding ionizing radiation exposure and diagnostic specificity have encouraged the exploration of alternative imaging approaches. Recent advances in magnetic resonance imaging have improved its applicability for thoracic imaging, enabling enhanced visualization of lung tissue and functional characteristics without radiation risk. In parallel, rapid progress in machine learning has transformed medical image analysis by enabling automated, data-driven detection of complex pathological patterns. This editorial examines the emerging role of machine learning–based analysis of MRI images for lung cancer detection from a computer science and engineering perspective. It discusses technological developments in MRI acquisition, algorithmic advances in machine learning and deep learning, and the computational challenges associated with data scarcity, variability, and model interpretability. Ethical, clinical, and regulatory considerations relevant to real-world deployment are also highlighted. By synthesizing current trends and future research directions, this editorial underscores the potential of integrating machine learning with MRI to improve early lung cancer detection, reduce diagnostic risk, and support clinical decision-making. The topic…
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