Artificial intelligence in lung cancer: current applications and perspectives

Guillaume Chassagnon, Constance De Margerie‑Mellon, Maria Vakalopoulou, Rafael Marini, Trieu‑Nghi Hoang‑Thi, Marie‑Pierre Revel, Philippe Soyer
Japanese Journal of Radiology
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AI, particularly deep learning, has become a significant area of research in thoracic oncology, benefiting thoracic imaging. This article reviews the current applications and perspectives of AI in this field. CADe tools have been available since the early 2000s for pulmonary nodule detection, with newer deep learning-based tools reducing false-positive results. Machine learning and deep learning methods have been employed for pulmonary nodule segmentation, volumetry, and characterization. AVIEW LCS was mentioned as an AI tool dedicated to lung cancer screening that can not only automatically detect and measure lung nodules but also suggest Lung-RADS categorization. Data from the NLST has led to the development of CADx tools for lung cancer diagnosis on chest CT scans. AI has also been used for virtual biopsies and predicting treatment response or survival. Numerous detection, characterization, and stratification tools have been proposed, with some being commercially available.


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