Research progress in computer-aided diagnosis systems for lung cancer

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Lung cancer is the leading cause of cancer death, necessitating improved diagnostic accuracy through computer-assisted systems.
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Computer-aided diagnosis (CAD) systems have evolved from traditional algorithms to advanced deep learning models, enhancing detection sensitivity.
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Current CAD systems achieve an AUC of ≥ 0.95 and reduce false positives to <0.1 per CT, significantly improving early lung cancer detection.
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Multimodal imaging techniques, such as CT/PET fusion, enhance diagnostic capabilities and provide comprehensive assessments of lung cancer.
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The integration of interpretable AI and privacy-preserving learning methods is crucial for the future development of lung cancer diagnostic systems.

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