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Research progress in computer-aided diagnosis systems for lung cancer

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机构： [1]Department of Thoracic Surgery, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University ofElectronic Science and Technology of China, Chengdu, China [2]Department of Medical Oncology, Sichuan Clinical Research Center for cancer, Sichuan CancerHospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China [3]Department of Radiation Oncology,Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology ofChina, Chengdu, China

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Lung cancer remains the top cause of cancer death, demanding consistent decisions. This clinically oriented review synthesizes computer-aided diagnosis across classical imaging, machine learning, and deep learning, emphasizing bedside-proven advances: multimodal CT/PET-clinical fusion; small-data strategies; interpretable AI; and privacy-preserving multi-center learning. Reported systems reach AUC ≥ 0.95 with <0.1 false positives/CT and boost early detection by ~20-30%; prognostic C-index ~0.85-0.90. We outline implementation checkpoints and priorities to convert accuracy into patient benefit.© 2025. The Author(s).

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出版当年[2025]版：

大类 | 1 区

医学

小类 | 1 区卫生保健与服务 1 区医学：信息

最新[2025]版：

大类 | 1 区

医学

小类 | 1 区卫生保健与服务 1 区医学：信息

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第一作者机构： [1]Department of Thoracic Surgery, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University ofElectronic Science and Technology of China, Chengdu, China

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