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<p>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 &lt;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.</p>

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Research progress in computer-aided diagnosis systems for lung cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Ke Ma,
  • Min Zheng,
  • Wenli Chen,
  • Yunxiang Qi,
  • Hao Rong

摘要

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.