Research progress in computer-aided diagnosis systems for lung cancer.
Authors
Ma K,Zheng M,Chen W,Qi Y,Rong H
Affiliations (5)
Affiliations (5)
- Department of Thoracic Surgery, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Medical Oncology, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Radiation Oncology, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Radiation Oncology, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China. [email protected].
- Department of Thoracic Surgery, Sichuan Clinical Research Center for cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China. [email protected].
Abstract
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.
Topics
Journal ArticleReview