Work

Research progress in computer-aided diagnosis systems for lung cancer

Year: 2025

Type: article

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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-025-02101-3

Authors: Ke Ma, Min Zheng, Wenli Chen, Yunxiang Qi, Hao Rong

Institutions: University of Electronic Science and Technology of China, Sichuan Cancer Hospital

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,090