ReviewNPJ digital medicine2025
Research progress in computer-aided diagnosis systems for lung cancer.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Specific PET Imaging for Precision Management of Lung Cancer: Advances, Clinical Translation and Future Directions.Chemical & biomedical imaging · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- FAD-YOLO: a lightweight feature-refined and task-aligned framework for AIS-MIA discrimination on pulmonary CT.Frontiers in medicine · 2026Article
- A spatial correlation-guided deep fusion framework for multimodal lung cancer classification using CT imaging.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.