ArticleNPJ precision oncology2025
Systematic review and meta-analysis of artificial intelligence for image-based lung cancer classification and prognostic evaluation.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gastric cancer survival prediction using artificial intelligence models based on electronic health records: a systematic review and meta-analysis.Frontiers in digital health · 2026Pooled it
- Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.Biomedical engineering online · 2026Article
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- Evaluating the Clinical Competence of Large Language Models in Prostate Cancer Management: A Comparative Study of DeepSeek-R1 and ChatGPT.Annals of surgical oncology · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Attention-Enhanced Hybrid Bidirectional LSTM and Temporal Convolutional Network for Early Detection of Lung Cancer in Low-Dose CT Scans.Biomedical engineering and computational biology · 2026Article
- Deep learning radiomics model of epicardial adipose tissue for predicting postoperative atrial fibrillation after lung lobectomy in lung cancer patients.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Lung cancer (LC) remains a leading global cause of cancer mortality, with current diagnostic and prognostic methods lacking precision. This meta-analysis evaluated the role of artificial intelligence (AI) in LC imaging-based diagnosis and prognostic prediction. We systematically reviewed 315 studies from major databases up to January 7, 2025. Among them, 209 studies on LC diagnosis yielded a combined sensitivity of 0.86 (0.84-0.87), specificity of 0.86 (0.84-0.87), and AUC of 0.92 (0.90-0.94). For LC prognosis, 106 studies were analyzed: 58 with diagnostic data showed a pooled sensitivity of 0.83 (0.81-0.86), specificity of 0.83 (0.80-0.86), and AUC of 0.90 (0.87-0.92). Additionally, 53 studies differentiated between low- and high-risk patients, with a pooled hazard ratio of 2.53 (2.22-2.89) for overall survival and 2.80 (2.42-3.23) for progression-free survival. Subgroup analyses revealed an acceptable performance. AI exhibits strong potential for LC management but requires prospective multicenter validation to address clinical implementation challenges.
Identifiers
What OpenQuestion 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 OpenQuestion graph.