ReviewCancers2022
Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 5 of them syntheses that pooled it.
What it found
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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.
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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.
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Who cites it
31 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Pooled it
- Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2024Pooled it
- Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.European radiology · 2024Pooled it
- Pooled it
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Applying artificial intelligence to ensure high quality and equitable lung cancer screening.Translational lung cancer research · 2026Review
- Review
- Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.World journal of gastrointestinal oncology · 2026Article
- Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study.JMIR bioinformatics and biotechnology · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- A Systematic Review of AI Performance in Lung Cancer Detection on CT Thorax.Healthcare (Basel, Switzerland) · 2025Review
- Smart Healthcare: The Role of Digital Health in Modern Medicine.Health care science · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.Cancers · 2025Review
- MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.Journal of imaging · 2024Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation.Journal of personalized medicine · 2024Article
- Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.Radiology. Cardiothoracic imaging · 2024Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. Six studies were included in the analysis. The pooled sensitivity and specificity were 0.93 (95% CI 0.85−0.98) and 0.68 (95% CI 0.49−0.84), respectively. Despite the significantly high heterogeneity for sensitivity (I2 = 94%, p < 0.01) and specificity (I2 = 99%, p < 0.01), most of it was attributed to the threshold effect. The pooled SROC curve with a bivariate approach yielded an area under the curve (AUC) of 0.90 (95% CI 0.86 to 0.92). The DOR for the studies was 26.7 (95% CI 19.7−36.2) and heterogeneity was 3% (p = 0.40). In this systematic review and meta-analysis, we found that when using the summary point from the SROC, the pooled sensitivity and specificity of DL algorithms for the diagnosis of lung cancer were 93% and 68%, respectively.
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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.