SynthesisFrontiers in public health2022
Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis.
Synthesis in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 13 of them syntheses that pooled it.
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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.
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Who cites it
40 citing papers in PubMed, 13 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Diagnostic accuracy of CT-based radiomics for predicting occult lymph node metastasis in early-stage lung adenocarcinoma: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Effectiveness and implementation challenges of mobile low-dose computed tomography units for early lung cancer detection: a systematic review.Frontiers in public health · 2026Pooled it
- Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.European radiology · 2025Pooled it
- The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis.Journal of gynecologic oncology · 2025Pooled it
- Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.Arquivos de neuro-psiquiatria · 2024Pooled it
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.Frontiers in medicine · 2024Pooled it
- The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2023Pooled it
- Development and internal validation of a high-resolution computed tomography radiomics and three-dimensional deep learning diagnostic prediction model for preoperative differentiation of minimally invasive and invasive adenocarcinoma in subsolid nodules.Journal of thoracic disease · 2026Article
- Radiologists' diagnostic confidence in surgically referred pulmonary nodules: a routine computed tomography report retrospective analysis.BMC pulmonary medicine · 2026Article
- A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study.Abdominal radiology (New York) · 2026Article
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- Review
- An optimized EfficientNetB7 deep learning model for lung cancer tissue pathology image recognition.Frontiers in oncology · 2026Article
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging. Methods: Studies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis. Results: The systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78-0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73-0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77-0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66-0.82). Conclusion: The models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging. Systematic Review Registration: https://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.
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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.