Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.
- DOI
- 10.3389/fonc.2026.1752652
- Published
- 2026
- Container
- Frontiers in oncology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/fonc.2026.1752652,
title = {Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.},
author = {Dong N and Li Z and Cong Z and Ba X and Qin J and Lin Z and Wang X and Liang P},
year = {2026},
journal = {Frontiers in oncology},
doi = {10.3389/fonc.2026.1752652},
url = {https://doi.org/10.3389/fonc.2026.1752652}
}RIS
TY - JOUR TI - Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis. AU - Dong N AU - Li Z AU - Cong Z AU - Ba X AU - Qin J AU - Lin Z AU - Wang X AU - Liang P PY - 2026 JO - Frontiers in oncology DO - 10.3389/fonc.2026.1752652 UR - https://doi.org/10.3389/fonc.2026.1752652 ER -
APA
N, D., Z, L., Z, C., X, B., J, Q., Z, L., X, W., & P, L. (2026). Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1752652
Source records
- pubmed · retrieved 2026-09-25T12:23:44.885Z