Related Experiment Video For FNCA
Updated: Dec 18, 2025
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis
Jian Huang1, Dongcun Wang1, Jiping Da1
1Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Background:
The role of retrospective analysis has been evolved greatly in cancer research. We undertook this meta-analysis to evaluate the diagnostic value of Neural networks (NNs) in Fine needle aspiration cytological (FNAC) image of cancer.
Methods:
We systematically retrieved 396 literatures on cytodiagnosis of NNs from Cochrane, PubMed, and EMBASE. After screening, only six studies were included in meta-analysis finally. Data was comprehensively analyzed by RevMan and meta-Disc software.
Results:
A total of 1165 cases were extracted from six articles. Among them, 593 cases were in the abnormal/positive group and 572 cases in the normal/negative group. The pooled estimates for the NNs cytology were Area under ROC curve (AUC): 0.99, Sensitivity: 0.85 (95% CI:0.82-0.88), Specificity: 0.96 (95% CI:0.94-0.97), Positive Likelihood Ratio (LR):18.43 (95% CI:6.83-49.74), Negative Likelihood Ratio (LR): 0.06 (95% CI:0.001-0.58), and Diagnostic odds ratio (DOR): 343.21 (34.41-3422.77).
Conclusions:
This meta-analysis confirms that NNs Automated Classification algorithm can facilitate to some extent the FNCA diagnosis of cancer.
