Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta‐analysis

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.