SynthesisDiagnostic cytopathology2020
Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis.
Synthesis in Diagnostic cytopathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis 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.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis.Diagnostic cytopathology · 2020Pooled it
- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe 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.
methodsWe 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.
resultsA 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).
conclusionsThis meta-analysis confirms that NNs Automated Classification algorithm can facilitate to some extent the FNCA diagnosis of cancer.
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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.