ArticleAsian Pacific journal of cancer prevention : APJCP2023
Artificial Intelligence Role in Subclassifying Cytology of Thyroid Follicular Neoplasm.
Article in Asian Pacific journal of cancer prevention : APJCP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.European journal of medical research · 2024Pooled it
- Deep learning-based ultrasound diagnostic model for follicular thyroid carcinoma.European radiology · 2026Article
- [Prospects for the application of convolutional neural networks in the cytological diagnosis of thyroid nodules].Problemy endokrinologii · 2025Review
- Deep learning-based differentiation of benign and malignant thyroid follicular neoplasms on multiscale intraoperative frozen pathological images: A multicenter diagnostic study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Advancements in Digital Cytopathology Since COVID-19: Insights from a Narrative Review of Review Articles.Healthcare (Basel, Switzerland) · 2025Review
- The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms.BMC medical imaging · 2024Article
- Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.Frontiers in endocrinology · 2024Article
- A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.Journal of imaging · 2023Article
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7 authors.
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Abstract
objectiveFine needle aspiration cytology has higher sensitivity and predictive value for diagnosis of thyroid nodules than any other single diagnostic methods. In the Bethesda system for reporting thyroid, the category IV, encompasses both adenoma and carcinoma, but it is not possible to differentiate both lesions in the cytology practice and can be only differentiated after resection. In this work, we aim at exploring the ability of a convolutional neural network (CNN) model to sub-classifying cytological images of Bethesda category IV diagnosis into follicular adenoma and follicular carcinoma.
methodsWe used a cohort of cytology cases n= 43 with extracted images n= 886 to train CNN model aiming to sub-classify follicular neoplasm (Bethesda category IV) into either follicular adenoma or follicular carcinoma.
resultIn our study, the model subclassification of follicular neoplasm into follicular adenoma (n = 28/43, images n = 527/886) from follicular carcinoma (n = 15/43, images n= 359/886), has achieved an accuracy of 78%, with a sensitivity of 88.4%, and a specificity of 64% and an area under the curve (AUC) score of 0.87 for each of follicular adenoma and follicular carcinoma.
conclusionOur CNN model has achieved high sensitivity in recognizing follicular adenoma amongest cytology smears of follciualr neoplasms, thus it can be used as an ancillary technique in the subcalssification of Bethesda Iv category cytology smears.
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