Article citationsMore>>

Miao, S., Xuan, Q., Jia, Q., Jiang, Y., Jia, H., An, Y., et al. (2025) Deep Learning-Based CT Image for Pulmonary Nodule Classification with Intrathoracic Fat: A Multicenter Study. Biomedical Signal Processing and Control, 100, Article ID: 106938.
https://doi.org/10.1016/j.bspc.2024.106938

has been cited by the following article:

• TITLE: The Diagnostic Significance of Liquid-Based Cytology of Thyroid Fluid for Benign and Malignant Conditions

  AUTHORS: Xi Lin

  KEYWORDS: Liquid-Based Cytology, Fine Needle Aspiration, Thyroid Nodules, Bethesda System, Diagnostic Efficacy

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.16 No.9, September 11, 2025

  ABSTRACT: Thyroid nodules are a common thyroid condition, with a detection rate of up to 20% - 50% in the general population, of which only 5% - 10% are malignant. Fine-needle aspiration cytology (FNAC) is currently the recommended preoperative screening method, while liquid-based cytology (LBC) has gained increasing application in thyroid diagnostics since 2000 due to its advantages such as a clean background and compatibility with auxiliary technologies. This article systematically reviews the value of LBC in distinguishing between benign and malignant thyroid nodules from the perspectives of methodology, morphology, diagnostic efficacy, Bethesda system grading, auxiliary techniques, limitations, and future directions, with the aim of providing evidence-based guidance for clinical and pathological professionals.