Evidence map›Paper›PMID 41254777›Full record

ArticleWorld journal of surgical oncology2025

Development of a malignancy risk prediction model combining ultrasound and cytology for Bethesda category III thyroid nodules.

Fan Zhang, Fang Mei, Wen Chen

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Article in World journal of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. The role of cytological subcategorization in indeterminate thyroid nodules: Optimizing management strategies by refining malignancy risk stratification.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Fan Zhang *Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Haidian District,, Beijing, P.R., 100191, China.
Fang Mei *Department of Pathology, Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing, P.R., 100191, China.
Wen ChenDepartment of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Haidian District,, Beijing, P.R., 100191, China. chenwen0708@bjmu.edu.cn.

Funding

Proof of Concept Program of Zhongguancun Science City and Peking University Third Hospital HDCXZHKC2022210
6 · The paper itself

Abstract

backgroundBethesda category III thyroid nodules pose substantial management challenges, and a validated malignancy risk stratification system is urgently needed to guide clinical decision-making. This study aims to identify specific sonographic and cytological features associated with malignant Bethesda III thyroid nodules and to develop a practical model for predicting malignancy risk through an integrated analysis of multimodal data from histopathologically confirmed cases.

methodsThis retrospective study used clinical data from patients with both Bethesda III cytological diagnoses and corresponding surgical pathology outcomes between July 2016 and December 2024. The sonographic, clinical, and cytological characteristics of benign and malignant nodules were systematically assessed and compared. Univariable and multivariable logistic regression analyses were conducted to evaluate the association between these features and malignancy. A prediction nomogram, incorporating sonographic and cytological features independently associated with malignancy, was developed and validated to assess its performance.

resultsIn this study, a total of 187 Bethesda III thyroid nodules were analyzed, consisting of 77 benign nodules and 110 malignant nodules. Factors such as maximum diameter ≤ 1 cm, absence of smooth margins, microcalcifications, and nuclear atypia in cytology were identified as independent factors associated with malignancy. A prediction nomogram model was developed using these variables, demonstrating strong performance in distinguishing between benign and malignant nodules categorized as Bethesda III, with an area under the curve (AUC) of 0.874.

conclusionsIn Bethesda III thyroid nodules, malignant cases were associated with suspicious sonographic characteristics, smaller size (≤ 1 cm), and cytological nuclear atypia. The nomogram developed from these predictors demonstrates potential utility in estimating malignancy risk for Bethesda III thyroid nodules.

Indexed as

NomogramsThyroid NeoplasmsThyroid NoduleAdultAgedBiopsy, Fine-NeedleCytodiagnosisFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentUltrasonographyBethesda IIICytologyPrediction modelThyroid noduleUltrasound

Identifiers

PMID41254777
PMCPMC12625282

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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.