Evidence map›Paper›PMID 39210250›Full record

ArticleBMC medical imaging2024

Prediction model for lateral lymph node metastasis of papillary thyroid carcinoma in children and adolescents based on ultrasound imaging and clinical features: a retrospective study.

Shiyang Lin, Yuan Zhong, Yidi Lin, Guangjian Liu

Abstract readEvaluation StudyMulticenter Study
In one paragraph

Article in BMC medical imaging, 2024. 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.

0numbers the graph read from it
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2citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Shiyang Lin *Department of ultrasound, The Sixth Affiliated Hospital, Sun Yat-sen University, Biomedical Innovation Center, 26 Yuancunerheng Street, Tianhe District, Guangzhou, China.
Yuan Zhong *Department of ultrasound, The First People's Hospital of Foshan, Dafu Road, Chancheng District, Foshan, China.
Yidi LinDepartment of ultrasound, Guangzhou Panyu Central Hospital, 8 Fuyu East Road, South Bridge Street, Panyu District, Guangzhou, China.
Guangjian LiuDepartment of ultrasound, The Sixth Affiliated Hospital, Sun Yat-sen University, Biomedical Innovation Center, 26 Yuancunerheng Street, Tianhe District, Guangzhou, China. liugj@mail.sysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC.

methodsWe developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis.

resultsAmong the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%.

conclusionsThe independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.

Indexed as

Lymphatic MetastasisPredictive Learning ModelsThyroid Cancer, PapillaryThyroid NeoplasmsUltrasonographyAdolescentArea Under CurveFemaleHumansLymph NodesMalePredictive Value of TestsReference ValuesRegression AnalysisRetrospective StudiesRisk AssessmentChildrenLymph node metastasisPapillary thyroid cancerPrediction modelUltrasound

Identifiers

PMID39210250
PMCPMC11361114

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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.