Evidence map›Paper›PMID 40917360›Full record

ArticleFrontiers in endocrinology2025

Preoperative identification of the risk factors of cervical lymph node metastasis in medullary thyroid carcinoma.

Yitong Wang, Jiahui Chen, Xuemeng Gao, Ying Huang

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yitong WangDepartment of Ultrasound, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.
Jiahui ChenDepartment of Ultrasound, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.
Xuemeng GaoDepartment of Ultrasound, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.
Ying HuangDepartment of Ultrasound, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research aimed to investigate the preoperative risk factors for lymph node metastasis (LNM) in medullary thyroid carcinoma (MTC) using clinical, pathological, serological, ultrasound, and radiomics characteristics. Additionally, it aimed to explore the diagnostic precision of ultrasound (US) for MTC and LNM. A retrospective analysis of 111 nodules was eligible from 104 patients from January 1, 2000, to December 28, 2024. Based on the presence of LNM, they were divided into Group 1 (with LNM, n=51 nodules from 44 patients) and Group 2 (without LNM, n=60 nodules from 60 patients). Three predictive models were constructed: (1) Model 1: incorporating clinical, pathological, serological, ultrasound features; (2) Model 2: utilizing only radiomics features; and (3) Model 3: combining all the above features. The AUC values for the three models were 0.759, 0.97, and 0.97, respectively. The nomogram for Model 1 achieved a C-index of 0.707. Additionally, we evaluated the diagnostic efficacy of ultrasound for MTC, lymph node enlargement, and metastasis. Results indicated that patients with symptoms on admission, multifocality, and solid lesions in MTC were at increased risk of LNM. The nomogram and radiomics features significantly improved the predictive performance. Our study provides a strong basis for predicting LNM.

Indexed as

Carcinoma, NeuroendocrineLymphatic MetastasisLymph NodesThyroid NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedNeckNomogramsPreoperative CarePreoperative PeriodRetrospective StudiesRisk Factorsmedullary thyroid carcinomametastasismultivariate regression modelradiomicsultrasound

Identifiers

PMID40917360
PMCPMC12408262

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LicenceCC BY
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Registered trials

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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.