Evidence map›Paper›PMID 38609953›Full record

Trial reportBMC medicine2024

Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study.

Ming-Bo Zhang, Zhe-Ling Meng, Yi Mao, Xue Jiang, Ning Xu, Qing-Hua Xu, Jie Tian, Yu-Kun Luo, Kun Wang

Open access · goldAbstract readClinical TrialMulticenter Study
In one paragraph

Trial report in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
7.9field-weighted citation impact, top 2% of its field
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

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.

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  16. Predictive factors of metastasis in lymph nodes posterior to the right recurrent laryngeal nerve in papillary thyroid carcinoma.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 · 2025
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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

9 authors at 4 institutions in 1 country.

Ming-Bo Zhang *Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China.
Zhe-Ling Meng *CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Yi MaoDepartment of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China.
Xue JiangDepartment of Ultrasound, the Fourth Medical Center, General Hospital of Chinese PLA, Beijing, China.
Ning XuDepartment of Ultrasound, Beijing Tong Ren Hospital, Beijing, China.
Qing-Hua XuDepartment of Ultrasound, China-Japan Friendship Hospital, Beijing, China.
Jie TianCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Yu-Kun LuoDepartment of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China. lyk301@163.com.
Kun WangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. kun.wang@ia.ac.cn.ORCID 0000-0003-2513-768X
Chinese Academy of Sciences · CNChinese PLA General Hospital · CNBeijing Tongren Hospital · CNChina-Japan Friendship Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance.

methodsFrom September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity.

resultsIn total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance.

conclusionsThe AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists.

trial registrationWe registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.

Indexed as

Artificial IntelligenceThyroid NeoplasmsHumansLymphatic MetastasisProspective StudiesRetrospective StudiesThyroid Cancer, PapillaryDeep learningLymphatic metastasisPapillaryThyroid cancerUltrasonography

Identifiers

PMID38609953
PMCPMC11015607
OpenAlexW4394769463

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.