Evidence map›Paper›PMID 38563008›Full record

ArticlePeerJ2024

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

Yu Wang, Hai-Long Tan, Sai-Li Duan, Ning Li, Lei Ai, Shi Chang

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Artificial intelligence in otolaryngology: current applications, limitations, and future perspectives.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
    Review
  3. Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026
    Review
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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

6 authors at 3 institutions in 1 country.

Yu WangDepartment of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Hai-Long TanDepartment of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Sai-Li DuanDepartment of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Ning LiDepartment of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Lei AiDepartment of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Shi ChangDepartment of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Central South University · CNXiangya Hospital Central South University · CNHunan Provincial Science and Technology Department · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F Results: The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, Conclusions: The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

Indexed as

Carcinoma, PapillaryDeep LearningThyroid NeoplasmsHumansLymphatic MetastasisRisk FactorsCentral lymph node metastasesDeep learningPapillary thyroid microcarcinomaUltrasound image

Identifiers

PMID38563008
PMCPMC10984175
OpenAlexW4393311825

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.