Evidence map›Paper›PMID 38509485›Full record

ArticleBMC cancer2024

Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma.

Yanwei Chen, Ming Yin, Yifeng Zhang, Ning Zhou, Shuangshuang Zhao, Hongqing Yin, Jun Shao, Xin Min, Baoding Chen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Frontiers in endocrinology · 2025
    Article
  2. 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

9 authors at 5 institutions in 1 country.

Yanwei Chen *Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, 212000, Zhenjiang, Jiangsu, China.
Ming Yin *Department of Medical Ultrasound, The Affiliated Taizhou People's Hospital of Nanjing Medical University , 225300, Taizhou, Jiangsu, China.
Yifeng ZhangDepartment of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, School of Medicine, Tongji University, 200072, Shanghai, China.
Ning ZhouLisen Imprinting Diagnostics, Inc., 214135, Wuxi, Jiangsu, China.
Shuangshuang ZhaoDepartment of Medical Ultrasound, Affiliated Hospital of Jiangsu University, 212000, Zhenjiang, Jiangsu, China.
Hongqing YinDepartment of Medical Ultrasound, The First People's Hospital of Kunshan, 215300, Kunshan, Jiangsu, China.
Jun ShaoDepartment of Medical Ultrasound, The First People's Hospital of Kunshan, 215300, Kunshan, Jiangsu, China.
Xin MinDepartment of Medical Ultrasound, Affiliated Hospital of Jiangsu University, 212000, Zhenjiang, Jiangsu, China.
Baoding ChenDepartment of Medical Ultrasound, Affiliated Hospital of Jiangsu University, 212000, Zhenjiang, Jiangsu, China. alphalife@163.com.
Jiangsu University · CNAffiliated Hospital of Jiangsu University · CNFirst People's Hospital of Kunshan · CNNanjing Medical University · CNTongji University · CN

Funding

Sixth Phase "169 Project" Scientific Research Project of Zhenjiang YLJ202104Social Development Program of Zhenjiang SH2023019Social Development Science and Technology Project of Kunshan KSF2021131
6 · The paper itself

Abstract

backgroundPapillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC.

methodsA total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model.

resultsA comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC.

conclusionThe present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

Indexed as

Carcinoma, PapillaryThyroid NeoplasmsThyroid NoduleHumansNomogramsRetrospective StudiesThyroid Cancer, PapillaryImprinted gene detectionNomogramPapillary thyroid carcinomaPrediction modelUltrasound features

Identifiers

PMID38509485
PMCPMC10953243
OpenAlexW4393006201

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.