Evidence map›Paper›PMID 30890092›Full record

ArticleTechnology in cancer research & treatment2019

Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images.

Tongtong Liu, Shichong Zhou, Jinhua Yu, Yi Guo, Yuanyuan Wang, Jin Zhou, Cai Chang

Abstract read
In one paragraph

Article in Technology in cancer research & treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 2 pooled it
–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

65 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  14. Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024
    Review
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  19. Targeted sequencing of DNA/RNA combined with radiomics predicts lymph node metastasis of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
  20. A CT based radiomics analysis to predict the CN0 status of thyroid papillary carcinoma: a two- center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article

5 more citing papers are in PubMed but not listed here.

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

7 authors.

Tongtong Liu1 Department of Electronic Engineering, Fudan University, Shanghai, China.ORCID 0000-0002-4112-7089
Shichong Zhou3 Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Jinhua Yu1 Department of Electronic Engineering, Fudan University, Shanghai, China.
Yi Guo1 Department of Electronic Engineering, Fudan University, Shanghai, China.ORCID 0000-0002-7142-2871
Yuanyuan Wang1 Department of Electronic Engineering, Fudan University, Shanghai, China.
Jin Zhou3 Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Cai Chang3 Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPapillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images.

methodsFour hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model.

resultsAmong 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710.

conclusionPapillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.

Indexed as

Preoperative CareCarcinoma, PapillaryFemaleFollow-Up StudiesHumansLymphatic MetastasisMaleMiddle AgedPositron-Emission TomographyPrognosisRetrospective StudiesROC CurveThyroid NeoplasmsUltrasonographyhead and neck cancerlymph node metastasispapillary thyroid carcinomaradiomicsultrasound images

Identifiers

PMID30890092
PMCPMC6429647

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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.