Evidence map›Paper›PMID 37783270›Full record

ArticleJournal of advanced research2024

Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning.

Jandee Lee, Jung Hyun Yoon, Eunjung Lee, Hwa Young Lee, Seonhyang Jeong, Sunmi Park, Young Suk Jo, Jin Young Kwak

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

Article in Journal of advanced research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 15% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Jandee LeeDepartment of Surgery, Open NBI Convergence Technology Research Laboratory, Yonsei Cancer Center, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, South Korea.
Jung Hyun YoonDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University, College of Medicine, Seoul 03722, South Korea.
Eunjung LeeSchool of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul 03722, South Korea.
Hwa Young LeeDepartment of Surgery, Open NBI Convergence Technology Research Laboratory, Yonsei Cancer Center, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, South Korea.
Seonhyang JeongDepartment of Internal Medicine, Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul 03722, South Korea.
Sunmi ParkDepartment of Internal Medicine, Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul 03722, South Korea.
Young Suk JoDepartment of Internal Medicine, Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul 03722, South Korea. Electronic address: joys@yuhs.ac.
Jin Young KwakDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University, College of Medicine, Seoul 03722, South Korea. Electronic address: docjin@yuhs.ac.
Yonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionUltrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US features of PTCs are largely unknown.

objectivesThis study aimed to investigate the molecular biological mechanisms behind US features assessed by radiologists and three convolutional neural networks (CNN) through transcriptome analysis.

methodsTranscriptome data from 273 PTC tissue samples were generated and differentially expressed genes (DEGs) were identified according to US feature. Pathway enrichment analyses were also conducted by gene set enrichment analysis (GSEA) and ClusterProfiler according to assessments made by radiologists and three CNNs - CNN1 (ResNet50), CNN2 (ResNet101) and CNN3 (VGG16). Signature gene scores for PTCs were calculated by single-sample GSEA (ssGSEA).

resultsIndividual suspicious US features consistently suggested an upregulation of genes related to immune response and epithelial-mesenchymal transition (EMT). Likewise, PTCs assessed as positive by radiologists and three CNNs showed the coordinate enrichment of similar gene sets with abundant immune and stromal components. However, PTCs assessed as positive by radiologists had the highest number of DEGs, and those assessed as positive by CNN3 had more diverse DEGs and gene sets compared to CNN1 or CNN2. The percentage of PTCs assessed as positive or negative concordantly by radiologists and three CNNs was 85.6% (231/273) and 7.1% (3/273), respectively.

conclusionUS features assessed by radiologists and CNNs revealed molecular biologic features and tumor microenvironment in PTCs.

Indexed as

Deep LearningEpithelial-Mesenchymal TransitionRadiologistsThyroid Cancer, PapillaryThyroid NeoplasmsUltrasonographyAdultAgedFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedTranscriptomeConvolutional neural networkEpithelial-mesenchymal transitionImmune responsePapillary thyroid cancerRadiologistsTranscriptomeUltrasonography

Identifiers

PMID37783270
PMCPMC11331164
OpenAlexW4387234028

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.