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ArticleScientific reports2023

Contrast-enhanced CT-based Radiomics for the Differentiation of Anaplastic or Poorly Differentiated Thyroid Carcinoma from Differentiated Thyroid Carcinoma: A Pilot Study.

Jayoung Moon, Jeong Hoon Lee, Jin Roh, Da Hyun Lee, Eun Ju Ha

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Radiomics in thyroid nodule assessment.BMC medical imaging · 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

5 authors.

Jayoung Moon *Department of Radiology, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, 16499, Korea.
Jeong Hoon Lee *Department of Radiology, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, 16499, Korea.
Jin RohDepartment of Pathology, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, 16499, Korea.
Da Hyun LeeDepartment of Radiology, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, 16499, Korea.
Eun Ju HaDepartment of Radiology, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, 16499, Korea. radhej@naver.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differential diagnosis of anaplastic thyroid carcinoma/poorly differentiated thyroid carcinoma (ATC/PDTC) from differentiated thyroid carcinoma (DTC) is crucial in patients with large thyroid malignancies. This study creates a predictive model using radiomics feature analysis to differentiate ATC/PDTC from DTC. We compared the clinicoradiological characteristics and radiomics features extracted from a volume of interest on contrast-enhanced computed tomography (CT) between the groups. Estimations of variable importance were performed via modeling using the random forest quantile classifier. The diagnostic performance of the model with radiomics features alone had the area under the receiver operating characteristic (AUROC) curve value of 0.883. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 81.7%, 93.3%, 97.7%, 64.5%, and 84.6%, respectively, for the differential diagnosis of ATC/PDTC and DTC. The model with both radiomics and clinicoradiological information showed the AUROC of 0.908, with sensitivity, specificity, PPV, NPV, and accuracy of 82.9%, 97.6%, 99.2%, 67.1%, and 86.5% respectively. Distant metastasis, moment, shape, age, and gray-level size zone matrix features were the most useful factors for differential diagnosis. Therefore, we concluded that a radiomics approach based on contrast-enhanced CT features can potentially differentiate ATC/PDTC from DTC in patients with large thyroid malignancies.

Indexed as

AdenocarcinomaThyroid Carcinoma, AnaplasticThyroid NeoplasmsHumansPilot ProjectsProlineRetrospective StudiesThiocarbamatesTomography, X-Ray ComputedProlineprolinedithiocarbamateThiocarbamates

Identifiers

PMID36941287
PMCPMC10027684

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