Evidence map›Paper›PMID 40046879›Full record

ArticleFrontiers in endocrinology2025

Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma.

Dai Zhang, Fan Yang, Wenjing Hou, Ying Wang, Jiali Mu, Hailing Wang, Xi Wei

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Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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.

Dai Zhang *Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Fan Yang *Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Wenjing HouDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Ying WangDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Jiali MuDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Hailing WangDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Xi WeiDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Medullary thyroid carcinoma (MTC) is aggressive and difficult to distinguish from papillary thyroid carcinoma (PTC) using traditional ultrasound. Objective to establish a standard-based ultrasound imaging model for preoperative differentiation of MTC from PTC. Methods: A retrospective study was conducted on the case data of 213 thyroid cancer patients (82 MTC, 90 lesions; 131 PTC, 135 lesions) from the Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital. We constructed clinical model, radiomics model and comprehensive model by executing machine learning algorithms based on baseline clinical, pathological characteristics and ultrasound image data, respectively. Results: The study showed that the comprehensive model observed the highest diagnostic efficacy in differentiating MTC from PTC with AUC, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 0.93, 0.88, 0.82, 0.77, 0.91, 85.8%. Delong test results showed that the comprehensive model was significantly better than the clinical model (Z=-3.791, P<0.001) and the radiomics model (Z=-2.017, Discussions: Radiomics model is effective in identifying MTC and PTC preoperatively, and the comprehensive model is better. This approach can aid in identifying the pathologic types of thyroid nodule before clinical operation, supporting personalized medicine in the decision-making process.

Indexed as

Carcinoma, NeuroendocrineThyroid Cancer, PapillaryThyroid NeoplasmsAdolescentAdultAgedDiagnosis, DifferentialFemaleHumansMachine LearningMaleMiddle AgedRadiomicsRetrospective StudiesUltrasonographyYoung Adultmedullary thyroid carcinomapapillary thyroid carcinomapersonalized medicineradiomicsultrasound

Identifiers

PMID40046879
PMCPMC11880605

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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.