Evidence map›Paper›PMID 42445878›Full record

ArticleFrontiers in endocrinology2026

An online nomogram based on bimodal ultrasound images for preoperative diagnosis of cytologically indeterminate thyroid nodules.

Wenwu Lu, Di Zhang, Xin Wu, Wenbo Ding, Xiang Xie, Lei Hu, Lei Ye, Wang Zhou, Chaoxue Zhang

Abstract readMulticenter Study
In one paragraph

Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wenwu Lu *Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University (Jixi Campus), Hefei, Anhui, China.
Di Zhang *Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University (Jixi Campus), Hefei, Anhui, China.
Xin Wu *Department of Ultrasound, Affiliated Hospital of Integration Chinese and Western Medicine with Nanjing University of Traditional Chinese Medicine, Nanjing, China.
Wenbo DingDepartment of Ultrasound, Affiliated Hospital of Integration Chinese and Western Medicine with Nanjing University of Traditional Chinese Medicine, Nanjing, China.
Xiang XieDepartment of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Lei HuDepartment of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of People's Republic of China, Hefei, Anhui, China.
Lei YeDepartment of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of People's Republic of China, Hefei, Anhui, China.
Wang ZhouDepartment of Ultrasound, The First Affiliated Hospital of Anhui Medical University (Jixi Campus), Hefei, Anhui, China.
Chaoxue ZhangDepartment of Ultrasound, The First Affiliated Hospital of Anhui Medical University (Jixi Campus), Hefei, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurately distinguishing benign from malignant indeterminate thyroid nodules is essential to avoid unnecessary diagnostic surgeries. This study aims to develop an online nomogram model that enables precise preoperative assessment of malignancy risk in indeterminate thyroid nodules (ITNs) following fine-needle aspiration, thereby helping to reduce unnecessary thyroidectomies. Methods: Patients with thyroid nodules were recruited from five centers and divided into retrospective training, independent testing, and prospective validation cohorts. Radiomics features and deep learning features were extracted from both B-mode ultrasound (BMUS) and strain elastography ultrasound (SEUS) images for each patient. Malignancy-associated features were selected to construct BMUS and SEUS signature scores, respectively. Multivariate regression analysis was performed on all variables to develop and visualize a comprehensive model for diagnosing benign and malignant ITNs. The model was further evaluated for discrimination, calibration, and clinical usefulness. Results: Multimodal imaging features, genetic testing, and elastography levels were identified as key biological markers for diagnosing ITNs. The nomogram model built with these variables demonstrated strong performance. The area under the receiver operating characteristic curve was 0.907 (95% CI: 0.877-0.931) in the training set, 0.885 (95% CI: 0.821-0.932) in the external test set, and 0.860 (95% CI: 0.762-0.929) in the prospective validation set. Compared to clinical and individual scoring models, the nomogram demonstrated superior performance and calibration. Conclusions: The proposed nomogram accurately diagnoses ITNs and holds promise for reducing unnecessary diagnostic thyroidectomies in clinical practice.

Indexed as

NomogramsThyroid NeoplasmsThyroid NoduleAdultBiopsy, Fine-NeedleElasticity Imaging TechniquesFemaleHumansMaleMiddle AgedProspective StudiesRadiomicsRetrospective StudiesUltrasonographymolecular testingradiomicsthyroid nodulestransfer learningultrasound

Identifiers

PMID42445878
PMCPMC13357161

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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.