Detection of malignant lesions in cytologically indeterminate thyroid nodules using a dual-layer spectral detector CT-clinical nomogram Article Swipe
Xiaofang Ren
,
Jiayan Zhang
,
Zuhua Song
,
Qian Li
,
Dan Zhang
,
Xiaojiao Li
,
Jiayi Yu
,
Zongwen Li
,
Youjia Wen
,
Dan Zeng
,
Xiaodi Zhang
,
Zhuoyue Tang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1357419
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1357419
Purpose To evaluate the capability of dual-layer detector spectral CT (DLCT) quantitative parameters in conjunction with clinical variables to detect malignant lesions in cytologically indeterminate thyroid nodules (TNs). Materials and methods Data from 107 patients with cytologically indeterminate TNs who underwent DLCT scans were retrospectively reviewed and randomly divided into training and validation sets (7:3 ratio). DLCT quantitative parameters (iodine concentration (IC), NIC P (IC nodule/IC thyroid parenchyma), NIC A (IC nodule/IC ipsilateral carotid artery), attenuation on the slope of spectral HU curve and effective atomic number), along with clinical variables, were compared between benign and malignant cohorts through univariate analysis. Multivariable logistic regression analysis was employed to identify independent predictors which were used to construct the clinical model, DLCT model, and combined model. A nomogram was formulated based on optimal performing model, and its performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis. The nomogram was subsequently tested in the validation set. Results Independent predictors associated with malignant TNs with indeterminate cytology included NIC P in the arterial phase, Hashimoto’s Thyroiditis (HT), and BRAF V600E (all p < 0.05). The DLCT-clinical nomogram, incorporating the aforementioned variables, exhibited superior performance than the clinical model or DLCT model in both training set (AUC: 0.875 vs 0.792 vs 0.824) and validation set (AUC: 0.874 vs 0.792 vs 0.779). The DLCT-clinical nomogram demonstrated satisfactory calibration and clinical utility in both training set and validation set. Conclusion The DLCT-clinical nomogram emerges as an effective tool to detect malignant lesions in cytologically indeterminate TNs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2024.1357419
- https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1357419/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399097768
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399097768Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fonc.2024.1357419Digital Object Identifier
- Title
-
Detection of malignant lesions in cytologically indeterminate thyroid nodules using a dual-layer spectral detector CT-clinical nomogramWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-28Full publication date if available
- Authors
-
Xiaofang Ren, Jiayan Zhang, Zuhua Song, Qian Li, Dan Zhang, Xiaojiao Li, Jiayi Yu, Zongwen Li, Youjia Wen, Dan Zeng, Xiaodi Zhang, Zhuoyue TangList of authors in order
- Landing page
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https://doi.org/10.3389/fonc.2024.1357419Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1357419/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1357419/pdfDirect OA link when available
- Concepts
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Nomogram, Medicine, Receiver operating characteristic, Thyroid nodules, Indeterminate, Nuclear medicine, Radiology, Nodule (geology), Thyroid cancer, Thyroid, Internal medicine, Mathematics, Biology, Pure mathematics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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