Structure · dataset · 2026
Data Sheet 2_A malignancy risk prediction model for Bethesda category III thyroid nodules based on serological indicators, C-TIRADS, and BRAF V600E testing.pdf
Listed in Loughborough Research Repository
Description
Objective<p>To investigate the diagnostic value of a multimodal prediction model integrating thyroglobulin antibody (TgAb), thyroid peroxidase antibody (TPOAb), the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS), and BRAF V600E mutation status, for evaluating the malignancy risk in Bethesda Category III thyroid nodules.</p>Methods<p>We retrospectively enrolled 93 patients whose thyroid nodules were diagnosed as Bethesda Category III via fine-needle aspiration and subsequently underwent surgical treatment at Northern Jiangsu People’s Hospital between July 2020 and June 2025.
Preoperative TgAb levels, TPOAb levels, C-TIRADS classification, and BRAF V600E mutation status data were collected. A malignancy risk prediction model was constructed using Firth’s penalized likelihood logistic regression. The diagnostic performance of individual indicators and the combined model was evaluated using receiver operating characteristic (ROC) curves.
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A nomogram was developed for individualized risk prediction, and the model was internally validated using bootstrap resampling (1000 iterations), calibration curves, and decision curve analysis (DCA) to assess the clinical utility.</p>Results<p>Among the 93 Bethesda III thyroid nodules, 84 (90.32%) were malignant. Among the individual predictors, BRAF V600E exhibited the highest diagnostic performance (AUC = 0.736, 95% CI: 0.589–0.884).
Compared with individual modalities, the combined multimodal model demonstrated a significantly improved diagnostic trend, with an AUC of 0.821 (95% CI: 0.653–0.989).</p>Conclusion<p>In a surgically enriched, high-risk cohort of patients with Bethesda III thyroid nodules, the multimodal model integrating TgAb, TPOAb, C-TIRADS, and BRAF V600E testing provides excellent auxiliary diagnostic value and can effectively guide preoperative risk stratification.</p>
Links
Where it is published
- DOI doi.org/10.3389/fendo.2026.1919450 ↗
DOI / persistent id · from repository lboro ac uk
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from repository lboro ac uk
Topics
- From keywords
- Cell metabolism · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Materials Science · Mathematics & Statistics · Medicine & Health · Psychology & Behavioral Science · Social Science
- Inferred from text
- Imaging 75%
Related
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- Possibly the same asTable 1_A malignancy risk prediction model for Bethesda category III thyroid nodules based on serological indicators, C-TIRADS, and BRAF V600E testing.csv
- Possibly the same asTable 1_A malignancy risk prediction model for Bethesda category III thyroid nodules based on serological indicators, C-TIRADS, and BRAF V600E testing.csv
- Possibly the same asTable 2_A malignancy risk prediction model for Bethesda category III thyroid nodules based on serological indicators, C-TIRADS, and BRAF V600E testing.csv
- Possibly the same asData Sheet 1_A malignancy risk prediction model for Bethesda category III thyroid nodules based on serological indicators, C-TIRADS, and BRAF V600E testing.csv
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- Possibly the same asData Sheet 2_A malignancy risk prediction model for Bethesda category III thyroid nodules based on serological indicators, C-TIRADS, and BRAF V600E testing.pdf
Provenance · 1 source records, 19 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Loughborough Research Repository | oai:figshare.com:article/33979756 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
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| concepts[field].local:field:chemistry | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:materials-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:mathematics-statistics | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[modality].local:modality:imaging | enrichment · repository lboro ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | /metadata/dc/description |
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| publication_date | source · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| title | source · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | /metadata/dc/title |