ArticleMedical science monitor : international medical journal of experimental and clinical research2019
A Predictive Model to Distinguish Papillary Thyroid Carcinomas from Benign Thyroid Nodules Using Ultrasonographic Features: A Single-Center, Retrospective Analysis.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Ultrasound characteristics of follicular and parafollicular thyroid neoplasms: diagnostic performance of artificial neural network.Thyroid research · 2023Article
- Contrast-Enhanced Ultrasound in the Differential Diagnosis and Risk Stratification of ACR TI-RADS Category 4 and 5 Thyroid Nodules With Non-Hypovascular.Frontiers in oncology · 2021Article
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Authors and funding
6 authors.
Funding
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Abstract
BACKGROUND We developed a model based on ultrasound (US) features of thyroid nodules and cervical lymph nodes to distinguish papillary thyroid carcinomas (PTC) from benign thyroid nodules. MATERIAL AND METHODS We retrospectively collected data on preoperative ultrasonographic characteristics and postoperative histological data from 1119 patients who underwent thyroidectomy in our center from January 2017 to January 2018. Variables of age, sex, and US features of thyroid nodule and lymph nodes features were analyzed. A logistic regression model was established for PTC prediction. RESULTS Logistic regression analysis confirmed that age under 45 years (OR=2.22, p=0.00), hypoechogenicity (OR=3.70, p=0.00), irregular shape (OR=2.13, p=0.004), ill-defined margin (OR=2.26, p=0.08), spiculate margin (OR=3.30, p=0.00), indefinite border (OR=2.45, p=0.00), capsular invasion (OR=7.76, p=0.006), taller-than-wide shape (OR=2.94, p=0.00), solid structure (OR=2.46, p=0.001), microcalcifications (OR=3.92, p=0.00), coexistence of microcalcifications and macrocalcifications (OR=5.84, p=0.006), and central vascularity (OR=2.10, p=0.001) were independently associated with increased risks for PTC, as well as lymph nodes metastasis features (absence of an echogenic hilum [OR=3.74, p=0.027] and increased vascularization [OR=3.55, p=0.086]). The area under the curve (AUC) for the risk score diagnosis system was 0.916. CONCLUSIONS This predictive model is a reliable, simple, and cost-effective diagnostic tool for PTC.
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