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10 December 2019 : Clinical Research  

A Predictive Model to Distinguish Papillary Thyroid Carcinomas from Benign Thyroid Nodules Using Ultrasonographic Features: A Single-Center, Retrospective Analysis

Da Fang1BCEF, Wenting Ma2DE, Lu Xu3B, Ying Liu1B, Xianghua Ma13AG*, Hui Lu4A

DOI: 10.12659/MSM.917825

Med Sci Monit 2019; 25:9409-9415

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.

Keywords: Carcinoma, Papillary, Logistic Models, Lymph Nodes, Thyroid Nodule, Ultrasonography

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Editorial

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Scientific Editor, Medical Science Monitor, International Scientific Information, Inc., Melville, NY, USA

DOI: 10.12659/MSM.935624

Med Sci Monit 2021; 27:e935624

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750