A predictive model for thyroid follicular carcinoma integrating ultrasound and clinical features

DOI: 10.12201/bmr.202603.00058

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  Abstract: Objective: To explore the diagnostic value of binary multivariate Logistic regression analysis prediction model based on ultrasound and clinical features in follicular thyroid carcinoma ( FTC ).Methods: A total of 107 patients with thyroid follicular tumors who underwent preoperative ultrasound examination and pathological diagnosis in the First Hospital of Quanzhou from April 2020 to August 2025 were collected, including 62 cases of follicular adenoma ( FA ) group and 45 cases of FTC group. The clinical and ultrasonic characteristics of the patients were collected and recorded, and the binary multivariate logistic regression analysis prediction model was constructed. The ROC curve was drawn, and the diagnostic efficacy was evaluated by calculating the area under the curve.Results:Binary Logistic regression analysis showed that age, thyroglobulin ( Tg ), anti-thyroglobulin antibody (TgAb), composition, echo uniformity, halo integrity, halo thickness, and blood flow richness were statistically different between the two groups ( P < 0.05 ). The above indicators were included in the regression analysis prediction model. The regression analysis equation was Logit ( P ) = -5.865-0.057 × age + 0.006 × Tg + 0.012 × TgAb + 2.181 × component + 3.630 × echo uniformity + 4.309 × halo integrity + 4.809 × halo thickness + 2.492 × blood flow richness. The area under the ROC curve of the prediction model was 0.936.Conclusion: The binary multivariate Logistic regression analysis prediction model based on clinical features and ultrasound findings showed good diagnostic efficacy in the diagnosis and differential diagnosis of benign and malignant thyroid follicular tumors.

  Key words: Thyroid follicular carcinoma; ultrasound; prediction model; clinical indicators

  Submit time: 17 March 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2026-03-05	10.12201/bmr.202603.00058V1	Download

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