Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Autor: Chen Z; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong. chenzm27@mail3.sysu.edu.cn., Chambara N; School of Healthcare Sciences, Cardiff University, Cardiff, UK., Lo X; Department of Surgery, North District Hospital, Sheung Shui, New Territories, Hong Kong., Liu SYW; Department of Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong., Gunda ST; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong., Han X; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong., Ying MTC; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong. michael.ying@polyu.edu.hk.
Jazyk: angličtina
Zdroj: Endocrine [Endocrine] 2024 Oct 07. Date of Electronic Publication: 2024 Oct 07.
DOI: 10.1007/s12020-024-04053-2
Abstrakt: Purpose: Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography.
Methods: A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined.
Results: Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%.
Conclusion: The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.
(© 2024. The Author(s).)
Databáze: MEDLINE
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