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改进甲状腺结节的诊断策略:基于人工智能的计算机辅助诊断系统与剪切波弹性成像的结合。

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

作者信息

Chen Ziman, Chambara Nonhlanhla, Lo Xina, Liu Shirley Yuk Wah, Gunda Simon Takadiyi, Han Xinyang, Ying Michael Tin Cheung

机构信息

Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.

School of Healthcare Sciences, Cardiff University, Cardiff, UK.

出版信息

Endocrine. 2025 Feb;87(2):744-757. doi: 10.1007/s12020-024-04053-2. Epub 2024 Oct 7.

Abstract

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.

摘要

目的

甲状腺结节在普通人群中极为常见,在准确区分良性和恶性病例方面构成临床挑战。本研究旨在探讨不同策略的诊断性能,利用计算机辅助诊断系统(AmCAD)和剪切波弹性成像(SWE)成像相结合的方法,在超声检查中有效区分甲状腺良恶性结节。

方法

本研究前瞻性纳入了126个经病理证实的甲状腺结节。使用AmCAD分析结节的超声成像特征,同时在甲状腺横切和纵切扫描中采用SWE测量其硬度。通过结合AmCAD诊断和SWE值形成了12种诊断模式,包括孤立、系列、平行和整合。使用受试者操作特征曲线和曲线下面积(AUC)评估诊断性能。还确定了敏感性、特异性、准确性、漏诊恶性率和不必要活检率。

结果

各种诊断方案在诊断性能方面显示出特定优势。总体而言,在横切扫描中将AmCAD与SWE成像相结合产生了最有利的诊断性能,AUC为72.2%(95%置信区间(CI):63.0 - 81.5%),优于其他诊断方案。此外,在直径<2 cm或2 - 4 cm结节的亚组分析中,整合方案始终表现出良好的诊断性能,AUC分别为74.2%(95% CI:61.9 - 86.4%)和77.4%(95% CI:59.4 - 95.3%),超过其他诊断方案。整合方案还通过将漏诊恶性率降至9.5%和不必要活检率降至22.2%,有效地解决了甲状腺结节的管理问题。

结论

在甲状腺横切扫描中整合AmCAD和SWE成像可显著提高区分甲状腺良恶性结节的诊断性能。该策略为临床医生提供了获得更准确临床诊断并就患者管理做出明智决策的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d92e/11811255/4f829838952c/12020_2024_4053_Fig1_HTML.jpg

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