A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules.
A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules.
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DOI：
10.3389/fonc.2021.611436
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发表时间：
2021
影响因子：
4.7
通讯作者：
Chen Z
中科院分区：
文献类型：
--
作者：
Liang X;Huang Y;Cai Y;Liao J;Chen Z
采用全自动AI Sonic计算机辅助设计（CAD）系统检测和诊断甲状腺结节的良恶性。本研究的目的是研究AI Sonic CAD系统的效率，并使用深度学习算法来提高超声引导细针穿刺（FNA）的诊断准确性。从124名患者中收集了138个甲状腺结节，并由专家、新手和甲状腺成像报告和数据系统（TI-RADS）进行诊断。比较了专家、新手和CAD系统的诊断效率和可行性。应用CAD系统，以提高新手的诊断效率进行了评估。此外，以专家经验为金标准，对CAD系统检测出的特征值进行了分析。比较专家、新手和CAD系统的FNA效率，以确定CAD系统是否有助于FNA的管理。总共从124名患者（平均年龄，46.4 ± 12.1岁;范围，12-70岁）中收集了56个恶性甲状腺结节和82个良性甲状腺结节。CAD系统、专家和新手的诊断曲线下面积分别为0.919、0.891和0.877（p < 0.05）。良、恶性结节的边界和成分特征无显著性差异（p > 0.05），而回声强度和是否存在回声灶有显著性差异（p < 0.05）。对于FNA的推荐，结果显示，CAD系统的性能优于专家和新手（p < 0.05）。甲状腺结节的准确诊断和细针穿刺的建议仍是研究的热点。基于深度学习的CAD系统对甲状腺结节的诊断具有更高的准确性和可行性，有助于避免不必要的FNA。CAD系统可能是诊断和无症状筛查的有效辅助方法，特别是在发展中地区。
The fully automatic AI-Sonic computer-aided design (CAD) system was employed for the detection and diagnosis of benign and malignant thyroid nodules. The aim of this study was to investigate the efficiency of the AI-Sonic CAD system with the use of a deep learning algorithm to improve the diagnostic accuracy of ultrasound-guided fine-needle aspiration (FNA). A total of 138 thyroid nodules were collected from 124 patients and diagnosed by an expert, a novice, and the Thyroid Imaging Reporting and Data System (TI-RADS). Diagnostic efficiency and feasibility were compared among the expert, novice, and CAD system. The application of the CAD system to enhance the diagnostic efficiency of novices was assessed. Moreover, with the experience of the expert as the gold standard, the values of features detected by the CAD system were also analyzed. The efficiency of FNA was compared among the expert, novice, and CAD system to determine whether the CAD system is helpful for the management of FNA. In total, 56 malignant and 82 benign thyroid nodules were collected from the 124 patients (mean age, 46.4 ± 12.1 years; range, 12–70 years). The diagnostic area under the curve of the CAD system, expert, and novice were 0.919, 0.891, and 0.877, respectively (p < 0.05). In regard to feature detection, there was no significant differences in the margin and composition between the benign and malignant nodules (p > 0.05), while echogenicity and the existence of echogenic foci were of great significance (p < 0.05). For the recommendation of FNA, the results showed that the CAD system had better performance than the expert and novice (p < 0.05). Precise diagnosis and recommendation of FNA are continuing hot topics for thyroid nodules. The CAD system based on deep learning had better accuracy and feasibility for the diagnosis of thyroid nodules, and was useful to avoid unnecessary FNA. The CAD system is potentially an effective auxiliary approach for diagnosis and asymptomatic screening, especially in developing areas.
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影响因子：
254.7
作者：
Jemal, Ahmedin;Siegel, Rebecca;Thun, Michael J.
通讯作者：
Thun, Michael J.
影响因子：
2.9
作者：
Chen, Kuen-Yuan;Chen, Chiung-Nien;Chang, King-Jen
通讯作者：
Chang, King-Jen
使用卷积神经网络基于超声图像的甲状腺结节自动分割
DOI：
10.1007/s11548-017-1649-7
发表时间：
2017-11-01
影响因子：
3
作者：
Ma, Jinlian;Wu, Fa;Kong, Dexing
通讯作者：
Kong, Dexing
影响因子：
3.8
作者：
Sun, Chao;Zhan, Yukang;Niu, Lijuan
通讯作者：
Niu, Lijuan
影响因子：
3.8
作者：
McIver, B;Hay, ID;Goellner, JR
通讯作者：
Goellner, JR