甲状腺多式成像深度学习的诊断价值为TI-RADS 3-5类分类类别的甲状腺成像
Tingting Qian1,2, Xuhan Feng3, Yahan Zhou2,4,5
1Graduate School, The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310014, China.
Endocrine
|March 8, 2025
概括
深度学习与超声波和计算机断层扫描 (CT) 结合,准确地分类甲状腺结节 (TI-RADS 3-5),提高诊断准确度,并可能减少不必要的活检.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 作为TI-RADS 3-5分类的甲状腺结节具有恶性瘤的可变风险.
- 精细针吸收 (FNA) 活检虽然是诊断性的,但会带来假阴性和并发症的风险.
- 避免不必要的活检对于患者管理至关重要.
研究的目的:
- 开发和评估一种深度学习模型,用于区分良性和恶性甲状腺结节 (TI-RADS 3-5).
- 评估结合超声波和CT成像与深度学习用于甲状腺结节分类的有效性.
- 将深度学习模型的诊断性能与放射科医生的手动解释进行比较.
主要方法:
- 分析了768个甲状腺结节 (TI-RADS 3-5) 的数据集,这些结节具有确定的术后病理.
- 深度学习模型使用超声波数据,CT数据和这两种模式的组合进行训练.
- 使用曲线下面积 (AUC),灵敏度,准确度和正预测值 (PPV) 来评估性能,并与手动读数进行比较.
主要成果:
- 使用超声波和CT的综合深度学习模型与单独的超声波 (0.901) 或单独的CT (0.776) 相比,实现了显著更高的AUC (0.930).
- 深度学习方法在超声波 (AUC平均值为0.725) 和CT (AUC平均值为0.617) 两方面都超过了放射科医生的手动评估.
- 综合深度学习方法在TI-RADS 3-5类别内对甲状腺结节进行分类时表现出卓越的准确性和精确性.
结论:
- 深度学习与超声波和CT成像相结合,为分类甲状腺结节提供了一个高度准确的方法 (TI-RADS 3-5).
- 这种先进的方法显示了提高诊断精度和减少像FNA活检这样的侵入性手术的需要的潜力.
- 综合深度学习模型代表了对甲状腺结节恶性瘤风险的非侵入性评估的重大进步.
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