目的:深度学习算法在简化困难的临床决策方面显示出潜力。在本研究中,我们报告了一种深度学习模型在鉴别甲状腺乳头状癌患者恶性和良性淋巴结方面的诊断概况.
方法:开发了一种内部基于深度学习的模型,称为“CrymNet”,并使用包含195个恶性淋巴结和178个良性淋巴结的超声图像的两个数据集进行了测试。放射科专家还查看了这些超声图像,并提取了常规临床实践中使用的定性成像特征。这些标志被用来训练三种不同的机器学习算法。然后将深度学习模型与包含22和82个恶性淋巴结以及20和76个良性淋巴结的内部和外部验证数据集上的机器学习模型进行比较。分别。
结果:在三种机器学习算法中,支持向量机模型(SVM)表现最好,灵敏度达到91.35%,特异性88.54%,精度为90.00%,所有队列的曲线下面积(AUC)为0.925。CryphNet在内部和外部验证中的性能优于SVM协议,灵敏度达到93.27%,特异性92.71%,准确率为93.00%,所有队列的AUC为0.948。
结论:用超声图像训练的深度学习模型优于三种常规机器学习算法,这些算法具有放射科医师解释的定性成像特征。我们的研究提供了有关CrymNet在早期和准确区分良性和恶性淋巴结病中的实用性的证据。
OBJECTIVE: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present
study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer.
METHODS: An in-house deep learning-based model called \"ClymphNet\" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively.
RESULTS: Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts.
CONCLUSIONS: A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our
study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.