Purpose

To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAF^V600E mutation in thyroid cancer.

Methods

469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0–100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAF^V600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAF^V600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves.

Results

In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAF^V600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAF^V600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAF^V600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004).

Conclusion

Deep learning-based CAD for thyroid US can help us predict the BRAF^V600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.

中文翻译：

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人工智能预测甲状腺癌患者的BRAF ^V600E突变

目的

为了研究在颈部US图像上使用深度学习卷积神经网络（CNN）开发的计算机辅助诊断（CAD）程序是否可以预测甲状腺癌中的BRAF ^V600E突变。

方法

这项回顾性研究包括了469例患者中的469例甲状腺癌。最近使用深层CNN开发的CAD程序提供了恶性肿瘤的风险（0–100％）以及二值化结果（是否有癌症）。使用CAD程序，我们根据每个甲状腺结节的美国图像（CAD值）计算了恶性风险。进行了单因素和多因素logistic回归分析，包括患者人口统计数据，美国放射学院（ACR）甲状腺成像，报告和数据系统（TIRADS）类别以及通过CAD计算的恶性风险，以确定甲状腺BRAF ^V600E突变的独立预测因素癌症。BRAF ^V600E的CAD值和最终多变量模型的预测能力 使用受体工作特征（ROC）曲线下的面积测量甲状腺癌的突变。

结果

在这项研究中，BRAF ^V600E突变为380位（81％）阳性，而89位（19％）阴性。在多变量分析中，BRAF ^V600E突变与年龄较大（OR = 1.025，p = 0.018），体型较小（OR = 0.963，p = 0.006）和较高的CAD值（OR = 1.016，p = 0.004）相关。CAD值得出的BRUC ^V600E突变的AUC为0.646（95％CI：0.576，0.716），而多变量模型得出的AUC为0.706（95％CI：0.576，0.716）。多变量模型显示出比单独的CAD值明显更好的性能（p = 0.004）。

结论

基于深度学习的甲状腺US CAD可帮助我们预测甲状腺癌中的BRAF ^V600E突变。需要更多具有更多案例的多中心研究来进一步验证我们的研究结果。