Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region
Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region
复制标题
使用超声图像预测乳腺癌腋窝淋巴结转移的深度学习与放射组学：不要忘记瘤周区域
DOI：
10.3389/fonc.2020.00053
复制
发表时间：
2020-01-31
影响因子：
4.7
通讯作者：
Li, Zhi-Cheng
中科院分区：
文献类型：
--
作者：
Sun, Qiuchang;Lin, Xiaona;Li, Zhi-Cheng
目的：乳腺癌腋窝淋巴结（ALN）转移状况对指导治疗具有重要意义。目的是评估与放射组学分析相比，深度卷积神经网络（CNN）在使用乳腺超声预测ALN转移方面的表现，并研究肿瘤内和肿瘤周围区域在ALN转移预测中的价值。方法：我们回顾性纳入了479例乳腺癌患者的2，395个乳腺超声图像。基于肿瘤内、肿瘤周围以及组合的肿瘤内和肿瘤周围区域，使用DenseNet构建了三个CNN，并分别使用随机森林构建了三个放射组学模型。通过结合分子亚型，建立了另外三个CNN和三个放射组学模型。所有模型均建立在训练队列（343例患者1，715张图像）上，并使用ROC分析对测试队列（136例患者680张图像）进行评估。招募了另一个16名患者的前瞻性队列，以进一步测试模型。结果如下：在两个训练/测试队列中，仅图像CNN的AUC对于组合区域为0.957/0.912，对于瘤周区域为0.944/0.775，对于瘤内区域为0.937/0.748，其在数值上高于其相应的放射组学模型，AUC为0.940/0.886，0.920/0.724和0.913/0.693。图像分子CNN在训练/测试队列中的AUC方面的整体性能分别略微增加至0.962/0.933、0.951/0.813和0.931/0.794。在测试队列中，构建在组合区域上的CNN和放射组学模型的AUC均显著优于肿瘤内或肿瘤周围区域的AUC（p < 0.05）。在前瞻性研究中，基于组合区域构建的CNN模型在所有仅图像模型中达到了最高的AUC 0.95。结论：在预测乳腺癌ALN转移方面，CNN在数值上显示出比放射组学模型更好的整体性能。对于CNN和放射组学模型，结合肿瘤内和肿瘤周围区域实现了显著更好的性能。
Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models. Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.