Hsieh Te-Chun, Liao Chiung-Wei, Lai Yung-Chi, Law Kin-Man, Chan Pak-Ki, Kao Chia-Hung
Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404, Taiwan.
Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan.
J Pers Med. 2021 Nov 24;11(12):1248. doi: 10.3390/jpm11121248.
Patients with bone metastases have poor prognoses. A bone scan is a commonly applied diagnostic tool for this condition. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation, which indicates all-cause bone remodeling. The current study evaluated deep learning techniques to improve the efficacy of bone metastasis detection on bone scans, retrospectively examining 19,041 patients aged 22 to 92 years who underwent bone scans between May 2011 and December 2019. We developed several functional imaging binary classification deep learning algorithms suitable for bone scans. The presence or absence of bone metastases as a reference standard was determined through a review of image reports by nuclear medicine physicians. Classification was conducted with convolutional neural network-based (CNN-based), residual neural network (ResNet), and densely connected convolutional networks (DenseNet) models, with and without contrastive learning. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. A total of 37,427 image sets were analyzed. The overall performance of all models improved with contrastive learning. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and negative predictive value (NPV) for the optimal model were 0.961, 0.878, 0.599, 0.712, 0.92 and 0.965, respectively. In particular, the high NPV may help physicians safely exclude bone metastases, decreasing physician workload, and improving patient care.
骨转移患者预后较差。骨扫描是针对这种情况常用的诊断工具。然而，其准确性受到放射性药物聚集非特异性特征的限制，这种聚集表明了各种原因导致的骨重塑。本研究评估了深度学习技术以提高骨扫描中骨转移检测的效能，回顾性研究了2011年5月至2019年12月期间接受骨扫描的19041例年龄在22至92岁之间的患者。我们开发了几种适用于骨扫描的功能成像二元分类深度学习算法。通过核医学医生对图像报告的审查来确定是否存在骨转移作为参考标准。使用基于卷积神经网络（CNN）、残差神经网络（ResNet）和密集连接卷积网络（DenseNet）模型进行分类，有无对比学习。每组骨扫描包含分辨率为1024×256像素的前后位图像。共分析了37427个图像集。所有模型的整体性能通过对比学习得到了提高。最佳模型的准确率、精确率、召回率、F1分数、受试者操作特征曲线下面积和阴性预测值（NPV）分别为0.961、0.878、0.599、0.712、0.92和0.965。特别是，高NPV可能有助于医生安全地排除骨转移，减轻医生工作量，并改善患者护理。
