Wang Yubo, Lin Qiang, Zhao Shaofang, Zeng Xianwu, Zheng Bowen, Cao Yongchun, Man Zhengxing
Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China.
Curr Med Imaging. 2024 Jan 19. doi: 10.2174/0115734056281578231212104108.
Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images.
To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model.
A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis).
Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively.
The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of Tc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.
当实体瘤侵犯骨骼时，癌症患者会发生骨转移，骨骼是继肺和肝之后转移性癌症第三大最常受累的部位。骨转移的早期检测对于做出恰当的治疗决策和提高生存率至关重要。深度学习作为机器学习的一个主流分支，已迅速成为分析医学图像的有效方法。
在这项工作中，为了利用骨闪烁显像自动诊断骨转移，我们提议通过开发基于深度学习的自动分类模型，将骨转移诊断问题转化为自动图像分类。
提出了一个由特征提取子网络和特征分类子网络组成的自定义卷积神经网络，用于自动检测肺癌骨转移，其中特征提取子网络从SPECT骨闪烁显像中提取分层特征，特征分类子网络将高级特征分为两类（即有转移和无转移的图像）。
使用SPECT骨闪烁显像的临床数据，对所提出的模型进行评估以检验其检测准确性。如果对每位患者采集的两幅图像（即前后位扫描）在排除膀胱的图像上使用逐像素加法运算进行融合，则可获得最佳性能，准确率、精确率、召回率、特异性、F1分数和AUC值分别达到最佳分数0.8038、0.8051、0.8039、0.8039、0.8036和0.8489。
与现有的经典深度学习模型相比，所提出的二分类网络能够以最佳性能预测图像是否包含肺癌骨转移。膀胱中高浓度的锝亚甲基二膦酸盐对骨转移的自动诊断有负面影响。建议在自动分析前去除膀胱。
