Graduate School of Biomedical Science and Engineering, School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
BMC Cancer. 2020 Mar 17;20(1):227. doi: 10.1186/s12885-020-6694-x.
As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal.
This retrospective study investigated 3485 sequential patients with malignant or suspected malignant disease, who underwent whole-body FDG PET/CT at our institute. All the cases were classified into the 3 categories by a nuclear medicine physician. A residual network (ResNet)-based CNN architecture was built for classifying patients into the 3 categories. In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region).
There were 1280 (37%), 1450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In the patient-based analysis, CNN predicted benign, malignant and equivocal images with 99.4, 99.4, and 87.5% accuracy, respectively. In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively.
The CNN-based system reliably classified FDG PET images into 3 categories, indicating that it could be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.
随着 PET/CT 扫描仪数量的增加以及 FDG PET/CT 成为肿瘤学的常用成像方式，对人工智能 (AI) 自动检测系统的需求迅速增长，以防止人为疏忽和误诊。我们旨在开发一种基于卷积神经网络 (CNN) 的系统，该系统可以将全身 FDG PET 分类为 1)良性、2)恶性或 3)不确定。
这项回顾性研究调查了在我们机构接受全身 FDG PET/CT 检查的 3485 例恶性或疑似恶性疾病的连续患者。所有病例均由核医学医师分为 3 类。建立了基于残差网络 (ResNet) 的 CNN 架构，用于将患者分为 3 类。此外，我们对 CNN 进行了基于区域的分析（头颈部、胸部、腹部和盆腔区域）。
分别有 1280 例（37%）、1450 例（42%）和 755 例（22%）患者被归类为良性、恶性和不确定。在患者层面的分析中，CNN 对良性、恶性和不确定的图像预测准确率分别为 99.4%、99.4%和 87.5%。在基于区域的分析中，预测正确率分别为头颈部 97.3%、胸部 96.6%、腹部 92.8%和盆腔区域 99.6%。
基于 CNN 的系统可靠地将 FDG PET 图像分为 3 类，表明它可以作为一种双重检查系统，有助于医生防止疏忽和误诊。
