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.

中文翻译：

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基于卷积神经网络的系统，用于使用FDG PET / CT检查对患者进行分类

随着PET / CT扫描仪数量的增加以及FDG PET / CT成为肿瘤学的常见成像方式，对用于防止人为疏忽和误诊的人工智能（AI）自动化检测系统的需求正在迅速增长。我们旨在开发基于卷积神经网络（CNN）的系统，该系统可以将全身FDG PET分为1）良性，2）恶性或3）模棱两可。这项回顾性研究调查了3485例在我院接受了全身FDG PET / CT手术的恶性或疑似恶性疾病患者。核医学医师将所有病例分为3类。构建了基于残差网络（ResNet）的CNN架构，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，胸部，腹部，和骨盆区域）。分别被分为良性，恶性和模棱两可的患者分别为1280（37％），1450（42％）和755（22％）。在基于患者的分析中，CNN分别以99.4、99.4和87.5％的准确性预测良性，恶性和模棱两可的图像。在基于区域的分析中，预测是正确的，概率分别为97.3％（头颈部），96.6％（胸部），92.8％（腹部）和99.6％（骨盆区域）。基于CNN的系统将FDG PET图像可靠地分为3类，这表明它可以作为医生进行双重检查的系统，以防止监督和误诊。准确度分别为99.4和87.5％。在基于区域的分析中，预测是正确的，概率分别为97.3％（头颈部），96.6％（胸部），92.8％（腹部）和99.6％（骨盆区域）。基于CNN的系统将FDG PET图像可靠地分为3类，这表明它可以作为医生进行双重检查的系统，以防止监督和误诊。准确度分别为99.4和87.5％。在基于区域的分析中，预测是正确的，概率分别为97.3％（头颈部），96.6％（胸部），92.8％（腹部）和99.6％（骨盆区域）。基于CNN的系统将FDG PET图像可靠地分为3类，这表明它可以作为医生进行双重检查的系统，以防止监督和误诊。