Division of Central Radiology, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata, 951-8566, Japan.
Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, Niigata, Japan.
Int J Comput Assist Radiol Surg. 2022 Sep;17(9):1651-1661. doi: 10.1007/s11548-022-02694-0. Epub 2022 Jun 28.
Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN).
This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence.
The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86.
We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.
尽管手术是治疗肺癌的主要手段，但仍有部分患者会出现一定程度的复发。如果能够在开始治疗前及早预测术后复发情况，就有可能为患者提供个体化治疗。因此，本研究提出了一种使用深度卷积神经网络（DCNN）从肺腺癌患者手术前获得的 CT 图像预测术后复发的计算机辅助诊断（CAD）系统。
这是一项回顾性研究，共纳入 150 例接受根治性手术治疗的原发性肺腺癌患者。为了创建原始图像，从术前增强 CT 图像中裁剪出肿瘤部分。通过数据扩充，将 DCNN 的输入图像数量增加到 3000 张。我们使用预先训练的 VGG19 模型进行迁移学习构建了 CAD 系统。进行了五次十折交叉验证。平均识别率为 0.5 或更高的病例被确定为复发。
中位随访时间为 73.2 个月。性能评估结果表明，该方法的灵敏度、特异度和准确率分别为 0.75、0.87 和 0.82，受试者工作特征曲线下面积为 0.86。
我们证明了 DCNN 在使用术前 CT 图像预测肺腺癌术后复发方面的有效性。由于我们的方法仅使用 CT 图像，我们认为它具有能够在术前和非侵入性地对每个患者的术后复发情况进行评估的优势。
