Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
Department of Diagnostic Radiology, Toranomon Hospital, 2-2-2 Toranomon, Minato-ku, Tokyo, 105-8470, Japan.
BMC Med Imaging. 2022 Apr 30;22(1):80. doi: 10.1186/s12880-022-00808-3.
To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions.
This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs.
The AUC of the CNNs pertaining to the single and combined image sets were 0.88-0.95 and 0.87-0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88-0.95.
CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.
比较使用卷积神经网络（CNN）的深度学习模型与放射科医生在诊断子宫内膜癌方面的诊断性能，并验证合适的成像条件。
本回顾性研究纳入了 2015 年至 2020 年间接受 MRI 检查的患有子宫内膜癌或非癌性病变的患者。在实验 1 中，使用 204 例癌症患者和 184 例非癌性病变患者的多个序列的单一和组合图像集来训练 CNN。随后，使用 51 例癌症患者和 46 例非癌性病变患者的 97 张图像进行测试。由三位盲法放射科医生独立解读测试图像集。实验 2 研究了使用单一图像集训练时添加不同类型的图像是否可以提高 CNN 的诊断性能。
CNN 对单一和组合图像集的 AUC 分别为 0.88-0.95 和 0.87-0.93，表明其诊断性能不逊于放射科医生。将其他类型的单一图像添加到单一图像集中训练的 CNN 的 AUC 为 0.88-0.95。
使用 MRI 进行诊断时，CNN 对子宫内膜癌的诊断具有较高的性能。尽管没有显著差异，但添加其他类型的图像可以提高某些单一图像集的诊断性能。
