Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan.
Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Eur J Radiol. 2022 Sep;154:110445. doi: 10.1016/j.ejrad.2022.110445. Epub 2022 Jul 20.
To assess the clinical effectiveness of temporal subtraction computed tomography (TS CT) using deep learning to improve vertebral bone metastasis detection.
This retrospective study used TS CT comprising bony landmark detection, bone segmentation with a multi-atlas-based method, and spatial registration of two images by a log-domain diffeomorphic Demons algorithm. Paired current and past CT images of 50 patients without vertebral metastasis, recorded during June 2011-September 2016, were included as training data. A deep learning-based method estimated registration errors and suppressed false positives. Thereafter, paired CT images of 40 cancer patients with newly developed vertebral metastases and 40 control patients without vertebral metastases were evaluated. Six board-certified radiologists and five radiology residents independently interpreted 80 paired CT images with and without TS CT.
Records of 40 patients in the metastasis group (median age: 64.5 years; 20 males) and 40 patients in the control group (median age: 64.0 years; 20 males) were evaluated. With TS CT, the overall figure of merit (FOM) of the board-certified radiologist and resident groups improved from 0.848 to 0.876 (p = 0.01) and from 0.752 to 0.799 (p = 0.02), respectively. The sub-analysis focusing on attenuation changes in lesions revealed that the FOM of osteoblastic lesions significantly improved in both the board-certified radiologist and resident groups using TS CT. The sub-analysis focusing on lesion location showed that the FOM of the resident group significantly improved in the vertebral arch (p = 0.04).
TS CT was effective in detecting bone metastasis by both board-certified radiologists and radiology residents.
评估基于深度学习的时间减影 CT（TS CT）在提高椎体骨转移检测中的临床效果。
本回顾性研究使用了包括骨性标志检测、基于多图谱的骨分割以及通过对数域仿射 Demons 算法进行两幅图像空间配准的 TS CT。将 50 例无椎体转移患者在 2011 年 6 月至 2016 年 9 月期间记录的当前和过去的 TS CT 配对图像作为训练数据。基于深度学习的方法估计了配准误差并抑制了假阳性。然后，对 40 例新发生椎体转移的癌症患者和 40 例无椎体转移的对照患者的配对 CT 图像进行了评估。6 名放射学委员会认证的放射科医师和 5 名放射科住院医师独立对 80 对有和无 TS CT 的 CT 图像进行了评估。
对 40 例转移组患者（中位年龄：64.5 岁；男性 20 例）和 40 例对照组患者（中位年龄：64.0 岁；男性 20 例）的记录进行了评估。使用 TS CT，放射学委员会认证的放射科医师和住院医师组的总体符合度（FOM）分别从 0.848 提高到 0.876（p=0.01）和从 0.752 提高到 0.799（p=0.02）。重点关注病变部位衰减变化的亚分析表明，在使用 TS CT 时，两组的成骨病变 FOM 均显著提高。重点关注病变位置的亚分析显示，在椎体弓根处，住院医师组的 FOM 显著提高（p=0.04）。
TS CT 对放射学委员会认证的放射科医师和放射科住院医师均有效，可用于检测骨转移。
