Fukuta Kentaro, Shimada Yoshihisa, Nagamatu Yuki, Amemiya Ryosuke, Oomori Tomokazu, Furumoto Hideyuki, Kudo Yujin, Oba Taro, Hagiwara Masaru, Kakihana Masatoshi, Park Jinho, Ohira Tatuso, Ikeda Norihiko
Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan.
Department of Radiology, Tokyo Medical University, Tokyo, Japan.
Eur J Cardiothorac Surg. 2025 Mar 4;67(3). doi: 10.1093/ejcts/ezae449.
The advantages of preoperative three-dimensional (3D) image simulations, which require enhanced computed tomography (ECT), for anatomical lung resection are well documented. However, the necessity for contrast agent presents a significant barrier for some patients. This study thus aims to evaluate the accuracy of an artificial intelligence-based 3D simulation using unenhanced computed tomography (UECT) data in comparison to ECT data.
The study enrolled 18 lung cancer patients who underwent anatomical lung resections. Utilizing the artificial intelligence software Version6.7 within the Synapse Vincent system (Fujifilm Corporation, Tokyo, Japan), automatic construction of 3D images of the bronchovascular trees was achieved using both ECT and UECT. We further assessed the accuracy of pulmonary vessel identification on UECT, and compared the calculated lung segment volumes obtained from UECT with those obtained from ECT.
The comparison of accuracy to operative findings showed that ECT identified 98.9% of artery branches (PAs) and 85.7% of vein branches (PVs), while UECT identified 96.6% of PAs and 82.1% of PVs. Out of 371 PAs and 319 PVs identified on ECT, UECT failed to detect 16 PAs (4.4%) and 32 PVs (10.1%), yielding a correlation coefficient for branch detection of 0.9783 (P < 0.001). There was a significant correlation between ECT and UECT in measuring artery-oriented volumes on both the right-side segments (R = 0.8330) and the left-side segments (R = 0.8082).
This 3D image technique using UECT data may be comparable to that obtained with ECT data in terms of achieving lobar and partial segmental branch levels.
术前三维（3D）图像模拟对于解剖性肺切除具有诸多优势，这在需要增强计算机断层扫描（ECT）的研究中已有充分记录。然而，造影剂的使用对一些患者来说是一个重大障碍。因此，本研究旨在评估基于人工智能的3D模拟使用非增强计算机断层扫描（UECT）数据与ECT数据相比的准确性。
本研究纳入了18例行解剖性肺切除的肺癌患者。利用Synapse Vincent系统（日本东京富士胶片公司）内的人工智能软件版本6.7，通过ECT和UECT自动构建支气管血管树的3D图像。我们进一步评估了UECT上肺血管识别的准确性，并将从UECT获得的计算肺段体积与从ECT获得的进行比较。
与手术结果的准确性比较显示，ECT识别出98.9%的动脉分支（PAs）和85.7%的静脉分支（PVs），而UECT识别出96.6%的PAs和82.1%的PVs。在ECT上识别出的371个PAs和319个PVs中，UECT未能检测到16个PAs（4.4%）和32个PVs（10.1%），分支检测的相关系数为0.9783（P < 0.001）。在测量右侧段（R = 0.8330）和左侧段（R = 0.8082）的动脉导向体积方面，ECT和UECT之间存在显著相关性。
这种使用UECT数据的3D图像技术在实现叶和部分段分支水平方面可能与使用ECT数据获得的技术相当。
