Sugawara Yasuharu, Tachibana Hidenobu, Kadoya Noriyuki, Kitamura Nozomi, Sawant Amit, Jingu Keiichi
Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan.
Particle Therapy Division, Research Center for Innovative Oncology, National Cancer Center Hospital East, Chiba, Japan.
Med Dosim. 2017;42(4):326-333. doi: 10.1016/j.meddos.2017.07.004. Epub 2017 Aug 9.
We evaluated the accuracy of an in-house program in lung stereotactic body radiation therapy (SBRT) cancer patients, and explored the prognostic factors associated with the accuracy of deformable image registrations (DIRs). The accuracy of the 3 programs which implement the free-form deformation and the B-spline algorithm was compared regarding the structures on 4-dimensional computed tomography (4DCT) image datasets between the peak-inhale and peak-exhale phases. The dice similarity coefficient (DSC) and normalized DSC (NDSC) were measured for the gross tumor volumes from 19 lung SBRT patients. We evaluated the accuracy of DIR using gross tumor volume, magnitude of displacement from 0% phase to 50% phase, whole lung volume in the 50% phase image, and status of tumor pleural attachment. The median NDSC values using the NiftyReg, MIM Maestro and Velocity AI programs were 1.027, 1.005, and 0.946, respectively, indicating that NiftyReg and MIM Maestro programs had similar accuracy with an uncertainty of < 1 mm. Larger uncertainty of 1 to 2 mm was observed using the Velocity AI program. The NiftyReg and the MIM programs provided higher NDSC values than the median values when the gross tumor volume was attached to the pleura (p <0.05). However, it showed a different trend in using the Velocity AI program. All software programs provided unexpected results, and there is a possibility that such results would reduce the accuracy of 4D treatment planning and adaptive radiotherapy. The unexpected results may be because the tumors are surrounded by other tissues, and there are differences regarding the region of interest for rigid and nonrigid registration. Furthermore, our results indicated that the pleural attachment status might be an important predictor of DIR accuracy for thoracic images, indicating that there is a potentially large dose distribution discrepancy concerning 4D treatment planning and adaptive radiotherapy.
我们评估了一个内部程序在肺癌立体定向体部放射治疗（SBRT）患者中的准确性，并探讨了与可变形图像配准（DIR）准确性相关的预后因素。比较了实施自由形式变形和B样条算法的3个程序在4维计算机断层扫描（4DCT）图像数据集上吸气峰值和呼气峰值阶段结构的准确性。对19例肺癌SBRT患者的大体肿瘤体积测量了骰子相似系数（DSC）和归一化DSC（NDSC）。我们使用大体肿瘤体积、从0%相位到50%相位的位移幅度、50%相位图像中的全肺体积以及肿瘤胸膜附着状态评估了DIR的准确性。使用NiftyReg、MIM Maestro和Velocity AI程序的NDSC中位数分别为1.027、1.005和0.946，表明NiftyReg和MIM Maestro程序具有相似的准确性，不确定性<1mm。使用Velocity AI程序观察到1至2mm的较大不确定性。当大体肿瘤体积附着于胸膜时，NiftyReg和MIM程序提供的NDSC值高于中位数（p<0.05）。然而，使用Velocity AI程序时显示出不同的趋势。所有软件程序都给出了意外结果，并且这种结果有可能降低4D治疗计划和自适应放疗的准确性。意外结果可能是因为肿瘤被其他组织包围，并且刚性和非刚性配准的感兴趣区域存在差异。此外，我们的结果表明胸膜附着状态可能是胸部图像DIR准确性的重要预测指标，这表明在4D治疗计划和自适应放疗方面可能存在较大的剂量分布差异。
