Radiation Oncology, University of California, San Francisco, USA.
Radiation Oncology, University of California, Los Angeles, USA.
Radiother Oncol. 2024 Nov;200:110514. doi: 10.1016/j.radonc.2024.110514. Epub 2024 Aug 29.
Effective respiratory motion management reduces healthy tissue toxicity and ensures sufficient dose delivery to lung cancer cells in pulmonary stereotactic body radiation therapy (SBRT) with high fractional doses. An articulated robotic arm paired with an X-ray imaging system is designed for real-time motion-tracking (RTMT) dose delivery. However, small tumors (<15 mm) or tumors at challenging locations may not be visible in the X-ray images, disqualifying patients with such tumors from RTMT dose delivery unless fiducials are implanted via an invasive procedure. To track these practically invisible lung tumors in SBRT, we hereby develop a deep learning-enabled template-free tracking framework, SAFE Track.
SAFE Track is a fully supervised framework that trains a generalizable prior for template-free target localization. Two sub-stages are incorporated in SAFE Track, including the initial pretraining on two large-scale medical image datasets (DeepLesion and Node21) followed by fine-tuning on our in-house dataset. A two-stage detector, Faster R-CNN, with a backbone of ResNet50, was selected as our detection network. 94 patients (415 fractions; 40,348 total frames) with low tumor visibility who thus had implanted fiducials were included. The cohort is categorized by the longest dimension of the tumor (<10 mm, 10-15 mm and > 15 mm). The patients were split into training (n = 66) and testing (n = 28) sets. We simulated fiducial-free tumors by removing the fiducials from the X-ray images. We classified the patients into two groups - fiducial implanted inside tumors and implanted outside tumors. To ensure the rigor of our experiment design, we only conducted fiducial removal simulation in training patients and utilized patients with fiducial implanted outside of the tumors for testing. Commercial Xsight Lung Tracking (XLT) and a Deep Match were included for comparison.
SAFE Track achieves promising outcomes to as accurate as 1.23±1.32 mm 3D distance in testing patients with tumor size > 15 mm where Deep Match is at 4.75±1.67 mm and XLT is at 12.23±4.58 mm 3D distance. Even for the most challenging tumor size (<10 mm), SAFE Track maintains its robustness at 1.82 plus or minus 1.67 mm 3D distance, where Deep Match is at 5.32 plus or minus 2.32 mm, and XLT is at 24.83±12.95 mm 3D distance. Moreover, SAFE Track can detect some considerably challenging cases where the tumor is almost invisible or overlapped with dense anatomies.
SAFE Track is a robust, clinically compatible, fiducial-free, and template-free tracking framework that is applicable to patients with small tumors or tumors obscured by overlapped anatomies in SBRT.
在肺部立体定向体部放射治疗（SBRT）中，采用高分次剂量可有效控制呼吸运动，确保将足够的剂量输送至肺癌细胞，同时减少健康组织的毒性。配备 X 射线成像系统的铰接式机械臂被设计用于实时运动跟踪（RTMT）剂量输送。然而，对于直径小于 15 毫米的小肿瘤或位于挑战性部位的肿瘤，X 射线图像可能无法显示，除非通过侵入性手术植入基准标记物，否则这些肿瘤患者将无法接受 RTMT 剂量输送。为了在 SBRT 中跟踪这些实际上不可见的肺部肿瘤，我们开发了一种基于深度学习的无模板跟踪框架，即 SAFE Track。
SAFE Track 是一个完全监督的框架，用于训练无模板目标定位的可推广先验知识。SAFE Track 包含两个子阶段，包括在两个大型医学图像数据集（DeepLesion 和 Node21）上进行初始预训练，然后在我们的内部数据集上进行微调。我们选择了具有 ResNet50 主干的两阶段检测器 Faster R-CNN 作为我们的检测网络。共有 94 名肿瘤可见度低因而植入了基准标记物的患者（415 个分次治疗；共 40348 个总帧数）被纳入研究。该队列根据肿瘤的最长直径分为<10 毫米、10-15 毫米和>15 毫米三组。将患者分为训练集（n=66）和测试集（n=28）。我们通过从 X 射线图像中移除基准标记物来模拟无基准标记物的肿瘤。我们将患者分为两组——肿瘤内植入基准标记物和肿瘤外植入基准标记物。为了确保实验设计的严谨性，我们仅在训练患者中进行基准标记物移除模拟，并利用肿瘤外植入基准标记物的患者进行测试。我们还纳入了商业的 Xsight Lung Tracking（XLT）和 Deep Match 进行比较。
在测试患者中，SAFE Track 取得了有希望的结果，肿瘤直径大于 15 毫米的患者的三维距离误差为 1.23±1.32 毫米，而 Deep Match 的三维距离误差为 4.75±1.67 毫米，XLT 的三维距离误差为 12.23±4.58 毫米。即使对于最具挑战性的肿瘤大小（<10 毫米），SAFE Track 仍保持其稳健性，三维距离误差为 1.82 加或减 1.67 毫米，而 Deep Match 的三维距离误差为 5.32 加或减 2.32 毫米，XLT 的三维距离误差为 24.83±12.95 毫米。此外，SAFE Track 还可以检测到一些相当具有挑战性的病例，其中肿瘤几乎不可见或与密集的解剖结构重叠。
SAFE Track 是一种稳健、临床兼容、无基准标记物和无模板的跟踪框架，适用于 SBRT 中直径较小的肿瘤或被重叠解剖结构遮挡的肿瘤患者。
