A Pipeline for Automated Voxel Dosimetry: Application in Patients with Multi-SPECT/CT Imaging After 177Lu-Peptide Receptor Radionuclide Therapy
A Pipeline for Automated Voxel Dosimetry: Application in Patients with Multi-SPECT/CT Imaging After 177Lu-Peptide Receptor Radionuclide Therapy
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DOI：
10.2967/jnumed.121.263738
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发表时间：
2022-11-01
影响因子：
9.3
通讯作者：
Nelson, Aaron S.
中科院分区：
文献类型：
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作者：
Dewaraja, Yuni K.;Mirando, David M.;Nelson, Aaron S.
在放射药物治疗（RPT）中，由于缺乏准确和实用的临床工具，患者特异性剂量测定受到阻碍。我们的目标是构建和测试一个集成的体素级管道，该管道自动化RPT剂量测定过程的关键组件（器官分割、配准、剂量率估计和曲线拟合），然后使用它来报告Lu-177-DOTATATE治疗中的患者特异性剂量测定。方法：构建除肿瘤分割外整个剂量测定过程自动化的集成工作流程。首先，使用卷积神经网络（cnn）在治疗后SPECT/CT扫描的CT部分上自动分割器官。其次，基于局部轮廓强度的SPECT-SPECT对齐导致感兴趣体积传播到其他时间点。第三，使用快速剂量计划方法代码通过显式蒙特卡罗（MC）辐射输运估计剂量率。第四，对每个体素自动选择最优的剂量率拟合函数。当报告平均剂量时，我们应用部分体积校正，并通过扰动分割的经验方法估计不确定性。结果：该工作流程使用了来自20例77例神经内分泌肿瘤患者的4个时间点的Lu-177 SPECT/CT成像数据，由放射科医生进行分割。与人工分割相比，cnn定义的肾脏导致高Dice值（0.91-0.94），平均剂量差异很小（2%-5%）。基于轮廓强度的配准可以增强视觉对齐，体素级拟合具有较高的R-2值。不同患者的剂量测定结果差异很大；例如，病变的平均吸收剂量（Gy/GBq）为3.2（范围为0.2-10.4），左肾为0.49（范围为0.24-1.02），右肾为0.54（范围为0.31-1.07），健康肝脏为0.51（范围为0.27-1.04）。患者的结果进一步证明，假设阈值吸收剂量为23 Gy至肾脏和100 Gy至肿瘤所需的周期数具有高度可变性。由于分割的差异，平均剂量的不确定性对于器官平均为6%（范围，3%-17%），对于病变平均为10%（范围，3%-37%）。对于一个典型的患者，整个过程在台式计算机上大约需要25分钟（类似于2分钟的人工时间），包括CNN器官分割、共配准、MC剂量测定和体素曲线拟合的时间。结论：一个集成了快速和自动化的新型工具的流水线为剂量学引导的RPT的临床翻译提供了能力。
Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. Our aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, dose-rate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient-specific dosimetry in Lu-177-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. First, convolutional neural networks (CNNs) are used to automatically segment organs on the CT portion of one posttherapy SPECT/CT scan. Second, local contour intensity-based SPECT-SPECT alignment results in volume-of-interest propagation to other time points. Third, dose rate is estimated by explicit Monte Carlo (MC) radiation transport using the fast, Dose Planning Method code. Fourth, the optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, we apply partial-volume correction, and uncertainty is estimated by an empiric approach of perturbing segmentations. Results: The workflow was used with 4-time-point Lu-177 SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91-0.94) and only small differences (2%-5%) in mean dose when compared with manual segmentation. Contour intensity-based registration led to visually enhanced alignment, and the voxel-level fitting had high R-2 values. Across patients, dosimetry results were highly variable; for example, the average of the mean absorbed dose (Gy/GBq) was 3.2 (range, 0.2-10.4) for lesions, 0.49 (range, 0.24-1.02) for left kidney, 0.54 (range, 0.31-1.07) for right kidney, and 0.51 (range, 0.27-1.04) for healthy liver. Patient results further demonstrated the high variability in the number of cycles needed to deliver hypothetical threshold absorbed doses of 23 Gy to kidney and 100 Gy to tumor. The uncertainty in mean dose, attributable to variability in segmentation, averaged 6% (range, 3%-17%) for organs and 10% (range, 3%-37%) for lesions. For a typical patient, the time for the entire process was about 25 min (similar to 2 min manual time) on a desktop computer, including time for CNN organ segmentation, coregistration, MC dosimetry, and voxel curve fitting. Conclusion: A pipeline integrating novel tools that are fast and automated provides the capacity for clinical translation of dosimetry-guided RPT.