Validation of the collapsed-cone superposition for whole-body patient-specific dosimetry in [177Lu]Lu-PSMA-617 radionuclide therapy

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-09-03 DOI:10.1002/mp.18076

Aya Terro, Solène Perret, Arthur Dumouchel, David Tonnelet, Agathe Edet-Sanson, Pierre Vera, Pierre Decazes, Arnaud Dieudonné

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引用次数: 0

Abstract

Background

Patient-specific dosimetry in radiopharmaceutical therapy (RPT) offers a promising approach to optimize the balance between treatment efficacy and toxicity. The introduction of 360° CZT gamma cameras enables the development of personalized dosimetry studies using whole-body single photon emission computed tomography and computed tomography (SPECT/CT) data.

Purpose

This study proposes to validate the collapsed-cone superposition (CCS) approach against Monte Carlo (MC) simulations for whole-body dosimetry of [177Lu]Lu-PSMA-617 therapy in patients with metastatic castration resistant prostate cancer (mCRPC).

Materials and methods

Thirty patients with mCRPC were retrospectively included in this study. SPECT/CT images were acquired after the infusion of [177Lu]Lu-PSMA-617 therapy. SimpleDose was used to generate dose-rate maps (mGy/h) from a single SPECT/CT scan. The dosimetry relies on the CCS approach, which adjusts dose-point kernels according to tissue densities. Organ and lesion delineation were automated using the nnU-Net V2 neural network. MC simulations were performed with GATE 10 for 10⁸ events. To assess the impact of density-scaled DPK on the accuracy of the dosimetry, we implement a simplified version of CCS, denoted as $C C S_{S T}$ , which assumes a homogeneous soft tissue medium without incorporating the patient-specific density information derived from the CT image. The comparison between CCS, $C C S_{S T}$ and MC was conducted at the organ, lesion, and voxel levels.

Results

Absolute percentage errors (APE) between CCS and MC were < 5% for all organs and lesions. Compared to CCS, $C C S_{S T}$ exhibited higher APE with respect to MC in the liver, lungs, salivary glands, and lesions, while lower errors were observed in the bone marrow, kidneys, and pancreas, with comparable performance in the spleen. Voxel-level errors were mostly < 2% for both methods CCS and $C C S_{S T}$ . Median computation time was, respectively, 24.5 s, 46.45 s, and 6.8 h for CCS, $C C S_{S T}$ , and MC.

Conclusion

CCS showed high agreement with MC with greater computational efficiency, demonstrating its clinical potential for whole-body dosimetry.

Abstract Image

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[177Lu]Lu-PSMA-617放射性核素治疗中患者特异性全身剂量测定的塌锥叠加验证

背景：放射药物治疗（RPT）中的患者特异性剂量测定为优化治疗疗效和毒性之间的平衡提供了一种很有前途的方法。360°CZT伽马相机的引入使使用全身单光子发射计算机断层扫描和计算机断层扫描（SPECT/CT）数据的个性化剂量学研究的发展成为可能。目的：本研究旨在验证坍塌锥叠加（CCS）方法与蒙特卡罗（MC）模拟的对比，以用于转移性去势抵抗性前列腺癌（mCRPC）患者[177Lu]Lu-PSMA-617治疗的全身剂量测定。材料与方法回顾性分析30例mCRPC患者。输注[177Lu]Lu-PSMA-617治疗后获得SPECT/CT图像。SimpleDose用于生成单次SPECT/CT扫描的剂量率图（mGy/h）。剂量测定依赖于CCS方法，该方法根据组织密度调整剂量点核。使用nnU-Net V2神经网络自动描绘器官和病变。使用GATE 10对108个事件进行MC模拟。为了评估密度标度DPK对剂量学准确性的影响，我们实施了一个简化版的CCS，表示CC S ST $CC{S}_{ST}$，它假设一个均匀的软组织介质，不包含来自CT图像的患者特异性密度信息。在脏器、病变处比较CCS、C - C - S - S - T $CC{S}_{ST}$和MC。体素水平。结果CCS和MC在所有脏器和病变上的绝对误差（APE）为5%。与CCS相比，CCS S ST $CC{S}_{ST}$在肝脏、肺、唾液腺、虽然在骨髓、肾脏和胰腺中观察到较低的误差，但在脾脏中也有类似的表现。CCS和CCS ST $CC{S}_{ST}$的体素级误差均为<； 2%。CCS、CC s s T $CC{s}_{ST}$的计算时间中位数分别为24.5 s、46.45 s和6.8 h；结论CCS与MC的一致性高，计算效率高，显示了其在全身剂量学中的临床应用潜力。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.