PDF(Pubmed)
Abstract：
OBJECTIVE: Treatment planning through the diagnostic dimension of theranostics provides insights into predicting the absorbed dose of RPT, with the potential to individualize radiation doses for enhancing treatment efficacy. However, existing studies focusing on dose prediction from diagnostic data often rely on organ-level estimations, overlooking intra-organ variations. This study aims to characterize the intra-organ theranostic heterogeneity and utilize artificial intelligence techniques to localize them, i.e. to predict voxel-wise absorbed dose map based on pre-therapy PET.
METHODS: 23 patients with metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA I&T RPT were retrospectively included. 48 treatment cycles with pre-treatment PET imaging and at least 3 post-therapeutic SPECT/CT imaging were selected. The distribution of PET tracer and RPT dose was compared for kidney, liver and spleen, characterizing intra-organ heterogeneity differences. Pharmacokinetic simulations were performed to enhance the understanding of the correlation. Two strategies were explored for pre-therapy voxel-wise dosimetry prediction: (1) organ-dose guided direct projection; (2) deep learning (DL)-based distribution prediction. Physical metrics, dose volume histogram (DVH) analysis, and identity plots were applied to investigate the predicted absorbed dose map.
RESULTS: Inconsistent intra-organ patterns emerged between PET imaging and dose map, with moderate correlations existing in the kidney (r = 0.77), liver (r = 0.5), and spleen (r = 0.58) (P < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R2 = 0.92 for kidney). The DL model improved the mean slope of fitting lines in identity plots (199% for liver), when compared to the theoretical optimal results of the organ-dose approach.
CONCLUSIONS: Our results demonstrated the intra-organ heterogeneity of pharmacokinetics may complicate pre-therapy dosimetry prediction. DL has the potential to bridge this gap for pre-therapy prediction of voxel-wise heterogeneous dose map.
METHODS: 23 patients with metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA I&T RPT were retrospectively included. 48 treatment cycles with pre-treatment PET imaging and at least 3 post-therapeutic SPECT/CT imaging were selected. The distribution of PET tracer and RPT dose was compared for kidney, liver and spleen, characterizing intra-organ heterogeneity differences. Pharmacokinetic simulations were performed to enhance the understanding of the correlation. Two strategies were explored for pre-therapy voxel-wise dosimetry prediction: (1) organ-dose guided direct projection; (2) deep learning (DL)-based distribution prediction. Physical metrics, dose volume histogram (DVH) analysis, and identity plots were applied to investigate the predicted absorbed dose map.
RESULTS: Inconsistent intra-organ patterns emerged between PET imaging and dose map, with moderate correlations existing in the kidney (r = 0.77), liver (r = 0.5), and spleen (r = 0.58) (P < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R2 = 0.92 for kidney). The DL model improved the mean slope of fitting lines in identity plots (199% for liver), when compared to the theoretical optimal results of the organ-dose approach.
CONCLUSIONS: Our results demonstrated the intra-organ heterogeneity of pharmacokinetics may complicate pre-therapy dosimetry prediction. DL has the potential to bridge this gap for pre-therapy prediction of voxel-wise heterogeneous dose map.
摘要：
目的：治疗计划通过治疗药物的诊断维度提供了预测RPT吸收剂量的见解，具有个性化辐射剂量以增强治疗效果的潜力。然而,现有的研究侧重于从诊断数据中进行剂量预测，通常依赖于器官水平的估计，忽略器官内的变化。本研究旨在表征器官内治疗异质性，并利用人工智能技术对其进行定位，即基于治疗前PET预测逐体素吸收剂量图。
方法：回顾性纳入23例接受[177Lu]Lu-PSMAI&TRPT治疗的转移性去势抵抗性前列腺癌患者。选择具有治疗前PET成像和至少3个治疗后SPECT/CT成像的48个治疗周期。比较肾脏的PET示踪剂和RPT剂量的分布，肝脏和脾脏,表征器官内异质性差异。进行药代动力学模拟以增强对相关性的理解。探索了两种用于治疗前逐体素剂量测定预测的策略:(1)器官剂量引导的直接投影;(2)基于深度学习(DL)的分布预测。物理指标，剂量体积直方图（DVH）分析，和身份图被用来研究预测的吸收剂量图。
结果：PET成像和剂量图之间出现了不一致的器官内模式，肾脏中存在中度相关性（r=0.77），肝脏（r=0.5），脾(r=0.58)(P<0.025)。模拟结果表明，器官内药代动力学异质性可能解释了这种不一致性。基于DL的方法实现了较低的平均按体素归一化均方根误差为0.79±0.27%，关于地面真相剂量图，优于器官剂量引导投影（1.11±0.57％）（P<0.05）。DVH分析显示了良好的预测准确性（肾脏的R2=0.92）。DL模型改善了同一性图中拟合线的平均斜率（肝脏为199％），与器官剂量方法的理论最佳结果相比。
结论：我们的研究结果表明，药物动力学的器官内异质性可能会使治疗前剂量学预测复杂化。DL有可能弥合这一差距，用于逐体素异质剂量图的治疗前预测。
方法：回顾性纳入23例接受[177Lu]Lu-PSMAI&TRPT治疗的转移性去势抵抗性前列腺癌患者。选择具有治疗前PET成像和至少3个治疗后SPECT/CT成像的48个治疗周期。比较肾脏的PET示踪剂和RPT剂量的分布，肝脏和脾脏,表征器官内异质性差异。进行药代动力学模拟以增强对相关性的理解。探索了两种用于治疗前逐体素剂量测定预测的策略:(1)器官剂量引导的直接投影;(2)基于深度学习(DL)的分布预测。物理指标，剂量体积直方图（DVH）分析，和身份图被用来研究预测的吸收剂量图。
结果：PET成像和剂量图之间出现了不一致的器官内模式，肾脏中存在中度相关性（r=0.77），肝脏（r=0.5），脾(r=0.58)(P<0.025)。模拟结果表明，器官内药代动力学异质性可能解释了这种不一致性。基于DL的方法实现了较低的平均按体素归一化均方根误差为0.79±0.27%，关于地面真相剂量图，优于器官剂量引导投影（1.11±0.57％）（P<0.05）。DVH分析显示了良好的预测准确性（肾脏的R2=0.92）。DL模型改善了同一性图中拟合线的平均斜率（肝脏为199％），与器官剂量方法的理论最佳结果相比。
结论：我们的研究结果表明，药物动力学的器官内异质性可能会使治疗前剂量学预测复杂化。DL有可能弥合这一差距，用于逐体素异质剂量图的治疗前预测。
