Background and 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.

Conclusion

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.

中文翻译：

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通过表征 PSMA 导向的放射性药物治疗诊断中的器官内异质性，进行基于 PET 的治疗前体素剂量测定预测

背景和目标

通过治疗诊断学的诊断维度制定治疗计划，为预测 RPT 吸收剂量提供了见解，并有可能个性化放射剂量以提高治疗效果。然而，现有的侧重于根据诊断数据进行剂量预测的研究通常依赖于器官水平的估计，而忽略了器官内的变化。本研究旨在表征器官内治疗诊断异质性，并利用人工智能技术对其进行定位，即基于治疗前 PET 预测体素吸收剂量图。

方法

^{回顾性纳入 23 例接受 [ 177} Lu]Lu-PSMA I&T RPT治疗的转移性去势抵抗性前列腺癌患者。选择 48 个具有治疗前 PET 成像和至少 3 个治疗后 SPECT/CT 成像的治疗周期。比较肾脏、肝脏和脾脏的 PET 示踪剂和 RPT 剂量分布，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。探索了治疗前体素剂量学预测的两种策略：（1）器官剂量引导直接投影； (2)基于深度学习(DL)的分布预测。应用物理指标、剂量体积直方图 (DVH) 分析和恒等图来研究预测的吸收剂量图。

结果

PET 成像和剂量图之间出现不一致的器官内模式，其中肾脏 ( r  = 0.77)、肝脏 ( r  = 0.5) 和脾脏 ( r  = 0.58) 存在中度相关性 ( P  < 0.025)。模拟结果表明器官内药代动力学异质性可能可以解释这种不一致。就地面真实剂量图而言，基于深度学习的方法实现了较低的平均体素标准化均方根误差 0.79 ± 0.27%，优于器官剂量引导投影 (1.11 ± 0.57%) ( P  < 0.05)。 DVH 分析显示出良好的预测准确性（肾脏的 R ²  = 0.92）。与器官剂量方法的理论最佳结果相比，DL 模型改善了恒等图中拟合线的平均斜率（肝脏为 199%）。

结论

我们的结果表明，器官内药代动力学的异质性可能会使治疗前剂量测定预测复杂化。深度学习有潜力弥合这一差距，用于体素异质剂量图的治疗前预测。