Purpose

Although treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment. In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment.

Methods

Twenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with ¹⁷⁷Lu-PSMA I&T RLT were included retrospectively. They had available pre-therapy ⁶⁸ Ga-PSMA-HEBD-CC PET/CT and at least 3 planar and 1 SPECT/CT imaging for dosimetry. Overall, 43 cycles of ¹⁷⁷Lu-PSMA I&T RLT were applied. Organ-based standard uptake values (SUVs) were obtained from pre-therapy PET/CT scans. Patient dosimetry was calculated for the kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the planar and SPECT/CT images. Machine learning methods were explored for dose prediction from organ SUVs and laboratory measurements. The uncertainty of these dose predictions was compared with the population-based dosimetry estimates. Mean absolute percentage error (MAPE) was used to assess the prediction uncertainty of estimated dosimetry.

Results

An optimal machine learning method achieved a dosimetry prediction MAPE of 15.8 ± 13.2% for the kidney, 29.6% ± 13.7% for the liver, 23.8% ± 13.1% for the salivary glands, and 32.1 ± 31.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (p < 0.01), 25.5 ± 17.3% for the kidney, 139.1% ± 111.5% for the liver, 67.0 ± 58.3% for the salivary glands, and 54.1 ± 215.3% for the spleen.

Conclusion

The preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.

中文翻译：

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应用机器学习对接受 177Lu-PSMA I&T 治疗的晚期前列腺癌患者进行治疗前剂量测定估算

目的

尽管普遍推荐新兴的前列腺特异性膜抗原（PSMA）靶向放射配体疗法（RLT）的治疗计划和个体化剂量应用，但目前在实践中仍难以实施。在这项研究中，我们旨在证明在 RLT 治疗之前基于影像学和实验室测量的剂量学预测的治疗前概念。

方法

^{回顾性纳入了 23 名接受177} Lu-PSMA I&T RLT治疗的转移性去势抵抗性前列腺癌 (mCRPC)患者。他们有可用的治疗前⁶⁸  Ga-PSMA-HEBD-CC PET/CT 和至少 3 个平面和 1 个 SPECT/CT 成像用于剂量测定。总的来说，^{177的 43 个周期}应用了 Lu-PSMA I&T RLT。基于器官的标准摄取值 (SUV) 是从治疗前的 PET/CT 扫描中获得的。使用 Hermes Hybrid Dosimetry 4.0 从平面和 SPECT/CT 图像计算患者肾脏、肝脏、脾脏和唾液腺的剂量测定。探索了机器学习方法以根据器官 SUV 和实验室测量进行剂量预测。将这些剂量预测的不确定性与基于人群的剂量学估计值进行了比较。平均绝对百分比误差 (MAPE) 用于评估估计剂量测定的预测不确定性。

结果

最佳机器学习方法实现了剂量学预测 MAPE，肾脏为 15.8 ± 13.2%，肝脏为 29.6% ± 13.7%，唾液腺为 23.8% ± 13.1%，脾脏为 32.1 ± 31.4%。相比之下，基于文献人群平均值的预测具有显着更大的 MAPE ( p  < 0.01)，肾脏为 25.5 ± 17.3%，肝脏为 139.1% ± 111.5%，唾液腺为 67.0 ± 58.3%，唾液腺为 54.1 ± 215.3 %为脾脏。

结论

初步结果证实了治疗前估计治疗剂量的可行性及其对基于人口的经验估计的附加值。剂量预测的探索可能支持 RLT 治疗计划的实施。