用于治疗前预测体素剂量测定的 PBPK 适应性深度学习:实验室概念验证

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-03-28 DOI:10.1109/TRPMS.2024.3381849
Mohamed Kassar;Milos Drobnjakovic;Gabriele Birindelli;Song Xue;Andrei Gafita;Thomas Wendler;Ali Afshar-Oromieh;Nassir Navab;Wolfgang A. Weber;Matthias Eiber;Sibylle Ziegler;Axel Rominger;Kuangyu Shi
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引用次数: 0

摘要

治疗前剂量预测是新兴的放射性药物疗法(RPT)制定治疗计划和进行个性化优化的先决条件。描述放射性药物内在药代动力学的生理学药代动力学(PBPK)模型已被提出用于治疗前剂量预测。然而,该模型仅限于器官预测,而且根据治疗前测量结果进行定制仍具有挑战性。另一方面,人工智能(AI)在治疗前剂量预测方面已显示出潜力。然而,由于治疗前成像与治疗后剂量测定之间存在巨大差距,纯数据驱动模型实现体素预测仍具有挑战性。本研究旨在将 PBPK 模型集成到深度学习中,以实现体素预测。研究开发了一个条件生成对抗网络(cGAN),该网络集成了 PBPK 模型作为正则化。为了验证这一概念,利用现实的硅学模拟生成了 120 位具有 68Ga-PSMA-11 PET 成像和 177Lu-PSMA-I&T 剂量测定的虚拟患者。在肾脏、脾脏、肝脏和唾液腺方面,与纯深度学习相比,所提出的方法获得了更好的准确性和剂量体积直方图。初步结果证实,所提出的 PBPK 适应性深度学习可以改善治疗前的体素剂量预测,并可为支持个性化 RPT 的异质性剂量分布治疗规划提供实用的解决方案。
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PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept
Pretherapy dosimetry prediction is a prerequisite for treatment planning and personalized optimization of the emerging radiopharmaceutical therapy (RPT). Physiologically based pharmacokinetic (PBPK) model, describing the intrinsic pharmacokinetics of radiopharmaceuticals, have been proposed for pretherapy prediction of dosimetry. However, it is restricted with organwise prediction and the customization based on pretherapy measurements is still challenging. On the other side, artificial intelligence (AI) has demonstrated the potential in pretherapy dosimetry prediction. Nevertheless, it is still challenging for pure data-driven model to achieve voxelwise prediction due to huge gap between the pretherapy imaging and post-therapy dosimetry. This study aims to integrate the PBPK model into deep learning for voxelwise pretherapy dosimetry prediction. A conditional generative adversarial network (cGAN) integrated with the PBPK model as regularization was developed. For proof of concept, 120 virtual patients with 68Ga-PSMA-11 PET imaging and 177Lu-PSMA-I&T dosimetry were generated using realistic in silico simulations. In kidneys, spleen, liver and salivary glands, the proposed method achieved better accuracy and dose volume histogram than pure deep learning. The preliminary results confirmed that the proposed PBPK-adapted deep learning can improve the pretherapy voxelwise dosimetry prediction and may provide a practical solution to support treatment planning of heterogeneous dose distribution for personalized RPT.
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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