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
{"title":"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","authors":"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","doi":"10.1109/TRPMS.2024.3381849","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"646-654"},"PeriodicalIF":4.6000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10481675","citationCount":"0","resultStr":"{\"title\":\"PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept\",\"authors\":\"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\",\"doi\":\"10.1109/TRPMS.2024.3381849\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":\"8 6\",\"pages\":\"646-654\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10481675\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10481675/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10481675/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
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.