Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics
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| Názov: | Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics |
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| Autori: | Xue, Song, Gafita, Andrei, Zhao, Yu, Mercolli, Lorenzo, Cheng, Fangxiao, Rauscher, Isabel, D’Alessandria, Calogero, Seifert, Robert, Afshar-Oromieh, Ali, Rominger, Axel, Eiber, Matthias, Shi, Kuangyu |
| Zdroj: | Eur J Nucl Med Mol Imaging Xue, Song; Gafita, Andrei; Zhao, Yu; Mercolli, Lorenzo; Cheng, Fangxiao; Rauscher, Isabel; D'Alessandria, Calogero; Seifert, Robert; Afshar-Oromieh, Ali; Rominger, Axel; Eiber, Matthias; Shi, Kuangyu (2024). Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics. European journal of nuclear medicine and molecular imaging, 51(11), pp. 3450-3460. Springer 10.1007/s00259-024-06737-3 <http://dx.doi.org/10.1007/s00259-024-06737-3> |
| Informácie o vydavateľovi: | Springer Science and Business Media LLC, 2024. |
| Rok vydania: | 2024 |
| Predmety: | Male, Glutamate Carboxypeptidase II, Original Article, Radiopharmaceutical therapy, [, Dosimetry, Deep learning, Intra-organ heterogeneity, 610 Medicine & health, Middle Aged, Glutamate Carboxypeptidase II/metabolism [MeSH], Aged [MeSH], Radiopharmaceuticals/pharmacokinetics [MeSH], Humans [MeSH], Radiometry [MeSH], Precision Medicine/methods [MeSH], Retrospective Studies [MeSH], Middle Aged [MeSH], Antigens, Surface [MeSH], Positron-Emission Tomography/methods [MeSH], Male [MeSH], Prostatic Neoplasms, Castration-Resistant/diagnostic imaging [MeSH], Positron Emission Tomography Computed Tomography/methods [MeSH], Prostatic Neoplasms, Castration-Resistant/radiotherapy [MeSH], Radiopharmaceuticals/therapeutic use [MeSH], ddc, 3. Good health, Prostatic Neoplasms, Castration-Resistant, Positron-Emission Tomography, Positron Emission Tomography Computed Tomography, Antigens, Surface, Humans, Radiopharmaceuticals, Precision Medicine, Radiometry, Aged, Retrospective Studies |
| Popis: | 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 P 2 = 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. |
| Druh dokumentu: | Article Other literature type |
| Popis súboru: | application/pdf |
| Jazyk: | English |
| ISSN: | 1619-7089 1619-7070 |
| DOI: | 10.1007/s00259-024-06737-3 |
| DOI: | 10.48350/196670 |
| Prístupová URL adresa: | https://pubmed.ncbi.nlm.nih.gov/38724653 https://boris.unibe.ch/196670/ https://repository.publisso.de/resource/frl:6496785 https://mediatum.ub.tum.de/1770915 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi.dedup.....98d1eddaa243ed97269f6f40ec7fecf6 |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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 P 2 = 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. |
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| ISSN: | 16197089 16197070 |
| DOI: | 10.1007/s00259-024-06737-3 |