Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy
Publié dansEuropean journal of nuclear medicine and molecular imaging, vol. 51, no. 6, p. 1516-1529
Date de publication2024-05
Date de mise en ligne2024-01-25
Résumé
Mots-clés
- Deep learning
- Monte Carlo simulation
- Radiation dosimetry
- Radionuclide therapy
- [177Lu]Lu-DOTATATE
- Deep Learning
- Female
- Humans
- Image Processing, Computer-Assisted / methods
- Male
- Monte Carlo Method
- Neuroendocrine Tumors / diagnostic imaging
- Neuroendocrine Tumors / radiotherapy
- Octreotide / analogs & derivatives
- Octreotide / therapeutic use
- Organometallic Compounds / therapeutic use
- Precision Medicine / methods
- Radiometry / methods
- Radiopharmaceuticals / therapeutic use
- Single Photon Emission Computed Tomography Computed Tomography / methods
Structure d'affiliation
Groupe de recherche
Financement
- European Commission - Radiation risk appraisal for detrimental effects from medical exposure during management of patients with lymphoma or brain tumour [945196]
- Swiss National Science Foundation - Precision Medicine in Molecular Radiotherapy using Deep Learning [211066]
Citation (format ISO)
MANSOURI, Zahra et al. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy. In: European journal of nuclear medicine and molecular imaging, 2024, vol. 51, n° 6, p. 1516–1529. doi: 10.1007/s00259-024-06618-9
Fichiers principaux (1)
Article (Published version)
Fichiers secondaires (1)
Identifiants
- PID : unige:176796
- DOI : 10.1007/s00259-024-06618-9
- PMID : 38267686
- PMCID : PMC11043201
URL commercialhttps://link.springer.com/10.1007/s00259-024-06618-9
ISSN du journal1619-7070