ArticleEuropean journal of nuclear medicine and molecular imaging2024
Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics.
Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Outcome-driven dosimetry optimization for [European journal of nuclear medicine and molecular imaging · 2026Article
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- State-of-the-art and future developments in dosimetry for SSTR- and PSMA-targeting radioligand therapies.Zeitschrift fur medizinische Physik · 2026Review
- A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions.Scientific data · 2026Article
- Dual-tracer PET/CT and cocktail therapy with [European journal of nuclear medicine and molecular imaging · 2026Article
- Swin UNETR-based prediction of [BMC medical imaging · 2026Article
- Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.Nuclear medicine and molecular imaging · 2026Review
- Prognostic Value of Cycle 1 SPECT SUVJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Review
- Long Axial Field-of-view PET: A New Era of Quantitative PET Imaging for Theranostic Applications in Clinic.PET clinics · 2025Review
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.Clinical nuclear medicine · 2025Article
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
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12 authors.
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Abstract
BACKGROUND AND
objectiveTreatment 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.
methods23 patients with metastatic castration-resistant prostate cancer treated with [
resultsInconsistent 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 < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R
conclusionOur 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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