ArticleEuropean journal of nuclear medicine and molecular imaging2022
Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Dosimetry-Driven Personalization of Radiopharmaceutical Therapies: Evidence and Opportunities.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Review
- Evolutionary landscape and collaborative networks of global theranostic research in nuclear medicine: a visual bibliometric analysis.Nuclear medicine communications · 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
- Predictive imaging biomarkers on whole-body diffusion-weighted MRI (WB-DWMRI) and [Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Swin UNETR-based prediction of [BMC medical imaging · 2026Article
- Precision theranostics in oncology: integrating antibody-drug conjugates, radioimmunotherapy, and immuno‑PET for adaptive cancer care.Discover oncology · 2026Review
- Recent advances and current landscape of software tools for image analysis and dosimetry in nuclear medicine.EJNMMI physics · 2026Review
- Review
- The role of multimodality imaging in selection, response assessment, and follow-up of patients receivingInsights into imaging · 2026Article
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- Long Axial Field-of-view PET: A New Era of Quantitative PET Imaging for Theranostic Applications in Clinic.PET clinics · 2025Review
- CanJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Influence of dosimetry accuracy on the correlation with treatment outcome in a preliminary PSMA radiopharmaceutical therapy study.European journal of nuclear medicine and molecular imaging · 2025Article
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.Ecancermedicalscience · 2025Review
- Validation of dosimetry programs (Olinda & IDAC) for evaluation of absorbed dose inEJNMMI physics · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAlthough treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment. In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment.
methodsTwenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with
resultsAn optimal machine learning method achieved a dosimetry prediction MAPE of 15.8 ± 13.2% for the kidney, 29.6% ± 13.7% for the liver, 23.8% ± 13.1% for the salivary glands, and 32.1 ± 31.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (p < 0.01), 25.5 ± 17.3% for the kidney, 139.1% ± 111.5% for the liver, 67.0 ± 58.3% for the salivary glands, and 54.1 ± 215.3% for the spleen.
conclusionThe preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.
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