ReviewNuclear medicine and molecular imaging2026
Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.
Review in Nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Targeted Alpha Therapy as a Multiscale Design Problem: From Radioactive Decay to Therapeutic Outcome.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Cellular responses to targeted radionuclide therapy: rethinking radiobiology under continuous low dose rates.Frontiers in oncology · 2026Review
- Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.Frontiers in nuclear medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiopharmaceutical dosimetry is rapidly evolving from a physics-dominated calculation tool to a central pillar of precision nuclear medicine. As targeted radionuclide therapies expand across indications, there is a growing clinical imperative to personalize dose estimation, predict therapeutic efficacy and mitigate organ toxicity. This review critically examines the current landscape of dosimetry methods including organ level Medical Internal Radiation Dose (MIRD) schema, voxel-based S-values, Monte Carlo (MC) simulations and emerging artificial intelligence (AI)-assisted segmentation tools and their translational relevance. Through a comprehensive literature search of
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.