ReviewTheranostics2026
The evolution of surrogate microspheres in transarterial radioembolization: from SPECT prediction to multimodal AI-guided precision dosimetry.
Review in Theranostics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Surrogate materials enable pre-treatment prediction of intra-hepatic distribution of therapeutic radioactive microsphere, facilitating patient-specific dosimetry for transarterial radioembolization (TARE). Technetium-99m macroaggregated albumin (⁹⁹ᵐTc-MAA) has long constituted the clinical standard for SPECT-based surrogates, however, its fundamental physicochemical difference from therapeutic microspheres limits the reliability of MAA-based dosimetry and prediction for personalized treatment planning. Next-generation surrogate technology includes biodegradable polymeric microspheres engineered to closely replicate the therapeutic microsphere while affording post-procedural arterial recanalization through controlled biodegradation. Theranostic microspheres incorporating diverse radionuclides, positron-emitting PET compatible surrogates, and multimodal CT and MRI-visible surrogates have collectively expanded same-particle pre-treatment dosimetry capabilities. These material-level advances are synergistically coupled with computational dosimetry tools including artificial intelligence/deep learning algorithms for automated segmentation/dose-prediction, and Monte Carlo simulations that enhance voxel-based dosimetry precision. This review examines recent advances in surrogate materials design and integrated imaging/dosimetry methodologies that enhance TARE efficacy, safety, and personalized treatment outcomes.
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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.