Diffusing alpha‐emitters radiation therapy: In vivo measurements of effective diffusion and clearance rates across multiple tumor types
Bibliographic record
Abstract
Abstract Background Diffusing alpha‐emitters radiation therapy (“Alpha‐DaRT”) is a new modality that uses alpha particles to treat solid tumors. Alpha‐DaRT employs interstitial sources loaded with low activities of , designed to release a chain of short‐lived alpha‐emitters, which diffuse over a few millimeters around each source. Alpha‐DaRT dosimetry is described, to first order, by a framework called the “diffusion–leakage” (DL) model. Purpose The aim of this work is to estimate the tumor‐specific parameters of the DL model from in vivo studies on multiple histological cancer types. Methods Autoradiography studies with phosphor imaging were conducted on 113 tumors in mice from 10 cancer cell lines. An observable, referred to as the “effective diffusion length” , was extracted from images of histological slices obtained using phosphor screens. The tumor and Alpha‐DaRT source activities were measured after excision with a gamma counter to estimate the probability of clearance from the tumor by the blood, . Results The measured values of are in the range of 0.2–0.7 mm across different tumor types and sizes. is between 10 and 90% for all measured tumors, and it generally decreases in magnitude and spread for larger tumors. Conclusions The measured values of and and associated dose calculations indicate that hexagonal Alpha‐DaRT source lattices of 4‐mm spacing with ‐scale activities can lead to effective coverage of the tumor volume with therapeutic dose levels, with considerable margin to compensate for local variations in diffusion and leakage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".