Exploring the therapeutic potential of localized alpha irradiation for cancer: from DNA damage to immune activation
Bibliographic record
Abstract
Alpha radiation has emerged as a promising modality in cancer treatment due to its unique physical and biological properties. Among these, diffusing alpha-emitters radiation therapy (DaRT) delivers alpha radiation directly into solid tumours using inserted seeds. This review synthesizes both the biological mechanisms and therapeutic implications of alpha irradiation, with a focus on DaRT. We explore how alpha particles induce complex DNA damage, modulate the tumour microenvironment, and interact with immune therapies. Emphasis is placed on preclinical and early clinical findings that suggest DaRT's potential to improve outcomes, especially in difficult-to-treat malignancies. The high linear energy transfer (LET) radiation induces complex DNA damage in tumour cells, leading to increased cell death compared to conventional radiotherapy. Alpha particles have a short range in tissue, allowing for highly localized treatment with minimal damage to surrounding healthy tissue. Recent studies have demonstrated that alpha radiation can stimulate antitumor immune responses, potentially enhancing treatment efficacy. Clinical trials utilizing alpha-emitting radioisotopes have shown encouraging results in various cancer types, particularly for metastatic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".