Short report: Plasma based biomarkers detect radiation induced brain injury in cancer patients treated for brain metastasis: A pilot study
Bibliographic record
Abstract
BACKGROUND: Radiotherapy has an important role in the treatment of brain metastases but carries risk of short and/or long-term toxicity, termed radiation-induced brain injury (RBI). As the diagnosis of RBI is crucial for correct patient management, there is an unmet need for reliable biomarkers for RBI. The aim of this proof-of concept study is to determine the utility of brain-derived circulating free DNA (BncfDNA), identified by specific methylation patterns for neurons, astrocytes, and oligodendrocytes, as biomarkers brain injury induced by radiotherapy. METHODS: Twenty-four patients with brain metastases were monitored clinically and radiologically before, during and after brain radiotherapy, and blood for BncfDNA analysis (98 samples) was concurrently collected. Sixteen patients were treated with whole brain radiotherapy and eight patients with stereotactic radiosurgery. RESULTS: During follow-up nine RBI events were detected, and all correlated with significant increase in BncfDNA levels compared to baseline. Additionally, resolution of RBI correlated with a decrease in BncfDNA. Changes in BncfDNA were independent of tumor response. CONCLUSIONS: Elevated BncfDNA levels reflects brain cell injury incurred by radiotherapy. further research is needed to establish BncfDNA as a novel plasma-based biomarker for brain injury induced by radiotherapy.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".