USING CCA-MRI TO PREDICT RESPONSE TO STEREOTACTIC RADIOSURGERY IN PATIENTS WITH BRAIN METASTASES
Bibliographic record
Abstract
Abstract AIMS Brain metastases (BMs) from different primary cancers may have different radiosensitivity, for example melanoma and renal cell cancers are considered more radioresistant. Radiosensitivity is determined by the intrinsic radiobiological properties of tumours, which include vascularisation and oxygenation. Contrast Clearance Analysis (CCA)-MRI measures delayed contrast behaviour to offer a window into tumour vascularity. The CCA colour map shows areas of contrast accumulation (red), suggesting vascular damage, and rapid contrast clearance (blue), suggesting intact and increased vasculature. We aim to interrogate CCA-MRIs pre-stereotactic radiosurgery (SRS) for differences in contrast handling between BMs from different primaries. METHOD 10 patients, with 70 BMs, were included in the study. All had CCA-MRIs pre-SRS as part of their planning-MRIs. Radiotherapy-planning-CT scans were fused with the planning-post-contrast-T1-MRI, with consultant clinical oncologist-approved contoured targets, and the CCA-images. Threshold analysis was performed to obtain proportions of red and blue within the enhancing areas of each tumour. RESULTS There was a significant difference in the proportion of blue between BMs from different primaries (p=0.0294). In pairwise-comparisons, only the difference between lung and renal was significant (mean difference 37.65%, p=0.0390). The difference between the proportion of red was also significant overall (p=0.0084) (Figure 2). In pairwise comparisons, renal was significantly different from lung, breast and melanoma (mean difference 12.65%, p=0.0161; mean difference 12.97%, p=0.0154; and mean difference 11.29%, p=0.0169, respectively). CONCLUSION This study offers a novel insight into the vascularity of BMs from different primaries. Further research to explore if these findings can explain differential SRS outcomes is underway.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".