NEIM-04 LEVERAGING NOVEL NEUROIMAGING TECHNIQUES TO LINK BRAIN METASTASES AND LOCAL GENE EXPRESSION
Bibliographic record
Abstract
Abstract Metastases are the most prevalent adult brain tumour, most commonly arising from lung, breast, or melanoma primaries. Studies have suggested that different primary tumor types may have predilection for seeding to specific brain regions. One hypothesis is that the interaction of the genomic environment within specific brain region(s) and seeding tumor cells is ideal for supporting this process. The recent availability of neuroimaging based transcriptomic atlases make it feasible to test this hypothesis. In this proof-of-concept study, we leverage the Allen atlas to evaluate whether variance in location among different tumour subtypes can be explained by normative gene expression. Manual segmentation was done on contrast-enhanced T1-weighted MRIs in 31 patients with brain metastases and known primary tumour [breast (n=7), lung (n=14), genitourinary (n=5) and melanoma (n=5)]. Segmented lesions were transformed to template brain space. First, odds-ratio maps were created for each primary tumour subtype. These maps delineate brain regions that were preferentially engaged by each subtype. Consistent with prior literature, odds-ratio maps demonstrated a preference for metastases to seed to different brain regions according to primary tumour subtype, e.g. lung - cerebellum, melanoma - frontal and temporal lobes. Next, mapping our lesions on the Allen atlas of normative gene expression, we identified significant (p<0.01) differences in the local expression of certain genes– such as LEPROT and ITPKA – related to the spatial pattern of breast, lung, genitourinary, and melanoma. This novel approach integrates imaging and transcriptomic techniques that could be used towards an improved understanding of neuro-oncologic processes. Crucially, this approach would allow investigators to leverage conventional anatomical images – acquired as part of a patient’s normal clinical course and in the absence of tissue samples – to better understand cancer mechanics. This has potential ramifications for therapeutic decision-making. Large-scale prospective studies are 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 | 0.001 |
| 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) | 0.002 | 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".