RARE-11. QUANTITATIVE MR IMAGING FEATURES ASSOCIATED WITH UNIQUE TRANSCRIPTIONAL CHARACTERISTICS IN PEDIATRIC ADAMANTINOMATOUS CRANIOPHARYNGIOMA: A POTENTIAL GUIDE FOR THERAPY
Bibliographic record
Abstract
Abstract METHODS Through the Advancing Treatment for Pediatric Craniopharyngioma (ATPC) consortium we accumulated preoperative MRIs and tumor RNA for 50 unique ACP patients. MRIs were assessed quantitatively for 28 different features and analyzed using Multiple Factor Analysis (MFA) and optimal clustering was determined via maximization of Bayesian Information Criterion (BIC). Following bulk RNAseq, differential expression and pathway enrichment were performed using standard methodologies (i.e., DESeq2 and GSEA). RESULTS MRI features were well represented in the first 3 dimensions of MFA (variance explained=67.32%); specifically tumor/cyst size, ventricular size, and cyst fluid diffusivity. Using this three-way axis, we identified 3 patient subgroups. Transcriptional differences between these subgroups indicated one group was enriched for DNA damage response and MYC related pathways, one group enriched for SHH, and one group enriched for WNT/β-catenin and EMT-related pathways. CONCLUSION This preliminary work suggests that there may be unique gene expression variants within ACP, which may be identified preoperatively using easily quantifiable MRI parameters. These radiogenomic signatures could provide prognostic information and/or guidance in the selection of antitumor therapies for children with ACP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".