NCOG-04. PRETREATMENT VOLUME OF MR-DETERMINED WHITE MATTER INJURY (WMI) PREDICTS NEUROCOGNITIVE DECLINE AFTER HIPPOCAMPAL AVOIDANT (HA) WBRT+MEMANTINE FOR BRAIN METASTASES: SECONDARY ANALYSIS OF NRG ONCOLOGYCC001
Bibliographic record
Abstract
Abstract PURPOSE Previous secondary analysis of NRG/RTOG 0933 provided hypothesis-generating data supporting a relationship between larger volumes of MR-determined pre-treatment WMI and developing neurocognitive decline following HA-WBRT. The current study examines the relationship between pre-treatment WMI and neurocognitive function (NCF) following WBRT+memantine +/-HA in a substantially larger cohort. METHODS NCF testing was performed at baseline,2,4,6,and 12 months post-WBRT, and included Hopkins Verbal Learning Test–Revised (HVLT-R), Trail Making Test (TMT) Parts A and B, and Controlled Oral Word Association (COWA). Pre-treatment WMI was measured by FLAIR volume corrected for whole brain volume and corrected for the FLAIR volume associated with metastases (FLAIR/(whole brain volume – metastasis FLAIR volume). Pearson correlation coefficients were used to assess association between pre-treatment WMI and change from baseline for each standardized NCF score. RESULTS Of 518 randomized patients, 442 (217,WBRT+Memantine; 225,HA-WBRT+Memantine) had WMI data and were included. In the entire cohort, mean FLAIR volume was 9.3cc (0.1-68.2cc), mean metastases FLAIR volume was 61.5cc (0-423.5cc), mean Whole Brain volume was 1336.4cc (949.4-2397.8cc). At 2 months, there were no significant correlations between neurocognitive test change scores and pre-treatment WMI volume. However, at 4 months, both HVLT-R Total Recall and TMT Part B change score and pre-treatment WMI volume were significantly negatively correlated on the HA-WBRT+Memantine arm (ρ=-0.22 p=0.042 and ρ=-0.27, p=0.013). At 12 months, both TMT Part A and TMT Part B change scores and pre-treatment WMI volume were significantly negatively correlated on the HA-WBRT+Memantine arm (ρ=-0.30, p=0.046 and ρ=-0.53, p< 0.001). CONCLUSIONS Pre-treatment WMI volume was a significant imaging-biomarker predictor of post-treatment neurocognitive decline at 4-and 12-months following HA-WBRT+Memantine. This suggests patients with greater pre-treatment WMI were more susceptible to neurocognitive decline, specifically when undergoing HA-WBRT, but not following standard WBRT. Dose heterogeneity inherent to HA-WBRT delivery may contribute to these findings and are hypothesis generating.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".