NCO-14PRE-TREATMENT HIPPOCAMPAL VOLUME PREDICTS NEUROCOGNITIVE FUNCTION (NCF) FOR PATIENTS TREATED WITH HIPPOCAMPAL AVOIDANCE WHOLE BRAIN RADIOTHERAPY (HA-WBRT) FOR BRAIN METASTASES: SECONDARY ANALYSIS OF NRG ONCOLOGY/RTOG 0933
Bibliographic record
Abstract
BACKGROUND: RTOG 0933 demonstrated reduced risk of NCF decline in patients with brain metastases treated with HA-WBRT versus historical controls treated with standard WBRT. Hippocampal volume (HV) derived from high resolution MRI (HR-MRI) is an established predictor of NCF in Alzheimer's disease. We assessed associations of HV with NCF of patients enrolled on RTOG 0933. METHODS: HV (-Left, -Right, -Total) was calculated from (1) the submitted treatment structure file and (2) independent processing of baseline HR-MRI with validated software (FreeSurfer, FS). HV was correlated with baseline and 4 month NCF scores (Hopkins Verbal Learning Test-Revised (HVLT-R) Total Recall (TR), Immediate Recognition (IR), and Delayed Recall (DR)) using Pearson correlation. Comparisons between deteriorated and non-deteriorated patients were made using Wilcoxon. RESULTS: 39 of the 42 evaluable patients for the RTOG 0933 primary endpoint had hippocampal contours. All 42 had HR-MRIs, but only 21 were processable by FS due to scan quality. HVs from FS were larger than plan contours (median HV-Total 7.2 vs. 5.5 cc), though were significantly correlated (ρ = 0.68, p = 0.001). Larger FS HV-Total and HV-Right were significantly correlated with improved baseline HVLT-R TR (ρ = 0.46/p = 0.04, ρ = 0.51/p = 0.02, respectively) and DR (ρ = 0.46/p = 0.04, ρ = 0.49/p = 0.03, respectively) but not IR. Plan contour HV was not correlated with baseline HVLT-R though there was a trend for HV-Left with 4 month HVLT-R TR (ρ = 0.29/p = 0.07). There were no significant associations between HV and HVLT deterioration or change from baseline to 4 months. CONCLUSIONS: Baseline HV calculated from automatic segmentation of the hippocampus is significantly associated with baseline NCF. HV is not correlated with deterioration in NCF, though analysis is limited by few events and analyzable patients. The importance of HV will be explored further in two developing NRG trials. This project was supported by grants U10CA21661, U10CA180868, U10CA180822, U10CA37422 and UG1CA189867 from the National Cancer Institute (NCI).
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".