Association of Pretreatment Hippocampal Volume With Neurocognitive Function in Patients Treated With Hippocampal Avoidance Whole Brain Radiation Therapy for Brain Metastases: Secondary Analysis of NRG Oncology/RTOG 0933
Bibliographic record
Abstract
Purpose: Hippocampal volume (HV) is an established predicting factor for neurocognitive function (NCF) in neurodegenerative disease. Whether the same phenomenon exists with hippocampal-avoidant whole brain radiation therapy is not known; therefore, we assessed the association of baseline HV with NCF among patients enrolled on RTOG 0933. Methods and Materials: Hippocampal volume and total brain volume were calculated from the radiation therapy plan. Hippocampal volume was correlated with baseline and 4-month NCF scores (Hopkins Verbal Learning Test-Revised [HVLT-R] Total Recall [TR], Immediate Recognition, and Delayed Recall [DR]) using Pearson correlation. Deterioration in NCF was defined per the primary endpoint of RTOG 0933(mean 4-month relative decline in HVLT-R DR). Comparisons between patients with deteriorated and nondeteriorated NCF were made using the Wilcoxon test. Results: = .059). There was no significant association between HV and NCF change between baseline and 4 months. Fourteen patients (33.3%) developed NCF deterioration per the primary endpoint of RTOG 0933. There was no significant difference in HV between patients with deteriorated and nondeteriorated NCF, although in all instances, patients with deteriorated NCF had numerically lower HV. Conclusions: Larger HV was positively associated with improved performance on baseline and 4-month HVLT-R TR and DR scores in patients with brain metastases undergoing hippocampal-avoidant whole brain radiation therapy but was not associated with a change in NCF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".