Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) for small-cell lung cancer better preserves white matter networks compared to conventional PCI
Bibliographic record
Abstract
BACKGROUND: Hippocampal avoidance during prophylactic cranial irradiation (HA-PCI) is proposed to reduce neurocognitive decline, while preserving the benefits of PCI. We evaluated whether (HA-)PCI induces changes in white matter (WM) microstructure and whether sparing the hippocampus has an impact on preserving brain network topology. In addition, we evaluated associations between topological metrics with hippocampal volume and neuropsychological outcomes. METHODS: In this multicenter randomized phase 3 trial (NCT01780675), small-cell lung cancer (SCLC) patients underwent neuropsychological testing and diffusion tensor imaging (DTI) before, 4 months (33 PCI, 37 HA-PCI) and 1 year (19 PCI, 17 HA-PCI) after (HA-)PCI. Changes in WM microstructure were investigated using whole-brain voxel-based analysis of fractional anisotropy (FA) and mean diffusivity (MD). Both hippocampal and whole-brain graph measures were used to evaluate the topological organization of structural networks. Correlation analysis was performed to associate topological metrics with neuropsychological outcomes and hippocampal volume. RESULTS: Both HA-PCI and PCI were associated with decreased FA in major WM tracts, such as the corpus callosum, at 4 months and 1 year post-treatment. While these FA decreases did not differ significantly between treatment groups, only PCI demonstrated increased MD over time. In addition, PCI showed decreased global efficiency and increased characteristic path length over time when compared with HA-PCI. Significant correlations were found between whole-brain graph measures and neuropsychological outcomes. CONCLUSION: While both techniques induce important changes in the WM microstructure, HA-PCI might better preserve the topological organization of brain networks than PCI. The neuroprotective role of hippocampal sparing still needs further investigation.
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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 | 0.000 |
| 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".