Temporal lobe microstructural abnormalities in patients with nasopharyngeal carcinoma quantitatively evaluated by high-resolution DWI and DKI after concurrent chemoradiotherapy
Bibliographic record
Abstract
Purpose To investigate temporal lobe microstructural abnormalities and neurocognitive function impairment after concurrent chemoradiotherapy (CCRT) in patients with nasopharyngeal carcinoma (NPC). Methods NPC patients who underwent CCRT were enrolled. High-resolution diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) and diffusion-kurtosis imaging (DKI) MRI, were performed 5 times per patient (once pre-CCRT, 1 week post-CCRT, 3 months post-CCRT, 6 months post-CCRT, and 12 months post-CCRT). Neurocognitive function was evaluated by Montreal Neurocognitive Assessment (MoCA) twice per patient, once pre-CCRT, and once 12-months after CCRT. Results Of 111 patients, 56 completed the entire protocol. The MRI derived apparent diffusion coefficient (ADC), mean of diffusion coefficient (Dmean) and fractional anisotropy (FA) values were significantly decreased ( p < 0.05) over the 0–3 month period following CCRT and significantly increased ( p < 0.05) over the 3–12 month period following CCRT. The mean of kurtosis coefficient (Kmean) continued to decline over a year post-CCRT. All parameters reveal more pronounced changes in white matter (WM) than in grey matter (GM). MoCA also declined after CCRT ( p < 0.001). MoCA showed significant positive correlation with Kmean-WM-6 m, Kmean-WM-12 m and ΔKmean-WM. Conclusions High-resolution DWI and DKI should be considered as a promising method for the investigation of temporal lobe microstructural change in NPC patients after CCRT.
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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 | 0.001 |
| 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".