The Correlations Between MRI Perfusion, Diffusion Parameters, and 18F-FDG PET Metabolic Parameters in Primary Head-and-Neck Cancer
Bibliographic record
Abstract
This study aimed to investigate the relationships among parameters from dynamic contrast-enhanced (DCE) MRI, diffusion-weighted MRI (DWI), and F-fluorodeoxyglucose (F-FDG) PET in patients with primary head-and-neck squamous cell carcinoma (HNSCC).A total of 34 patients with primary HNSCC underwent DCE-MRI, DWI, and F-FDG PET before treatment. The perfusion parameters (Ktrans, Ktransmax, Kep, Ve, Vp, and AUC60) from DCE-MRI and ADC (ADCmean, ADCmin) values from DWI were calculated within the manually placed ROI around the main tumor. Standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG = SUVmean × MTV) were calculated with thresholds of 3.0 SUV. The associations between parameters were evaluated by Pearson correlation analysis.Significant correlations were identified between Ktrans and Kep (r = 0.631), Ktrans and Ve (r = 0.603), Ktrans and ADCmean (r = 0.438), Ktransmax and Kep (r = 0.667), Ktransmax and Vp (r = 0.351), Ve and AUC60 (r = 0.364), Ve and ADCmean (r = 0.590), and Ve and ADCmin (r = 0.361). ADCmin was reversely correlated with TLG (r = -0.347). Tumor volume was significantly associated with Ktransmax (r = 0.348).The demonstrated relationships among parameters from DCE, DWI, and F-FDG PET suggest complex interactions among tumor biologic characteristics. Each diagnostic technique may provide complementary information for HNSCC.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".