Radiomic Analysis of Tumour Heterogeneity Using MRI in Head and Neck Cancer Following Chemoradiotherapy: A Feasibility Study
Bibliographic record
Abstract
To evaluate interval changes in heterogeneity on diffusion-weighted apparent diffusion coefficient (ADC) maps and T1-weighted post-gadolinium (T1w post gad) MRI in head and neck carcinoma (HNSCC), with and without chemo-radiotherapy (CRT) response.This prospective observational cohort study included 24 participants (20 men, age 62.9 ± 8.8 years) with stage III and IV HNSCC. The primary tumour (n = 23) and largest lymph node (n = 22) dimensions, histogram parameters and grey-level co-occurrence matrix (GLCM) parameters were measured on ADC maps and T1w post gad sequences, performed pretreatment and 6 and 12 weeks post CRT. The 2-year treatment response at primary and nodal sites was recorded. The Wilcoxon signed-rank test was used to compare interval changes in parameters after stratifying for treatment response and failure (p < 0.001 statistical significance).23/23 primary tumours and 18/22 nodes responded to CRT at 2 years. Responding HNSCC demonstrated a significant interval change in ADC histogram parameters (kurtosis, coefficient of variation, entropy, energy for primary tumour; kurtosis for nodes) and T1w post gad GLCM (entropy and contrast in the primary tumour and nodes) by 6 weeks post CRT (p < 0.001). Lymph nodes with treatment failure did not demonstrate an interval alteration in heterogeneity parameters.ADC maps and T1w post gad MRI demonstrate the evolution of heterogeneity parameters in successfully treated HNSCC by 6 weeks post CRT; however, this is not observed in lymph nodes failing treatment.Early reduction in heterogeneity is demonstrated on MRI when HNSCC responds to CRT.
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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 | 0.002 |
| 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".