Effectiveness of Head-and-Neck Molecular Imaging Reporting and Data System Criterion in Head-and-Neck Squamous Cell Carcinoma PostConcurrent Chemoradiotherapy
Bibliographic record
Abstract
Purpose: Postconcurrent chemoradiotherapy (CRT) response assessment has been challenging in locally advanced head-and-neck squamous cell carcinoma (LA-HNSCC) due to prevailing postradiation changes. Molecular response methods have been encouraging, although further clarifications and validations were needed. We tested the effectiveness of a proposed semi-quantitative molecular response criterion in these patients. Materials and Methods: = 83) post 3 months CRT using a five points Head and Neck Molecular Imaging-Reporting and Data System (HAN-MI-RADS) criterion. Where available, histopathology examination with clinical and imaging interpretation was taken as a reference for the disease. A diagnostic accuracy comparison was done with the existing Hopkins score. Further effectiveness was analyzed with disease-free survival (DFI) and overall survival (OS). Results: Metastasis was developed in 11/83 patients at 3 months of evaluation. Of 72 patients, 39, 2, and 31 patients had a complete response, equivocal response, and partial response as per HAN-MI-RADS. Per patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for predicting loco-regional disease up to 1 and 2 years was 93.3%, 92.5%, 90.3%, 94.9%, 92.9%, and 84.9%, 91.9%, 90.3%, 87.2%, and 88.6% respectively. One year and two years DFI for each HAN-MI-RADS score showed a statistically significant difference while it was not for OS. The receiver operating characteristic curve analysis showed significantly better outcome predictability of HAN-MI-RADS (area under the curve [AUC] 0.884) than Hopkins (AUC 0.699). Conclusions: A five points HAN-MI-RADS criterion was found promising for response assessment with less equivocal results and statistically significant higher AUC than Hopkins for loco-regional recurrence prediction.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".