Prediction of treatment response in head and neck cancer by magnetic resonance spectroscopy.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Poor treatment outcome remains high in patients with squamous cell carcinoma (SCC) of the head and neck region. Treatment of head and neck cancer could be improved and perhaps standardized if reliable markers for tumor progression and poor prognosis could be developed. MR spectroscopy has been used previously to differentiate between malignant and adjacent normal specimen in these cancers. This study explores the capability of MR spectroscopy in providing an indication of the aggressiveness of a tumor and its response to treatment. METHODS: Thirty-six SCC patients with treatment failure, and 22 other patients who were treated concurrently at the same center but with no indication of failure for a period of 3 years, were selected for the study. Tumor specimens were kept frozen at -70 degrees C, and later subjected to 1H-MR spectroscopy at 25 degrees C. The resonance areas for 6 spectral regions were determined, and their ratio calculated. The mean values of the ratios were then compared between the 2 groups by using the Student t test. RESULTS: The choline-to-creatine (3.2/3.0 parts per million [ppm]) and the 1.3/0.9 ppm spectral intensity ratios (signal due to lipid or lactic acid) were the 2 most notable ones to be significantly elevated in the group with poor response. Using these ratios, a sensitivity of 83% and a specificity of 82% were obtained in predicting which head and neck cancer patients would fail treatment. CONCLUSIONS: These preliminary results suggest that MR spectroscopy has the potential to contribute to an accurate and early prediction of tumor behavior and response to treatment in squamous cell carcinoma of the head and neck region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".