A Clinical Study to Assess Diffuse Reflectance Spectroscopy with an Auto-Calibrated, Pressure-Sensing Optical Probe in Head and Neck Cancer
Bibliographic record
Abstract
Diffuse reflectance spectroscopy (DRS) is a powerful tool for quantifying optical and physiological tissue properties such as hemoglobin oxygen saturation and vascularity. DRS is increasingly used clinically for distinguishing cancerous lesions from normal tissue. However, its widespread clinical acceptance is still limited due to uncontrolled probe–tissue interface pressure that influences reproducibility and introduces operator-dependent results. In this clinical study, we assessed and validated a pressure-sensing and automatic self-calibration DRS in patients with suspected head and neck squamous cell carcinoma (HNSCC). The clinical study enrolled nineteen patients undergoing HNSCC surgical biopsy procedures. Patients consented to evaluation of this improved DRS system during surgery. For each patient, we obtained 10 repeated measurements on one tumor site and one distant normal location. Using a Monte Carlo-based model, we extracted the hemoglobin saturation data along with total hemoglobin content and scattering properties. A total of twelve cancer tissue samples from HNSCC patients and fourteen normal tissues were analyzed. A linear mixed effects model tested for significance between repeated measurements and compared tumor versus normal tissue. These results demonstrate that cancerous tissues have a significantly lower hemoglobin saturation compared to normal controls (p < 0.001), which may be reflective of tumor hypoxia. In addition, there were minimal changes over time upon probe placement and repeated measurement, indicating that the pressure-induced changes were minimal and repeated measurements did not differ significantly from the initial value. This study demonstrates the feasibility of conducting optical spectroscopy measurements on intact lesions prior to removal during HNSCC procedures, and established that this probe provides diagnostically-relevant physiologic information that may impact further treatment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".