Optical Detection of Tumors <i>In Vivo</i> by Visible Light Tissue Oximetry
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Bibliographic record
Abstract
Endoscopy is a standard procedure for identifying tumors in patients suspected of having gastrointestinal (G.I.) cancer. The early detection of G.I. neoplasms during endoscopy is currently made by a subjective visual inspection that relies to a high degree on the experience of the examiner. This process can be difficult and unreliable, as tumor lesions may be visually indistinguishable from benign inflammatory conditions and the surrounding mucosa. In this study, we evaluated the ability of local ischemia detection using visible light spectroscopy (VLS) to differentiate neoplastic from normal tissue based on capillary tissue oxygenation during endoscopy. Real-time data were collected (i) from human subjects (N = 34) monitored at various sites during endoscopy (enteric mucosa, malignant, and abnormal tissue such as polyps) and (ii) murine animal subjects with human tumor xenografts. Tissue oximetry in human subjects during endoscopy revealed a tissue oxygenation (StO2%, mean +/- SD) of 46 +/- 22% in tumors, which was significantly lower than for normal mucosal oxygenation (72 +/- 4%; P < or = 0.0001). No difference in tissue oxygenation was observed between normal and non-tumor abnormal tissues (P = N.S.). Similarly, VLS tissue oximetry for murine tumors revealed a mean local tumor oxygenation of 45% in LNCaP, 50% in M21, and 24% in SCCVII tumors, all significantly lower than normal muscle tissue (74%, P < 0.001). These results were further substantiated by positive controls, where a rapid real-time drop in tumor oxygenation was measured during local ischemia induced by clamping or epinephrine. We conclude that VLS tissue oximetry can distinguish neoplastic tissue from normal tissue with a high specificity (though a low sensitivity), potentially aiding the endoscopic detection of gastrointestinal tumors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it