Molecular immuno‐imaging improves tumor detection in head and neck cancer
Bibliographic record
Abstract
Detection and accurate delineation of tumor is important for the management of head and neck squamous cell carcinoma (HNSCC) but is challenging with current imaging techniques. In this study, we evaluated whether molecular immuno-imaging targeting myeloperoxidase (MPO) activity, an oxidative enzyme secreted by many myeloid innate immune cells, would be superior in detecting tumor extent compared to conventional contrast agent (DTPA-Gd) in a carcinogen-induced immunocompetent HNSCC murine model and corroborated in human surgical specimens. In C57BL/6 mice given 4-nitroquinoline-N-oxide (4-NQO), there was increased MPO activity in the head and neck region as detected by luminol bioluminescence compared to that of the control group. On magnetic resonance imaging, the mean enhancing volume detected by the MPO-targeting agent (MPO-Gd) was higher than that by the conventional agent DTPA-Gd. The tumor volume detected by MPO-Gd strongly correlated with tumor size on histology, and higher MPO-Gd signal corresponded to larger tumor size found by imaging and histology. On the contrary, the tumor volume detected by DTPA-Gd did not correlate as well with tumor size on histology. Importantly, MPO-Gd imaging detected areas not visualized with DTPA-Gd imaging that were confirmed histopathologically to represent early tumor. In human specimens, MPO was similarly associated with tumors, especially at the tumor margins. Thus, molecular immuno-imaging targeting MPO not only detects oxidative immune response in HNSCC, but can better detect and delineate tumor extent than nonselective imaging agents. Thus, our findings revealed that MPO imaging could improve tumor resection as well as be a useful imaging biomarker for tumor progression, and potentially improve clinical management of HNSCC once translated.
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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 | 0.000 | 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) | 0.001 | 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".