Multimodal Image-Guided Surgical and Photodynamic Interventions in Head and Neck Cancer: From Primary Tumor to Metastatic Drainage
Bibliographic record
Abstract
PURPOSE: The low survival rate of head and neck cancer (HNC) patients is attributable to late disease diagnosis and high recurrence rate. Current HNC staging has inadequate accuracy and low sensitivity for effective diagnosis and treatment management. The multimodal porphyrin lipoprotein-mimicking nanoparticle (PLP), intrinsically capable of positron emission tomography (PET), fluorescence imaging, and photodynamic therapy (PDT), shows great potential to enhance the accuracy of HNC staging and potentially HNC management. EXPERIMENTAL DESIGN: Using a clinically relevant VX-2 buccal carcinoma rabbit model that is able to consistently develop metastasis to regional lymph nodes after tumor induction, we investigated the abilities of PLP for HNC diagnosis and management. RESULTS: PLPs facilitated accurate detection of primary tumor and metastatic nodes (their PET image signal to surrounding muscle ratios were 10.0 and 7.3, respectively), and provided visualization of the lymphatic drainage from tumor to regional lymph nodes by both preoperative PET and intraoperative fluorescence imaging, allowing the identification of unknown primaries and recurrent tumors. PLP-PDT significantly enhanced cell apoptosis in mouse tumors (73.2% of PLP-PDT group vs 7.1% of PLP alone group) and demonstrated complete eradication of primary tumors and obstruction of tumor metastasis in HNC rabbit model without toxicity in normal tissues or damage to adjacent critical structures. CONCLUSIONS: PLPs provide a multimodal imaging and therapy platform that could enhance HNC diagnosis by integrating PET/computed tomography and fluorescence imaging, and improve HNC therapeutic efficacy and specificity by tailoring treatment via fluorescence-guided surgery and PDT.
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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".