Novel Approaches for Preclinical Lung Sentinel Lymph Node Mapping
Bibliographic record
Abstract
Sentinel lymph node mapping is a technique to identify the first draining lymph node of a solid organ cancer. This node theoretically represents the first potential site of cancer spread, and therefore should be prioritized for biopsy and thorough pathological evaluation. Despite successful adoption of sentinel lymph node mapping as standard-of-care for diseases like breast cancer and cutaneous melanoma, reliable performance for other cancers has proven more elusive. This includes lung cancer, which represents a leading cause of cancer death globally. Proponents of lung sentinel lymph node mapping note its potential utility for guiding therapeutic decision-making and prognostication. Yet prior attempts at lung sentinel lymph node mapping have been complicated by poor performance or technical complexity, even with techniques previously successful in other cancers or preclinical studies. Such failure may reflect a disconnect between prior preclinical research and the anatomic considerations of lung cancer. To that end, a series of experiments were conducted to lay the foundation for future success in lung sentinel lymph node mapping. First, we characterized the performance of dual-modality mixtures of water-soluble computed tomography contrast and near-infrared fluorescent indocyanine green. We found such mixtures preserve the radiopacity of the computed tomography contrast solvent while enhancing the fluorescence of the indocyanine green solute. Second, we leveraged the capabilities of these mixtures for a novel application of lung sentinel lymph node mapping: endoscopic nodal staging, which better reflects the unique anatomic configuration of the intrathoracic lymph nodes. We demonstrated that this novel technique was feasible in healthy pigs, while also identifying key considerations for clinical translation. Finally, we characterized a nodal metastasis model to facilitate more meaningful, future evaluation of lung sentinel lymph node mapping techniques. We demonstrated that this rabbit model formed nodal metastases even with isolated peripheral tumors, mirroring a pattern of disease that would meet the indications for lung sentinel lymph node mapping as currently conceived. The cumulative result of these investigations provides a path to re-explore the potential role of lung sentinel lymph node mapping.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".