Nodal lymph flow quantified with afferent vessel input function allows differentiation between normal and cancer-bearing nodes
Bibliographic record
Abstract
Morbidity and complexity involved in lymph node staging via surgical resection and biopsy could ideally be improved using node assay techniques that are non-invasive. While visible blue dyes are often used to locate the sentinel lymph nodes from draining lymphatic vessels near a tumor, they do not provide an in situ metric to evaluate presence of cancer. In this study, the transport kinetics of methylene blue were analyzed to determine the potential for better in situ information about metastatic involvement in the nodes. A rat model with cancer cells in the axillary lymph nodes was used, with methylene blue injection to image the fluorescence kinetics. The lymphatic flow from injection sites to nodes was imaged and the relative kinetics from feeding lymphatic ducts relative to lymph nodes was quantified. Large variability existed in raw fluorescence and transport patterns within each cohort resulting in no systematic difference between average nodal uptake in normal, sham control and cancer-bearing nodes. However, when the signal from the afferent lymph vessel fluorescence was used to normalize the signal of the lymph nodes, the high signal heterogeneity was reduced. Using a model, the lymph flow through the nodes [Formula: see text] was estimated to be 1.49 ± 0.64 ml/g/min in normal nodes, 1.53 ± 0.45 ml/g/min in sham control nodes, and reduced to 0.50 ± 0.24 ml/g/min in cancer-cell injected nodes. This summarizes the significant difference (p = 0.0002) between cancer-free and cancer-bearing nodes in normalized flow. This process of normalized flow imaging could be used as an in situ tool to detect metastatic involvement in nodes.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".