Intraoperative sentinel lymph node mapping with indocyanine green via video‐assisted thoracoscopic surgery for primary pulmonary neoplasia in a dog
Bibliographic record
Abstract
OBJECTIVE: To report the use of intraoperative sentinel lymph node (SLN) mapping with indocyanine green (ICG) and near-infrared (NIR) imaging in a dog with a primary pulmonary carcinoma that underwent lung lobectomy via a video-assisted thoracoscopic approach. STUDY DESIGN: Case report. ANIMALS: A 9-year-old female spayed Labrador retriever. METHODS: A solitary, 6.5 cm diameter, right caudal pulmonary mass was identified on contrast-enhanced thoracic and abdominal computed tomography, with no overt metastatic disease or other primary neoplastic diseases. The dog underwent video-assisted thoracoscopic surgery. A right-sided thoracoscopic approach was initially performed for right caudal pulmonary ligament transection and image-guided peritumoral (intrapulmonary) ICG injection. Due to the large size of the mass, a wound retractor was then placed in the right 6th intercostal space for right caudal lung lobectomy. Sentinel lymph node mapping was performed via NIR imaging, and the SLN was extirpated for histologic assessment. RESULTS: The right tracheobronchial lymph node was identified as sentinel via NIR fluorescence following peritumoral ICG injection. Right caudal lung lobectomy and right tracheobronchial lymph node extirpation were performed without complication, and histologic evaluation revealed a grade 1 pulmonary adenocarcinoma with pneumonia and a reactive lymph node. CONCLUSION: Peritumoral ICG administration with NIR imaging appears to be an effective method for intraoperative SLN mapping in dogs with primary pulmonary tumors. The technique utilized in this report can be applied to open or minimally invasive procedures. Large-scale studies with SLN mapping are needed to determine an accurate incidence of nodal metastatic disease and any effect of extirpation of early metastatic disease via SLN mapping techniques on oncologic outcomes in dogs with primary pulmonary carcinoma.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".