Anatomical characterization of the inguinal lymph nodes using microcomputed tomography to inform radical inguinal lymph node dissections in penile cancer
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Radical inguinal lymph node dissections (rILND) for penile cancer risk significant postoperative lymphocele and lymphedema. However, reducing the risk of lymphatic complications is limited by our understanding of lymphatic anatomy. Therefore, this study aims to elucidate the lymphatic anatomy within the current surgical borders of a rILND. METHODS: To visualize the position of the lymph nodes, tissue packets excised from the inguinal region of five fresh, male cadavers were imaged using microcomputed tomography (µCT). To standardize the position, rotation and size between specimens, each lymph node packet was aligned using a Generalized Procrustes analysis. RESULTS: There was a median of 13.5 lymph nodes (range = 8-18) per packet, with the majority (99%) clustered within a 6 cm radius of the saphenofemoral junction; a region 39%-41% smaller than current surgical borders. No difference existed between the number of nodes between sides, or distribution around the saphenofemoral junction. CONCLUSIONS: This study provides the first 3D, in situ, standardized characterization of lymph node anatomy in the inguinal region using µCT. By using knowledge of the normal lymphatic anatomy, this study can help inform the reduction in borders of rILND to limit disruption and ensure a complete lymphadenectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".