Anatomical Characterization of the Inguinal Lymph Nodes Using Micro‐Computed Tomography to Inform Radical Inguinal Lymph Node Dissections for Metastatic Penile Cancer
Bibliographic record
Abstract
Background, Rationale & Aim In men with penile cancer, a radical inguinal lymph node dissection (rILND) is integral to prevent metastasis and improve disease specific survival. Unfortunately, lymphocele and lymphedema are severe post‐surgical complications that are common to this procedure. The development of a modified inguinal lymph node dissection, that greatly reduced the amount of lymphatic resection, was effective at reducing the incidence of lymphedema and lymphocele; however, it was not suited for patients with advanced‐stage disease as it risked an incomplete cancer resection. As such, there exists a clinical need for a novel surgical template ‐ informed by the lymphatic anatomy ‐ to reduce these post‐surgical complications while ensuring a thorough cancer resection. However, the lymphatic anatomy of the inguinal region is not well characterized in the literature, with no definitive location or number of lymph nodes reported. Therefore, this study aims to elucidate the lymphatic anatomy within the current surgical borders of a rILND using human cadavers. Methodology To visualize the position of the lymph nodes, tissue packets excised from the inguinal region were imaged using micro‐computed tomography (μCT; 154 μm Locus Ultra CT scanner). To characterize the distribution of lymph nodes within each tissue packet, lymph nodes were segmented by grayscale values in 3D using a modified seed‐growing algorithm (Region Growing v1.5; Kellner, 2011) in MATLAB. To compare anatomy between tissue packets, the segmented lymph nodes were aligned (registered) with one another by translating, scaling, rotating, and mirroring (if necessary) such that they were all superimposed in a common coordinate system. This was achieved using a specimen‐specific transformation matrix that was obtained from a generalized Procrustes analysis performed using the positions of four landmarks from each specimen; the anterior superior iliac spine, pubic tubercle, sapheno‐femoral junction, and sartorius as it crosses the femoral artery. These landmarks were chosen due to their consistency between individuals as well as their use as surgical landmarks. Results Preliminary findings from five samples (n=3 cadavers) show a median of 6 lymph nodes (range = 3–7); their anatomical distribution is illustrated in Figure . In addition to obtaining more specimens, ongoing work focuses on segmentation of structures in this region, such as saphenous vein and the sartorius muscle, to provide spatial context to the identified lymph node distribution. Significance & Implications This study provides the first standardized comparison of lymph node anatomy in the inguinal region, and utilizes a novel imaging methodology validated by our lab to study the anatomy of lymphatic tissue in 3D and in situ . In doing so, the anatomy elucidated in this study will help inform refinement to the borders of the radical surgical template, to limit unnecessary resection in an attempt to reduce the incidence of post‐surgical lymphedema and/or lymphocele. The standardized lymph node data after having the generalized Procrustes analysis applied. The dotted lines represent the rILND surgical borders. Figure 1
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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 | 0.000 |
| 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) | 0.002 | 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".