Decisive role of SPECT/CT in localization of unusual periscapular sentinel nodes in patients with posterior trunk melanoma
Bibliographic record
Abstract
Sentinel node mapping is widely applied in patients with melanoma. Although this type of skin cancer usually drains to the standard regional nodal basins, some patients have drainage to an unpredicted site. Nodes lying along a lymphatic channel, between the primary melanoma site and a common basin, are often called interval, in-transit, ectopic, intercalated, or aberrant nodes. They must be considered sentinel lymph nodes because they receive direct lymphatic drainage from a primary tumor site. Most investigators agree that interval sentinel nodes should be harvested; however, the management of melanoma patients with an involved interval sentinel node without established metastasis in the regional basin downstream is controversial. New and innovating technologies have improved nuclear medicine images, including single-photon emission computed tomography/computed tomography (SPECT/CT), a multimodal technique that fuses the radioactivity distribution detected by SPECT with the anatomic information harvested by CT. SPECT/CT does not replace the conventional planar images; it should be considered as a complementary modality for the search of sentinel lymph nodes. We report three illustrative cases that underline the decisive role of SPECT/CT with two-dimensional and three-dimensional reconstruction images to localize the uncommon periscapular sentinel nodes in patients with melanoma of the posterior trunk. The use of this image fusion technique on these patients leads to improved preoperative visualization of the sentinel nodes, may help identify additional periscapular interval sentinel nodes, and enables precise localization of the nodes with their surrounding anatomic structures. The cases are discussed together with a review of the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".