Deep penetrating nevus-like borderline tumors: A unique subset of ambiguous melanocytic tumors with malignant potential and normal cytogenetics
Bibliographic record
Abstract
BACKGROUND: Deep penetrating nevi (DPN) are a relatively uncommon subtype of melanocytic nevi. A small subset of these lesions exhibit atypical features (cytologic and architectural atypia, mitotic activity) seen in melanoma. These lesions we term the deep penetrating nevus-like borderline tumor. Unequivocal melanomas can show overlapping morphologic features of DPN, which have been termed plexiform melanomas. PATIENTS AND METHODS: 40 cases of DPN-like borderline tumor were identified along with 6 cases of plexiform melanoma. Clinical follow up was obtained, along with cytogenetic analysis in the form of fluorescent in situ hybridization (FISH) and/or comparative genomic hybridization (CGH). RESULTS: The DPN-like borderline tumor cases included 24 females and 16 males. Of sentinel lymph node biopsies performed, 1/3 of cases showed lymph node involvement. All patients where an aggressive clinical approach was adopted remain free of disease. All 6 DPN-like borderline tumor cases tested by CGH showed normal cytogenetics, as did 7 of 9 cases tested by FISH. Of the plexiform melanomas, 4/6 patients died of disease. In 3 cases there was morphologic progression from a DPN-like borderline tumor to overt melanoma. In one case of progression, cytogenetics was normal in the DPN-like borderline tumor and then abnormal in the progressed melanoma. CONCLUSION: DPN-like borderline tumors are melanocytic tumors associated with a high incidence of regional lymph node disease and exhibiting the potential for melanoma progression despite a normal cytogenetic profile. Patients with these lesions should be aggressively managed, with at least complete re-excision and consideration of sentinel node biopsy, regardless of cytogenetic data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".