Spindle cell melanocytic lesions—part I: an approach to compound naevoidal pattern lesions with spindle cell morphology and Spitzoid pattern lesions
Bibliographic record
Abstract
Melanocytic lesions show great morphological diversity in their architecture and the cytomorphological appearance of their composite cells. Whereas functional melanocytes reveal a dendritic cytomorphology and territorial isolation, lesional naevomelanocytes and melanoma cells typically show epithelioid, spindled or mixed cytomorphologies and a range of architectural arrangements. Spindling is common to melanocytic lesions, and may be either a characteristic feature or a divergent appearance. The presence of spindle cells may mask the melanocytic nature of a lesion, and is often disconcerting, either because of its infrequent appearance in a particular lesion or its interpretation as a dedifferentiated phenotype. Spindle cell melanocytic lesions follow the full spectrum of potential biological outcomes, and difficulty may be experienced judging the nature of a lesion because of a lack of consistently reliable features to predict biological behaviour. Over time, recognition of numerous histomorphological features that may portend a more aggressive lesion have been identified. However, the translation of these features into a diagnostic entity requires a gestalt approach. Although most spindle cell melanocytic lesions can reliably be resolved with this standard approach, problem areas do exist and cause no end of grief to the surgical pathologist or dermatopathologist. In this review, the authors present their algorithmic approach to spindle cell melanocytic lesions and discuss each entity in turn, in order to (1) model a systematic approach to such lesions, and (2) provide familiarity with those melanocytic lesions that either typically or occasionally display a spindled cytomorphology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".