Atypical Cellular Blue Nevi (Cellular Blue Nevi With Atypical Features): Lack of Consensus for Diagnosis and Distinction From Cellular Blue Nevi and Malignant Melanoma (“Malignant Blue Nevus”)
Bibliographic record
Abstract
The distinction of cellular blue nevi (CBN) with atypical features ["atypical" CBN (ACBN)] from conventional CBN and malignant melanomas related to or derived from CBN remains a difficult problem. Here, we report on the diagnosis of various cellular blue melanocytic neoplasms by 14 dermatopathologists who routinely examine melanocytic lesions. Three parameters were assessed: (1) for between rater analyses, we calculated interobserver agreement by the kappa statistic (regardless of whether the diagnosis was correct). (2) For each individual lesion, we reported whether a majority agreement (>50%) was reached and, if so, whether the majority agreed with the gold standard diagnosis, derived from standardized histopathologic criteria for melanoma, definitive outcome such as metastatic event or death of disease, or disease-free follow-up for > or =4 years. (3) For the individual pathologists, we calculated sensitivity and specificity for each type of lesion. The study set included 26 melanocytic lesions: (1) 6 malignant melanomas developing in or with attributes of CBN; (2) 11 CBN with atypical features and indeterminate biologic potential (ACBN); (3) 8 conventional CBN; and (4) 1 common BN. The kappa values for interrater agreement varied from 0.52 (95% confidence interval 0.45, 0.58) for melanoma to 0.02 (0.05, 0.08) for ACBN and 0.20 (0.13, 0.28) for CBN. The kappa for all lesions was 0.25 (0.22, 0.28). The pathologists' sensitivities were 68.6% (61.0%, 76.1%) for melanoma, 33.1% (21.0%, 45.2%) for ACBN, and 44.6% (29.0%, 60.3%) for CBN. The specificities were 65.7% (55.8%, 75.6%) for melanoma, 84.7% (77.3%, 92.2%) for ACBN, and 89.9% (82.7%, 97.1%) for CBN. Overall, greater than 50% of the pathologists agreed and were correct in their diagnosis 38.5% (10 lesions) of the time. There was a majority agreement, but with an incorrect diagnosis, another 26.9% (7 lesions) of the time. Six of the 7 majority agreements with an incorrect diagnosis were for ACBN lesions. In summary, the results of our study indicate that there is substantial confusion and disagreement among experienced histopathologists about the definitions and biologic nature of cellular blue melanocytic neoplasms particularly those thought to have atypical features ("atypical" CBN).
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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 | 0.035 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".