Esophageal Carcinoma Cuniculatum Diagnosed on Mucosal Biopsies Using a Semiquantitative Histologic Schema: Report of Two Esophagectomy-Confirmed Cases
Bibliographic record
Abstract
Esophageal carcinoma cuniculatum is a rare variant of squamous cell carcinoma characterized by a unique and common histologic pattern including hyperkeratosis, acanthosis, dyskeratosis, deep keratinization, intraepithelial neutrophils, neutrophilic microabscess, focal cytologic atypia, koilocyte-like cells, and keratin-filled cyst/burrows observed in the resection specimens. Preoperative diagnosis can be extremely difficult. A semiquantitative histologic scoring system has been previously proposed for mucosal biopsies, which has been associated with improved diagnostic yield. However, this histologic schema for the diagnosis of carcinoma cuniculatum has not been applied prospectively. Herein, we describe two cases of esophageal carcinoma cuniculatum in patients presenting with progressive dysphagia and esophageal mass. Presurgical endoscopic mucosal biopsies showed features consistent with carcinoma cuniculatum, and a preoperative diagnosis was achieved by applying the aforementioned semiquantitative histologic schema. Both patients underwent neoadjuvant chemoradiation followed by esophagectomy. Both esophagectomy specimens showed residual adventitia-invading carcinoma cuniculatum, negative lymph nodes, marked tumor regression, and an exuberant histiocytic and giant response. To our best knowledge, these represent the first two cases of esophageal carcinoma cuniculatum diagnosed by applying this semiquantitative histologic schema to mucosal biopsies. Large studies are needed to further confirm these preliminary findings and validate this histologic scoring system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".