Histology shift in esophageal cancer between biopsies and resections after neoadjuvant therapy: a pilot study
Bibliographic record
Abstract
Abstract Background Preoperative neoadjuvant therapy followed by resection is the mainstay treatment for locally advanced esophageal adenocarcinoma (EAC). We recently observed the histology shift from predominant EAC in the biopsy to neuroendocrine neoplasm (NEN) with or without EAC in the post-treatment esophagectomy. The underlying mechanism of this finding is uncertain and there is limited information in the literature. Methods Cases with a biopsy diagnosis of EAC and resection diagnosis of NEN with or without EAC were retrieved. All H&E slides were reviewed in conjunction with clinical history and ancillary studies. Results A total of 11 patients were identified with a median age of 60 years. Ten patients received presurgical chemoradiation therapy and 1 with chemotherapy only. All biopsies revealed conventional EAC. When neuroendocrine immunomarkers were retrospectively performed on 5 biopsies, two showed focal positivity, although the classic neuroendocrine morphology was not readily appreciated. The neuroendocrine neoplasm ranged from 1% to 100% in the resections, including 8 of well differentiated NETs and 3 of neuroendocrine carcinomas (NECs). Six cases were clinical stage III or above. Upon follow up, eight patients died of the disease (median survival = 26 months) and three patients were alive after a median follow-up of 14 months. The overall median survival time was better than the reported esophageal NEC (15 months). The 5-year observed survival rate was 11.3%, which was lower than the SEER 5-year survival rate of EAC (21.8%). Conclusions We reported a small series of EAC that showed histology changes between biopsy and esophagectomy after receiving neoadjuvant therapy. These patients tended to present with advanced stage of disease and poor prognosis. Acknowledging this unique phenomenon is helpful to solve diagnostic dilemma and potentially guide presurgical therapy to improve patient’s survival. The abstract of this study was presented at the Annual Conference of United States and Canadian Academy of Pathology (USCAP), March 2023, New Orleans, LA
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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 | 0.002 |
| 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) | 0.002 | 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".