389. DEVELOPING A PROGNOSTICALLY SIGNIFICANT MORPHOLOGICAL CLASSIFICATION SYSTEM FOR ESOPHAGEAL ADENOCARCINOMA
Bibliographic record
Abstract
Abstract Background Histologically, esophageal adenocarcinoma is broadly divided into two morphological subtypes: intestinal and diffuse types, with the latter conferring a worse prognosis. However, the present classification system does not account for morphological heterogeneity and the clinical and prognostic implications of this heterogeneity remains uncertain. This study focuses on elucidating the clinical and prognostic significance of the various EAC morphological patterns. Methods We identified surgical resection cases conducted at a single center from 2008 to the present date. All tumour-containing slides were analyzed, and the percentages of each morphological pattern was calculated. The overall percentage of each morphology was determined by summing across all tumour slides per case. Cases were stratified by neoadjuvant treatment status. Results This study reviewed 106 surgical resection cases. Seventy cases (58 male, 12 female, 59.26 +/− 11.12 years) received neoadjuvant treatment prior to surgery, while 36 cases (29 male, 7 female, 66.36 +/− 11.85 years) did not. A similar average slide number was obtained from treated versus untreated cases (10.13 +/− 7.43 slides vs 9.14 +/− 8.57, p = 0.5391). Morphological heterogeneity (i.e., 3+ morphologies occupying >10% of the tumor) was not different between treated and untreated cases (33% versus 24.2%, p = 0.35). Subgroup analysis revealed a significant increase in tumor morphological heterogeneity with poorer neoadjuvant treatment response (score 3) (p < 0.05). Conclusions To our knowledge, this is the first study investigating the clinical and prognostic implications of the heterogeneous morphological patterns observed in EAC. Our results suggest that tumours with poor response to neoadjuvant therapy are more likely to exhibit significant morphological heterogeneity. We are currently exploring whether specific morphological patterns are linked to adverse prognosis and poor response to neoadjuvant therapy. These findings have the potential to guide clinical decision-making in EAC.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".