FDG-PET/CT in assessing response to neoadjuvant chemoradiotherapy for potentially resectable locally advanced thoracic esophageal cancer
Bibliographic record
Abstract
510 Objectives To correlate metabolic response to neoadjuvant chemoradiotherapy (neoCR) on FDG-PET/CT to pathologic and clinical response, and survival in patients with locally advanced esophageal cancer (LAEC). Methods Forty-five patients with LAEC underwent PET/CT at baseline and after neoCR. Tumors were evaluated using PERCIST-based criteria including SUL, SUL tumor/liver ratio, % change in SUL, and visual assessment using the following parameters: residual uptake at or below background = complete response; focal uptake at least 30% below baseline = partial response; uptake similar to baseline (≤30%) = stable disease; uptake increasing in intensity or extent = progressive disease. These parameters were compared to pathology regression grade, clinical response, and overall survival. Results On surgical pathology, there was complete or near complete regression of tumor in 51.1%, partial response in 42.2%, and lack regression in 4.4%. One patient (2.2%) had progression of disease on imaging and did not undergo surgical resection. None of the baseline PET parameters had significant correlation to pathology regression grade or clinical response. On follow-up, SUL tumor/liver ratio and % change in SUL after neoCR were significant in predicting regression on pathology (p=0.049 & p=0.045, respectively) and overall clinical response (p=0.027 & p=0.03, respectively). There was strong association between visual assessment of tumor regression and pathologic regression grade (p=0.002), clinical response (p Conclusions PET/CT can predict pathologic tumor regression, overall clinical response and patient survival after neoCR for LAEC
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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 | 0.001 | 0.000 |
| 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".