Patterns of disease, treatment, and outcomes of esophageal cancer arising within a previous radiation treatment field.
Bibliographic record
Abstract
328 Background: Esophageal cancer arising within a previous radiation treatment field (ECRF) is rare. The patterns of disease, treatment and outcomes in these patients (pts) have not been well characterized. Methods: A retrospective analysis was performed for pts treated for esophageal cancer at the Princess Margaret Cancer Centre from 2002-2016. Electronic medical records of all pts with a histologic diagnosis of esophageal cancer occurring within the field of previous radiotherapy were reviewed. The Kaplan-Meier method was used to calculate progression free survival (PFS) and overall survival (OS). Results: Of 31 ECRF pts identified, the most common prior cancer was head and neck (45%), median radiation (RT) dose 50Gy, median time to diagnosis of esophageal cancer 12 years. Features at diagnosis of ECRF included: median age 71 years, 58% male, 87% with performance status (PS) 0-1, 77% squamous cell carcinoma, 19% stage IV. Treatment intent was curative in 16 pts, palliative in 15 (Table). Reasons for palliative treatment were: 40% metastatic, 53% comorbidities/PS, 7% anatomic factors. Of resected pts, 36% had a pT1-2 tumour, 55% pN0, 69% R0. For curative pts, median PFS was 26.2 months (95%CI 10.9-34.4) with a 3 year PFS rate of 35% (95% CI 15-81). Median OS for curative pts was 26.4 months (95%CI 17.8-105.5) with a 3 year OS rate of 43% (95% CI 22-83). Most palliative pts were unable to have chemotherapy due to comorbidities and PS. Median OS for palliative pts was 9.5 months (95% CI 3.6-15.4). Conclusions: Most ECRF pts presented with earlier stage disease; however, more than a third of these could not have aggressive curative treatment due to comorbidities and/or PS. Most curative pts had surgery alone. Few palliative pts had chemotherapy, largely due to poor clinical status. Our data suggest that outcomes in both curative and palliative ECRF pts may be limited by the ability to tolerate standard of care treatments. [Table: see text]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".