Comparison of bimodality versus trimodality therapy for esophageal or gastroesophageal junction (GEJ) cancer: Experience from the Princess Margaret Cancer Centre.
Bibliographic record
Abstract
4066 Background: There are no phase 3 trials comparing definitive chemoradiation (bimodality) vs. perioperative chemoradiation (trimodality) for locoregional esophageal/GEJ cancer. Methods: A retrospective analysis (2011-2016) compared bimodality and trimodality therapy in patients (pts) with locoregional esophageal/GEJ cancer treated with curative intent. Overall survival (OS) and disease-free survival (DFS) were calculated from the date of diagnosis. Uni- and multivariable Cox proportional hazards regression adjusted for patient and disease factors. Results: Of 141 patients, 107 (76%) were male. Mean ages were 66.7 ± 12.4 years (bimodality; N = 57) and 58.6 ± 11.3 years (trimodality; N = 84). For bimodality pts, 49% had adenocarcinoma (adeno) and 51% had squamous cell carcinoma (SCC). For trimodality pts, 76% had adeno and 24% had SCC. Bimodality pts received a higher radiation dose compared to trimodality pts (59.4 ± 7.2 vs. 44.9 ± 5.8 Gy). We found that trimodality therapy significantly improved OS and DFS compared to bimodality therapy (4-year OS: 40% vs. 31%, HR 0.56, 95%CI 0.36-0.87, p = 0.008; 4-year DFS: 33% vs. 23%, HR 0.60, 95%CI 0.40-0.90, p = 0.01). This difference was confined to the subgroup with adeno histology (OS: HR 0.28, 95%CI 0.16-0.47, p < 0.001; DFS: HR 0.26, 95%CI 0.16-0.43, p < 0.001). In the SCC subgroup, OS and DFS were similar (OS: HR 1.30, 95%CI 0.48-2.64, p = 0.77; DFS: HR 0.91, 95%CI 0.43-1.94, p = 0.82). Using multivariable regression with AIC backward selection, the only retained prognostic factors were treatment modality (p < 0.001) and histology (p = 0.002). Conclusions: Our findings support preferential use of trimodality therapy for pts with adeno histology given superior OS and DFS, whereas bimodality and trimodality therapy appeared comparable in pts with SCC histology. Pending confirmation in a larger series with longer follow-up, these findings suggest differential treatment algorithms for locoregional esophageal and GEJ cancer based on tumor histology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".