Is it time to retire preoperative radiation for localized esophageal and gastro-esophageal adenocarcinoma?
Bibliographic record
Abstract
Whether preoperative chemoradiotherapy (CRT) or perioperative chemotherapy is superior for localized esophageal or gastro-esophageal junction (GEJ) cancers has been a topic of long-standing debate. For years, standard of care in the United States for localized esophageal or GEJ adenocarcinoma (EAC) has been physician's choice between the 2 strategies. More recently, adjuvant immunotherapy has also been introduced into the treatment approach for those who received neoadjuvant CRT. While preoperative radiation remains an important option for patients with esophageal squamous cell carcinomas, the ESOPEC trial presented in 2024 suggested that perioperative chemotherapy is superior to preoperative CRT in EAC. In addition, the results of the TOPGEAR trial presented in 2024 showed that adding CRT to perioperative chemotherapy did not lead to improved outcomes. This has led to a shift in practice among oncologists. However, there are various complexities and factors to consider when interpreting these studies. In this review, we outline both trials and what their findings may mean for the future of preoperative CRT in EAC. Ultimately, until more data are available that incorporate novel agents such as immunotherapy, these studies indicate that we should defer the routine inclusion of radiation in preoperative treatment for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".