Radiotherapy Improves Survival of Patients With Lymphovascular Invasion in pT1b Esophageal Squamous Cell Cancer After Endoscopic Submucosal Dissection
Bibliographic record
Abstract
INTRODUCTION: Adjuvant radiotherapy is recommended for pT1b esophageal squamous cell cancer (ESCC) after endoscopic submucosal dissection (ESD). However, it is unclear whether additional radiotherapy can improve patient survival. This study aimed to evaluate the efficacy of adjuvant radiotherapy after ESD for pT1b ESCC. METHODS: This was a multicenter, cross-sectional study involving 11 hospitals in China. Between January 2010 and December 2019, patients with T1bN0M0 ESCC treated with or without adjuvant radiotherapy after ESD were included. Survival between groups was compared. RESULTS: Overall, 774 patients were screened, and 161 patients were included. Forty-seven patients (29.2%) received adjuvant radiotherapy after ESD (RT group) and 114 (70.8%) underwent ESD alone (non-RT group). There were no significant differences in overall survival (OS) and disease-free survival (DFS) between the RT and non-RT groups. Lymphovascular invasion (LVI) was the only prognostic factor. In the LVI+ group, adjuvant radiotherapy significantly improved survival (5-year OS: 91.7% vs 59.5%, P = 0.050; 5-year DFS: 92.9% vs 42.6%, P = 0.010). In the LVI- group, adjuvant radiotherapy did not improve survival (5-year OS: 83.5% vs 93.9%, P = 0.148; 5-year DFS: 84.2% vs 84.7%, P = 0.907). The standardized mortality ratios were 1.52 (95% confidence interval 0.04-8.45) in the LVI+ group with radiotherapy and 0.55 (95% confidence interval 0.15-1.42) in the LVI- group without radiotherapy. DISCUSSION: Adjuvant radiotherapy could improve survival in pT1b ESCC with LVI+ other than LVI- after ESD. Selective adjuvant radiotherapy based on LVI status achieved survival rates similar to those of the general population.
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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 | 0.001 |
| 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".