Surveillance frequency in resected esophageal cancer: Towards personalization of follow-up
Bibliographic record
Abstract
INTRODUCTION: In spite of advances in curative management of esophageal cancer, a significant proportion of patients have early recurrence following resection. The role of CT-guided surveillance remains undefined. This study aims to determine if follow-up can be personalised, to allow detection of clinically relevant recurrence, while reducing low-yield surveillance for patients. METHODS: A retrospective review was conducted encompassing patients undergoing esophagectomy with curative intent from 1st March 2018-31st May 2022. Routine 3-monthly CT scanning was conducted for 2 years, followed by 6-monthly surveillance for 1 year, and annual surveillance to 5 years. Disease characteristics, time to recurrence and time to death were recorded and interrogated to determine their impact on recurrence and personalization of surveillance. RESULTS: In total, 190 patients underwent surveillance. Seventy-one (37 %) developed recurrence, with most in the first two years. Those who recurred were younger (63 vs 67, p < 0.001), had higher pathologic staging (p < 0.001), higher tumour regression grade (p = 0.005), higher lymph node ratio (p < 0.001) and high-risk histology (p < 0.001). Most recurrences detected were asymptomatic (94 %). A personalised surveillance score was devised. With strict criteria, 12 % of patients could be excluded from surveillance without compromising detection of asymptomatic recurrence. By broadening criteria, a larger portion of patients could avoid imaging, with a small number of asymptomatic recurrences missed. This would require significant balancing of the risk-benefit ratio for individuals. CONCLUSION: Intensive surveillance post-resection of esophageal cancer will detect most recurrences while asymptomatic, potentially facilitating intervention. In select patients, routine surveillance could be excluded without compromising oncologic or patient outcomes.
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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 | 0.004 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".