PS01.144: LYMPH NODE YIELDS AFTER ESOPHAGECTOMY: IMPACT OF APPROACH TO SURGERY AND USE OF NEOADJUVANT THERAPIES
Bibliographic record
Abstract
Abstract Background Although the importance of lymph node (LN) harvest for the adequate staging of esophageal cancer has been well studied, lymph node yields in the literature remain highly variable. Moreover, the effect of modern treatments of esophageal cancer such as induction therapy and minimally invasive approaches on lymph node yield is incompletely understood. Methods A retrospective review of 307 patients who underwent esophagectomy for esophageal cancer between 2005–2013 at Toronto General Hospital was conducted. Early in this experience, thoracoabdominal, transhiatal, Ivor Lewis and Mckeown approaches were utilized with transition over time to fully minimally invasive Ivor Lewis and Mckeown operations. Induction chemoradiotherapy is now our standard for locally advanced esophageal cancer. Demographics, histology, type and approach to esophagectomy, use of induction therapy, lymph node yield, and number of positive lymph nodes were collected. Kruskal-Wallis test was utilized for significance between groups. Results Our population comprised of 239 (78%) males and 68 (22%) females. Adenocarcinoma was the predominant histology at 220 (72%) with 78 (25%) squamous cell carcinoma and 9 (3%) as other histology. 144 (47%) patients had surgery alone, 147 (48%) had induction chemoradiotherapy, and 16 (5%) had induction chemotherapy. The open approach was used in 178 (58%), hybrid minimally-invasive in 33 (11%), fully minimally-invasive in 58 (19%), and transhiatal in 38 (12%). Overall, a median of 24 [IQR17–33; min 3, max 92] nodes were obtained. Induction therapy did not lower our yield (no induction 23[16–32], induction chemotherapy 32.5[17–47], induction chemoradiotherapy 25[19–33], P = 0.07). Transition to a minimally invasive approach similarly did not lower our yield, with only the transhiatal approach showing lower lymph node yield (open 26[17–33], hybrid 33[23–39], fully minimally-invasive 25[19–36], transhiatal 16[11–22], P = 0.005). Conclusion Lymph node yields above 20 can be routinely achieved for adequate staging. Despite the increasing use of induction therapy and minimally-invasive approaches, similar lymph node yields can and should be achieved. Disclosure All authors have declared no conflicts of interest.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".