Impact of Treatment Modalities on Locally Advanced Gastric Cancer—Real-World Data
Bibliographic record
Abstract
The optimal sequencing of chemotherapy in locally advanced gastric cancer (LAGC) remains controversial. This study aimed to compare survival outcomes between adjuvant (ACT) and neoadjuvant (NACT) chemotherapy and to identify clinicopathological factors associated with progression-free survival (PFS) and overall survival (OS) in a real-world setting. Methods: We retrospectively analyzed 103 patients with non-metastatic gastric cancer treated between 2014 and 2024. Patients were categorized into ACT (n = 56) and NACT (n = 47) groups. Kaplan–Meier and Cox regression analyses were used to assess survival outcomes and prognostic factors. Results: The NACT group was younger and had more proximal tumors. Median OS was 48.7 months in the ACT group versus 17.7 months in the NACT group (p = 0.048). Median PFS was not reached in the ACT group and was 15.6 months in the NACT group (p = 0.008). Negative surgical margin status was independently associated with improved survival, whereas age was an independent negative prognostic factor for OS. No significant associations were found between OS or PFS and histologic subtype, lymphovascular invasion, perineural invasion, gender, D2 dissection, or type of surgery. Notably, 21% of NACT patients did not proceed to surgery due to progression, treatment intolerance, or refusal. Conclusion: Although ACT was associated with longer PFS and OS in this cohort, these differences are most likely explained by baseline imbalances, patient selection factors, and survivorship bias rather than the timing of chemotherapy itself. These findings highlight the importance of careful patient selection for NACT and underscore the need for prospective, randomized studies to define optimal sequencing strategies in LAGC. Our study contributes descriptive, real-world data rather than definitive evidence of treatment superiority.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".