Short- and Long-Term Outcomes of Neoadjuvant Chemotherapy in Operable Locally Advanced Colon Cancer: A Systematic Review
Bibliographic record
Abstract
The management of operable locally advanced colon cancer has traditionally centered on upfront surgical resection. The role of neoadjuvant chemotherapy (NAC) in this setting remains a subject of investigation, with potential benefits including tumor downstaging and early treatment of micrometastases. This systematic review aims to synthesize the existing evidence on the short- and long-term outcomes of NAC for patients with operable locally advanced colon cancer. A systematic literature search was conducted across PubMed/MEDLINE, Embase, Scopus, and Web of Science up to October 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies reporting on pathological response, surgical outcomes, recurrence, disease-free survival (DFS), or overall survival (OS) in patients receiving NAC for locally advanced colon cancer were included. The risk of bias was assessed using the Cochrane RoB 2 tool for randomized trials and the ROBINS-I tool for non-randomized studies. A qualitative synthesis was performed due to heterogeneity among the included studies. Twelve studies were included. Pathological complete response rates varied, reaching up to 16% with FOLFOX-based regimens in colon cancer. NAC was associated with high R0 resection rates and acceptable postoperative morbidity. A key finding was the stage-dependent survival benefit, with a significant OS improvement specifically in T4 disease but not in T3 disease. The addition of targeted therapy based on biomarker status (e.g., panitumumab in KRAS-wildtype tumors) demonstrated significant improvements in DFS and OS. Evidence from rectal cancer studies suggested that NAC could achieve outcomes comparable to neoadjuvant chemoradiotherapy. NAC is a feasible and effective strategy for operable locally advanced colon cancer, demonstrating significant pathological responses and promising survival outcomes, particularly in T4 tumors and with biomarker-directed therapy. Its efficacy is highly dependent on careful patient selection based on disease stage and molecular characteristics. Future research should focus on randomized trials in high-risk populations and the integration of personalized treatment approaches.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".