Pathologic complete response after neoadjuvant therapy for locally advanced rectal cancer in a real-world setting: a population-based study
Bibliographic record
Abstract
Aim This study aimed to determine the impact of time from neoadjuvant therapy (NAT) to surgery on the complete pathologic response (pCR) rate in patients with locally advanced rectal cancer. NAT decreases the local recurrence of rectal cancer. Some patients achieve a pCR. The optimal time between NAT and surgery to maximize pCR remains uncertain. Method We identified adults with Tany, Nany, M0 rectal adenocarcinoma treated with short-course radiation therapy (SCRT) or long-course chemoradiotherapy (LCRT) followed by total mesorectal excision. Multivariable logistic regression examined characteristics associated with pCR and survival. Results In total, 3,476 patients were included from between 2000 and 2017. Of these, 1,554 (44.7%) received LCRT and 1,796 (51.7%) SCRT. The pCR rate was 13.2% (181/1373) among the LCRT group and 1.5% (26/1770) among the SCRT group. A pCR among the SCRT group was positively associated with weeks from SCRT to surgery [odds ratio (OR) 1.45, 95% confidence interval (CI) 1.13,1.86; p=0.003], tumor grade (grade 1 OR 5.72, 95% CI 1.70, 19.30, p=0.005), and stage (stage 1 OR 7.07, 95% CI 2.49, 20.08, p=<0.001). The pCR rate among the LCRT group was not associated with weeks from LCRT to surgery but was associated with sex and stage. Median follow-up was 9.5 years, and median overall survival (OS) was 9.7 years. Among patients receiving LCRT, the 5-year OS rate was higher (69.8%) when surgery followed LCRT by 6–10 weeks compared to those undergoing surgery <6 weeks or 10+ weeks post-LCRT (p = .003). Conclusion Among rectal cancers treated with LCRT in a population-based cohort, longer delay to radical resection is associated with increased pCR rate. However, the overall pCR rate was lower than that reported in trial populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".