VEGF as a predictive marker of rectal tumor response to preoperative radiotherapy
Bibliographic record
Abstract
BACKGROUND: Neoadjuvant radiotherapy for rectal cancer may result in tumor downstaging or complete tumor regression leading to greater sphincter preservation. The identification of molecular predictive markers of tumor response to preoperative radiotherapy would provide an additional tool for selecting patients most likely to benefit from treatment. The aim of this study was to determine whether VEGF expression in preirradiation tumor biopsies is a useful predictive marker of tumor response in patients with rectal cancer undergoing preoperative radiotherapy. METHODS: Immunohistochemistry for VEGF was performed on 59 preirradiation biopsies from patients with completely responsive (ypT0) or nonresponsive tumors after preoperative radiotherapy. VEGF positivity was evaluated using several scoring methods and the association between VEGF and tumor response was compared. The distribution of VEGF scores was obtained as well as the mean VEGF expression in the two response groups. RESULTS: The mean VEGF expression in nonresponsive tumors (NR) was significantly greater than in completely responsive tumors (CR) (P = 0.0035). Nearly half (47%) of all CR tumors had a VEGF expression of 10% or less. Eleven tumors were negative (0% immunoreactivity) for the protein and all of these (100%) were complete responders. Fifty-two percent of the NR tumors had VEGF scores of 80% or greater. The four scoring methods used to determine the association between VEGF and tumor response each produced significant results (P < 0.05). CONCLUSIONS: The results of this study indicate that VEGF assessed immunohistochemically from preirradiation tumor biopsies may be a useful marker of rectal tumor response to preoperative radiotherapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".