Positron Emission Tomography Combined With Computed Tomography vs. No Positron Emission Tomography Combined With Computed Tomography for the Management of Patients With Resectable Colorectal Cancer Liver Metastases and Synchronous Extrahepatic Disease
Bibliographic record
Abstract
INTRODUCTION: Selected patients with colorectal cancer liver metastases (CRLM) and synchronous extrahepatic disease (EHD) are considered for surgery. OBJECTIVES: To evaluate the change in surgical management and long-term survival (disease-free survival [DFS] and overall survival [OS]) for patients with CRLM and EHD who undergo positron emission tomography combined with computed tomography (PET-CT) vs no PET-CT. METHODS: Patients with CRLM were enrolled in a trial evaluating the effect of PET-CT (vs no PET-CT) on surgical management, DFS, and OS. This is a sub-study of the trial, including only patients with synchronous EHD. Cox proportional hazard models were used to calculate risks for recurrence and death. Survival were described by Kaplan-Meier method and compared with log-rank test. RESULTS: Of 25 patients with EHD (PET-CT arm: 14/270 (5%) and no PET-CT arm: 11/134 (8%)), PET-CT changed surgical management in 14%, all of which avoided liver resection due to more extensive disease. Complete metastasectomy was achieved in 36% (5/14) and 72% (8/11), respectively. Respectively, PET-CT vs no PET-CT had statistically similar median DFS, 5.6 months (95% confidence interval (CI) 3.6-18) vs 7.6 months (95% CI 2.9-15) and median OS, 42 months (95% CI 25-48) vs 29 months (95% CI 17-41). EHD was associated with worse DFS (hazard ratio HR = 1.89, 95% CI 1.41-2.52) and OS (HR = 2.47, 95% CI 1.6-3.83). CONCLUSIONS: Preoperative PET-CT for the management of resectable CRLM did not improve long-term outcomes among patients who had synchronous EHD; however, it changed surgical management in a relatively significant proportion of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".