The Role of Routine Clinical Pretreatment 18F-FDG PET/CT in Predicting Outcome of Colorectal Liver Metastasis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the value of SUV-based metabolic parameters derived from pretreatment F-FDG PET/CT of colorectal liver metastases in predicting disease response, progression-free survival (PFS), and overall survival (OS). PATIENTS AND METHODS: We retrospectively reviewed 70 colorectal patients with liver metastases who underwent pretreatment F-FDG PET/CT. SUVmean, SUVmax, TLG (total lesion glycolysis), metabolic tumor volume, and metabolic tumor diameter were the metabolic parameters derived from volume of interest analysis of the most FDG-avid liver lesion in each subject. Clinical and laboratory parameters were recorded. Tumor response was assessed by response evaluation criteria in solid tumors 1.1 criteria at 12 weeks after treatment. Associations between tumor response, metabolic parameters, and clinical/laboratory parameters were examined by 1-way analysis of variance. The relationship of the metabolic parameters with PFS and OS was determined by Kaplan-Meier analyses and further confirmed with multivariate Cox regression analyses. RESULTS: SUVmean less than 4.48, SUVmax less than 6.59, TLG less than 75.2, metabolic tumor volume less than 4.49 cm, and hemoglobin level greater than or equal to 11 g/dL were associated with longer PFS (P < 0.05). Prior surgery or radiofrequency ablation to the liver metastases was the only additional factor shown to be associated with longer OS. CONCLUSIONS: SUV-based metabolic parameters derived from pretreatment F-FDG PET/CT can predict PFS in colorectal liver metastases.
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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 | 0.000 |
| 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 | 0.000 | 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".