Delayed tumour enhancement on gadoxetate-enhanced MRI is associated with overall survival in patients with colorectal liver metastases
Bibliographic record
Abstract
OBJECTIVES: To determine whether tumour enhancement on preoperative delayed-phase gadoxetate-enhanced MRI can predict long-term survival in patients with colorectal liver metastases (CRCLM) post-hepatectomy. MATERIALS AND METHODS: Sixty-five patients who received a preoperative gadoxetate-enhanced MRI prior to liver resection for CRCLM from January 1, 2010, to December 31, 2012, were included in this retrospective study. Target tumour enhancement (TuEn) was calculated as the mean percentage increase in SNR from precontrast to 10-min or 20-min delayed phase for up to two target lesions. Per-patient TuEn was stratified into weak and strong enhancement based on the cut-off determined by the Youden Index for 3-year survival. Kaplan-Meier and Cox regression analyses were used to determine whether tumour enhancement could predict overall survival independent of potential confounders (clinical risk score). RESULTS: The proportion surviving at 3 years was 85.1% in patients with strong TuEn at 10 min vs. 56.5% in those with weak TuEn at 10 min (p = 0.001). The proportion surviving at 3 years was 79.4% in patients with strong TuEn at 20 min vs. 58.7% in those with weak TuEn at 20 min (p = 0.011). After adjusting for potential confounders, the hazard ratio of death was 0.24 (p = 0.009) in patients who had weak TuEn at 10 min and 0.32 (p = 0.018) in patients who had weak TuEn at 20 min. CONCLUSIONS: Strong delayed tumour enhancement seen on gadoxetate-enhanced MRI is associated with overall survival in patients with CRCLM post-hepatectomy and may be useful for preoperative risk stratification. KEY POINTS: • Delayed tumour enhancement of colorectal liver metastases on gadoxetate-enhanced MRI is associated with survival post-hepatectomy • Delayed tumour enhancement of colorectal liver metastases on gadoxetate-enhanced MRI can be measured at both 10 min and 20 min post-contrast injection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".