Liver-First Approach for Synchronous Colorectal Metastases: Analysis of 7360 Patients from the LiverMetSurvey Registry
Bibliographic record
Abstract
BACKGROUND: The liver-first approach in patients with synchronous colorectal liver metastases (CRLM) has gained wide consensus but its role is still to be clarified. We aimed to elucidate the outcome of the liver-first approach and to identify patients who benefit at most from this approach. METHODS: Patients with synchronous CRLM included in the LiverMetSurvey registry between 2000 and 2017 were considered. Three strategies were analyzed, i.e. liver-first approach, colorectal resection followed by liver resection (primary-first), and simultaneous resection, and three groups of patients were analyzed, i.e. solitary metastasis, multiple unilobar CRLM, and multiple bilobar CRLM. In each group, patients from the three strategy groups were matched by propensity score analysis. RESULTS: Overall, 7360 patients were analyzed: 4415 primary-first, 552 liver-first, and 2393 simultaneous resections. Compared with the other groups, the liver-first group had more rectal tumors (58.0% vs. 31.2%) and higher hepatic tumor burden (more than three CRLMs: 34.8% vs. 24.0%; size > 50 mm: 35.6% vs. 22.8%; p < 0.001). In patients with solitary and multiple unilobar CRLM, survival was similar regardless of treatment strategy, whereas in patients with multiple bilobar metastases, the liver-first approach was an independent positive prognostic factor, both in unmatched patients (3-year survival 65.9% vs. primary-first 60.4%: hazard ratio [HR] 1.321, p = 0.031; vs. simultaneous resections 54.4%: HR 1.624, p < 0.001) and after propensity score matching (vs. primary-first: HR 1.667, p = 0.017; vs. simultaneous resections: HR 2.278, p = 0.003). CONCLUSION: In patients with synchronous CRLM, the surgical strategy should be decided according to the hepatic tumor burden. In the presence of multiple bilobar CRLM, the liver-first approach is associated with longer survival than the alternative approaches and should be evaluated as standard.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".