First Long-term Oncologic Results of the ALPPS Procedure in a Large Cohort of Patients With Colorectal Liver Metastases
Bibliographic record
Abstract
OBJECTIVES: To analyze long-term oncological outcome along with prognostic risk factors in a large cohort of patients with colorectal liver metastases (CRLM) undergoing ALPPS. BACKGROUND: ALPPS is a two-stage hepatectomy variant that increases resection rates and R0 resection rates in patients with primarily unresectable CRLM as evidenced in a recent randomized controlled trial. Long-term oncologic results, however, are lacking. METHODS: Cases in- and outside the International ALPPS Registry were collected and completed by direct contacts to ALPPS centers to secure a comprehensive cohort. Overall, cancer-specific (CSS), and recurrence-free (RFS) survivals were analyzed along with independent risk factors using Cox-regression analysis. RESULTS: The cohort included 510 patients from 22 ALPPS centers over a 10-year period. Ninety-day mortality was 4.9% and median overall survival, CSS, and RFS were 39, 42, and 15 months, respectively. The median follow-up time was 38 months (95% confidence interval 32-43 months). Multivariate analysis identified tumor-characteristics (primary T4, right colon), biological features (K/N-RAS status), and response to chemotherapy (Response Evaluation Criteria in Solid Tumors) as independent predictors of CSS. Traditional factors such as size of metastases, uni versus bilobar involvement, and liver-first approach were not predictive. When hepatic recurrences after ALPPS was amenable to surgical/ablative treatment, median CSS was significantly superior compared to chemotherapy alone (56 vs 30 months, P < 0.001). CONCLUSIONS: This large cohort provides the first evidence that patients with primarily unresectable CRLM treated by ALPPS have not only low perioperative mortality, but achieve appealing long-term oncologic outcome especially those with favorable tumor biology and good response to chemotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".