Locoregional Therapies for Colorectal Cancer Liver Metastases: Options Beyond Resection
Bibliographic record
Abstract
Colorectal cancer was the third most common malignancy worldwide in 2018, and most patients present with or develop distant metastases. Colorectal liver metastases are most commonly observed because of the vascular drainage of the colon and superior rectum. Current guidelines recommend surgical resection as first-line treatment; however, 80% to 90% of patients with colorectal liver metastases are ineligible for primary resection. For patients with unresectable disease, a multidisciplinary treatment approach is favored, incorporating systemic therapy and a toolbox of local ablative therapies. These treatments either aim at cytoreduction to enable a conversion to surgical resectability or control of disease progression and spread. Each of these treatments carries unique outcomes and risk profiles, thereby contributing to an individualized treatment strategy for patients with colorectal liver metastases. This review summarizes evidence on hepatic artery infusion, stereotactic body radiation therapy, thermal ablation, transarterial chemoembolization with drug-eluding beads, and transarterial radioembolization for treatment of colorectal liver metastases. Results of large-scale prospective and retrospective studies and international guidelines are discussed to provide detailed background on the current and prospective use of local ablative techniques in management of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".