Regional Therapy for Colorectal Cancer Liver Metastases: Which Modality and When?
Bibliographic record
Abstract
For patients with unresectable colorectal liver metastases (uCRLM), regional therapies leverage the unique, dual blood supply to the liver; the hepatic artery is the main blood supply for liver tumors, whereas the portal vein supplies most normal hepatic parenchyma. Infusion of cancer therapies via the hepatic artery allows selective delivery to the tumors with relative sparing of normal liver tissue and little extrahepatic exposure, thus limiting systemic side effects. There is a paucity of randomized controlled trial evidence to inform the optimal integration of regional therapies into the management of CRLM. Hepatic arterial infusion pump (HAIP) chemotherapy has a potential survival benefit when used in the adjuvant setting after resection of CRLM. HAIP chemotherapy can be safely given with contemporary systemic therapies and is associated with a high objective response and rate of conversion to resectability in patients with uCRLM. Drug-eluting beads coated with irinotecan transarterial chemoembolization is associated with high objective response rates within the liver and has a well-established safety profile in patients with uCRLM. Transarterial radioembolization achieves high rates of response within the liver but is not associated with improvements in overall survival or quality of life in the first- or second-line setting for uCRLM. The best treatment approach is the one that most aligns with a given patients' values, preferences, and philosophy of care. In the first-line setting, HAIP could be offered to motivated patients who hope to achieve conversion to resectability. After progression on chemotherapy, HAIP, transarterial chemoembolization, and transarterial radioembolization are valuable treatment options to consider for patients with liver-limited or liver-predominant CRLM who seek to optimize response rates and regional control.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".