TACE Versus TARE in the Treatment of Liver-Metastatic Breast Cancer: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Liver metastases are common among patients with breast cancer and have a poor prognosis if left untreated. The aim of this systematic review is to evaluate and compare chemoembolization (TACE) versus radioembolization (TARE) treatments in patients with breast cancer liver-dominant metastases in terms of overall survival (OS), local tumor control (LC), and toxicity. Methods: The S.P.I.D.E.R framework was used to address the clinical question. A systematic literature search using PubMed and Scopus was performed to identify full articles evaluating the efficacy of TACE and TARE in patients with liver metastases from breast cancer. Results: The literature search resulted in 10 articles for TACE, 13 articles for TARE and 1 for combined TACE/TARE, totaling 462 patients for the TACE group and 627 for the TARE group. The median LC was 68.7% for TACE and 78.9% for TARE. The median OS was 15.3 months for TACE and 11.9 for TARE. Progression at three months was 32.5% for TACE and 20.6% for TARE. Conclusions: The included studies were heterogeneous, varying widely in design, patient selection, and therapeutic protocols. Nonetheless, this systematic review suggests that locoregional therapies are effective in the treatment of liver metastases in patients with breast cancer and may improve tumor burden, alleviate symptoms and extend overall survival. The median LC of the liver metastases at three months was higher in the TARE group compared to TACE. However, the TARE group showed lower OS rates after treatment.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".