Electrochemotherapy in the Locoregional Treatment of Metastatic Colorectal Liver Metastases: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The global incidence of secondary liver cancer is rising due to multiple risk factors, presenting significant challenges in public health. Similarly, colorectal cancer (CRC) remains a leading cause of cancer-related mortality with the development of frequent liver metastases. Surgical resection of CRC liver metastases is only suitable for a limited subset of patients, necessitating alternative nonsurgical treatments such as electrochemotherapy (ECT); Methods: This review adhered to the S.P.I.D.E.R. FRAMEWORK: Systematic searches of PubMed, Cochrane, and Scopus databases were conducted for studies published between 2003 and 2023, following PRISMA guidelines. Inclusion criteria were full-text clinical studies in English focusing on ECT-treated CRC liver metastases, excluding reviews, editorials, and non-clinical papers. The GRADE approach was utilized to assess evidence quality, considering study limitations, consistency, and other factors; Results: From 38 identified articles, 4 met the inclusion criteria, encompassing 78 patients and 128 treated lesions. The studies demonstrated variability in design and follow-up duration (3-11 months). Complete response (CR) rates ranged from 33.3% to 63.0%, while progression disease (PD) rates were high, ranging from 23.0% to 55.6%. Median overall survival (OS) spanned 11.3 to 29.0 months. No severe ECT-related complications were reported. CONCLUSIONS: ECT appears to be a safe and effective modality for the treatment of CRC liver metastases, especially for lesions unsuitable for other ablative techniques. Further prospective and randomized studies are essential to better define the role of ECT in managing CRC liver metastases and to compare its efficacy with other ablative methods.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".