Combination of Electrochemotherapy with Radiotherapy: A Comprehensive, Systematic, PRISMA-Compliant Review of Efficacy and Potential Radiosensitizing Effects in Tumor Control
Bibliographic record
Abstract
Radiotherapy (RT) and electrochemotherapy (ECT) are established local treatments for cancer. While effective, both therapies have limitations, especially in treating bulky and poorly oxygenated tumors. ECT has emerged as a promising palliative treatment, raising interest in exploring its combination with RT to enhance tumor response. However, the potential benefits and challenges of combining these treatments remain unclear. A systematic review was conducted following PRISMA guidelines. PubMed, Scopus, and Cochrane libraries were searched. Studies were screened and selected based on predefined inclusion and exclusion criteria. Ten studies were included, comprising in vitro and in vivo experiments. Different tumor types were treated with ECT alone or in combination with RT. ECT plus RT demonstrated superior tumor response compared to that under single therapies or other combinations, regardless of the cytotoxic agent and RT dose. However, no study demonstrated a clear superadditive effect in cell survival curves, suggesting inconclusive evidence of specific ECT-induced radiosensitization. Toxicity data were limited. In conclusion, the combination of ECT and RT consistently improved tumor response compared to that with individual therapies, supporting the potential benefit of their combination. However, evidence for a specific ECT-induced radiosensitization effect is currently lacking. Additional investigations are necessary to elucidate the potential benefits of this combination therapy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".