{"doi":"10.3390/cancers15245806","title":"The Importance of Optimal Thermal Ablation Margins in Colorectal Liver Metastases: A Systematic Review and Meta-Analysis of 21 Studies","abstract":"Background: Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in the US. Thermal ablation (TA) can be a comparable alternative to partial hepatectomy for selected cases when eradication of all visible tumor with an ablative margin of greater than 5 mm is achieved. This systematic review and meta-analysis aimed to encapsulate the current clinical evidence concerning the optimal TA margin for local cure in patients with colorectal liver metastases (CLM). Methods: MEDLINE, EMBASE, and the CENTRAL databases were systematically searched from inception until 1 May 2023, in accordance with the PRISMA Guidelines. Measure of effect included the risk ratio (RR) with 95% confidence interval (CI) using the random-effects model. Results: Overall, 21 studies were included, comprising 2005 participants and 2873 ablated CLMs. TA with margins less than 5 mm were associated with a 3.6 times higher risk for LTP (n = 21 studies, RR: 3.60; 95% CI: 2.58–5.03; p-value < 0.001). When margins less than 5 mm were additionally confirmed by using 3D software, a 5.1 times higher risk for LTP (n = 4 studies, RR: 5.10; 95% CI: 1.45–17.90; p-value < 0.001) was recorded. Moreover, a thermal ablation margin of less than 10 mm but over 5 mm remained significantly associated with 3.64 times higher risk for LTP vs. minimal margin larger than 10 mm (n = 7 studies, RR: 3.64; 95% CI: 1.31–10.10; p-value < 0.001). Conclusions: This meta-analysis solidifies that a minimal ablation margin over 5 mm is the minimum critical endpoint required, whereas a minimal margin of at least 10 mm yields optimal local tumor control after TA of CLMs.","journal":"Cancers","year":2023,"id":321537,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":44,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9573,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":900520,"name":"Vlasios S. Sotirchos","orcid":"0000-0001-7278-1369","position":1,"is_corresponding":false},{"id":530362,"name":"Christos Georgiades","orcid":"0000-0002-4559-1639","position":2,"is_corresponding":false},{"id":713159,"name":"Dimitrios Filippiadis","orcid":"0000-0002-1424-7464","position":3,"is_corresponding":false},{"id":1033686,"name":"Ronald S. Arellano","orcid":"0000-0001-9232-4069","position":4,"is_corresponding":false},{"id":266133,"name":"Mithat Gönen","orcid":"0000-0001-8683-8477","position":5,"is_corresponding":false},{"id":1033687,"name":"Gregory C. Makris","orcid":"0000-0003-3437-621X","position":6,"is_corresponding":false},{"id":978113,"name":"Tushar Garg","orcid":"0000-0002-5964-6354","position":7,"is_corresponding":false},{"id":301657,"name":"Constantinos T. Sofocleous","orcid":"0000-0002-3142-1399","position":8,"is_corresponding":false},{"id":1033685,"name":"David-Dimitris Chlorogiannis","orcid":"0000-0003-1596-9466","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T01:07:32.611759Z","pmid":"38136351","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}