{"doi":"10.1093/nop/npaf125","title":"Identifying predictive factors for radiation necrosis vs local recurrence in biopsy-proven enlarging lesions post-stereotactic radiosurgery for brain metastases","abstract":"Background: Stereotactic radiosurgery (SRS) remains the standard of care for brain metastases (BM), offering effective local control with fewer neurocognitive side effects. However, distinguishing radiation necrosis (RN) from local recurrence (LR) is challenging in lesions that enlarge post-SRS. Identifying clinical and treatment-related factors associated with RN vs LR may improve decision-making. Prior smaller studies have suggested time to progression may help differentiate RN from LR; we aimed to reassess this observation and explore other factors in a larger cohort. Methods: We retrospectively analyzed patients treated with SRS for BM between 2011 and 2022 who later underwent biopsy, with or without LITT, for imaging-suspected RN vs LR. Data included demographics, clinicopathologic characteristics, SRS-treatment parameters, and biopsy results. Results: = 108) had complete dosimetric data. Median age at SRS was 62 years, with a median SRS-biopsy interval of 13 months. The median prescribed dose was 20 Gy for single-fraction and 27.5 Gy for hypofractionated regimens. Multivariable models identified age ≥65 and SRS to biopsy interval ≥9 months as predictors of RN in the full cohort. In the subset group with detailed dosimetric data, V12Gy ≥ median (4.64 cm³) additionally predicted RN and improved model fit. Conclusion: We identified older age, a longer SRS to biopsy interval, and higher V12Gy as predictors of RN. Our predictive models incorporate these variables and may support clinical decision-making, improve diagnostic accuracy, and optimize patient outcomes.","journal":"Neuro-Oncology Practice","year":2025,"id":535711,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9673,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1088907,"name":"Aden Haskell-Mendoza","orcid":"0000-0002-9186-7261","position":1,"is_corresponding":false},{"id":1419864,"name":"Ellery Reason","orcid":"0009-0004-0922-4446","position":2,"is_corresponding":false},{"id":494497,"name":"Joshua Jackson","orcid":"0000-0002-9736-2096","position":3,"is_corresponding":false},{"id":345257,"name":"Chunhao Wang","orcid":"0000-0002-6945-7119","position":4,"is_corresponding":false},{"id":1057411,"name":"Eugene Vaios","orcid":"0000-0003-2122-6650","position":5,"is_corresponding":false},{"id":1420355,"name":"Claire Bradbury","orcid":null,"position":6,"is_corresponding":false},{"id":1419865,"name":"Jingtong Zhao","orcid":"0009-0001-6674-3144","position":7,"is_corresponding":false},{"id":281444,"name":"James E. Herndon","orcid":"0000-0001-7998-3588","position":8,"is_corresponding":false},{"id":564965,"name":"Peter E. Fecci","orcid":"0000-0002-2912-8695","position":9,"is_corresponding":false},{"id":1419863,"name":"Ariel T Gonzalez","orcid":"0009-0008-0540-4262","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T02:52:00.885532Z","pmid":"42312117","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":[]}