{"doi":"10.3174/ajnr.a7488","title":"Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma","abstract":"BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: = 29-43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index = 0.472-0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692-0.750) and progression-free survival (concordance index = 0.680-0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.","journal":"American Journal of Neuroradiology","year":2022,"id":242831,"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":36,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9562,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":868730,"name":"Elizabeth Flagg","orcid":"0000-0003-4111-9609","position":1,"is_corresponding":false},{"id":256380,"name":"Ken Chang","orcid":"0000-0001-6956-5059","position":2,"is_corresponding":false},{"id":259901,"name":"Harrison X. Bai","orcid":"0000-0002-7460-8866","position":3,"is_corresponding":false},{"id":37161,"name":"Hugo J.W.L. Aerts","orcid":"0000-0002-2122-2003","position":4,"is_corresponding":false},{"id":103869,"name":"Martin Vallières","orcid":"0000-0001-7639-8172","position":5,"is_corresponding":false},{"id":25295,"name":"David A. Reardon","orcid":"0000-0001-6674-0157","position":6,"is_corresponding":false},{"id":259894,"name":"Raymond Y. Huang","orcid":"0000-0001-7661-797X","position":7,"is_corresponding":false},{"id":564652,"name":"Elizabeth George","orcid":"0000-0003-3141-5738","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T00:23:10.218426Z","pmid":"35483906","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":[]}