{"doi":"10.3390/cancers15133524","title":"Spectroscopic MRI-Based Biomarkers Predict Survival for Newly Diagnosed Glioblastoma in a Clinical Trial","abstract":"Despite aggressive treatment, glioblastoma has a poor prognosis due to its infiltrative nature. Spectroscopic MRI-measured brain metabolites, particularly the choline to N-acetylaspartate ratio (Cho/NAA), better characterizes the extent of tumor infiltration. In a previous pilot trial (NCT03137888), brain regions with Cho/NAA ≥ 2x normal were treated with high-dose radiation for newly diagnosed glioblastoma patients. This report is a secondary analysis of that trial where spectroscopic MRI-based biomarkers are evaluated for how they correlate with progression-free and overall survival (PFS/OS). Subgroups were created within the cohort based on pre-radiation treatment (pre-RT) median cutoff volumes of residual enhancement (2.1 cc) and metabolically abnormal volumes used for treatment (19.2 cc). We generated Kaplan–Meier PFS/OS curves and compared these curves via the log-rank test between subgroups. For the subgroups stratified by metabolic abnormality, statistically significant differences were observed for PFS (p = 0.019) and OS (p = 0.020). Stratification by residual enhancement did not lead to observable differences in the OS (p = 0.373) or PFS (p = 0.286) curves. This retrospective analysis shows that patients with lower post-surgical Cho/NAA volumes had significantly superior survival outcomes, while residual enhancement, which guides high-dose radiation in standard treatment, had little significance in PFS/OS. This suggests that the infiltrating, non-enhancing component of glioblastoma is an important factor in patient outcomes and should be treated accordingly.","journal":"Cancers","year":2023,"id":377197,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9522,"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":810789,"name":"Karthik K. Ramesh","orcid":null,"position":1,"is_corresponding":false},{"id":442400,"name":"Vicki Huang","orcid":"0000-0003-3029-4700","position":2,"is_corresponding":false},{"id":318014,"name":"Eric A. Mellon","orcid":"0000-0001-9892-8402","position":3,"is_corresponding":false},{"id":256332,"name":"Peter B. Barker","orcid":"0000-0002-6410-7793","position":4,"is_corresponding":false},{"id":250706,"name":"Lawrence Kleinberg","orcid":"0000-0003-2473-2305","position":5,"is_corresponding":false},{"id":428905,"name":"Brent D. Weinberg","orcid":"0000-0002-7992-1747","position":6,"is_corresponding":false},{"id":294153,"name":"Hui‐Kuo G. Shu","orcid":"0000-0002-4060-0874","position":7,"is_corresponding":false},{"id":375815,"name":"Hyunsuk Shim","orcid":"0000-0001-8313-2612","position":8,"is_corresponding":false},{"id":1071580,"name":"Anuradha G. Trivedi","orcid":"0000-0002-3348-4529","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:16:36.370283Z","pmid":"37444634","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":[]}