{"doi":"10.1007/s11060-024-04812-1","title":"Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning","abstract":"PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resonance spectroscopy (WB-MRS) protocol that delves into the intricacies of tumor microenvironments, offering a comprehensive understanding of glioma progression to inform expectant surgical and adjuvant intervention. METHODS: We investigated five locoregional tumor metabolites in a post-treatment population and applied machine learning (ML) techniques to analyze key relationships within seven regions of interest: contralateral normal-appearing white matter (NAWM), fluid-attenuated inversion recovery (FLAIR), contrast-enhancing tumor at time of WB-MRS (Tumor), areas of future recurrence (AFR), whole-brain healthy (WBH), non-progressive FLAIR (NPF), and progressive FLAIR (PF). Five supervised ML classification models and a neural network were developed, optimized, trained, tested, and validated. Lastly, a web application was developed to host our novel calculator, the Miami Glioma Prediction Map (MGPM), for open-source interaction. RESULTS: Sixteen patients with histopathological confirmation of high-grade glioma prior to WB-MRS were included in this study, totaling 118,922 whole-brain voxels. ML models successfully differentiated normal-appearing white matter from tumor and future progression. Notably, the highest performing ML model predicted glioma progression within fluid-attenuated inversion recovery (FLAIR) signal in the post-treatment setting (mean AUC = 0.86), with Cho/Cr as the most important feature. CONCLUSIONS: This study marks a significant milestone as the first of its kind to unveil radiographic occult glioma progression in post-treatment gliomas within 8 months of discovery. These findings underscore the utility of ML-based WB-MRS growth predictions, presenting a promising avenue for the guidance of early treatment decision-making. This research represents a crucial advancement in predicting the timing and location of glioblastoma recurrence, which can inform treatment decisions to improve patient outcomes.","journal":"Journal of Neuro-Oncology","year":2024,"id":431218,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9595,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":890808,"name":"Shovan Bhatia","orcid":"0000-0001-8090-7208","position":1,"is_corresponding":false},{"id":785832,"name":"Alexis A. Morell","orcid":"0000-0002-0388-0467","position":2,"is_corresponding":false},{"id":1044277,"name":"Lekhaj Daggubati","orcid":"0000-0002-5573-8569","position":3,"is_corresponding":false},{"id":1044278,"name":"Martín A. Merenzon","orcid":"0000-0002-2181-9029","position":4,"is_corresponding":false},{"id":758423,"name":"Sulaiman Sheriff","orcid":"0009-0007-2491-2273","position":5,"is_corresponding":false},{"id":258529,"name":"Evan Luther","orcid":"0000-0001-9164-4984","position":6,"is_corresponding":false},{"id":1030805,"name":"Jay Chandar","orcid":"0000-0001-5923-3579","position":7,"is_corresponding":false},{"id":811230,"name":"Adam S. Levy","orcid":"0000-0002-7582-0877","position":8,"is_corresponding":false},{"id":1235518,"name":"Ashley Metzler","orcid":null,"position":9,"is_corresponding":false},{"id":1235019,"name":"Chandler Berke","orcid":"0000-0002-9787-8164","position":10,"is_corresponding":false},{"id":388235,"name":"Mohammed Goryawala","orcid":"0000-0002-6875-2191","position":11,"is_corresponding":false},{"id":318014,"name":"Eric A. Mellon","orcid":"0000-0001-9892-8402","position":12,"is_corresponding":false},{"id":1150150,"name":"Rita Bhatia","orcid":"0000-0001-7019-8399","position":13,"is_corresponding":false},{"id":1235519,"name":"Natalya Nagornaya","orcid":null,"position":14,"is_corresponding":false},{"id":1235020,"name":"Gaurav Saigal","orcid":"0000-0003-4134-2616","position":15,"is_corresponding":false},{"id":620583,"name":"Macarena I. de la Fuente","orcid":"0000-0002-9676-9329","position":16,"is_corresponding":false},{"id":258530,"name":"Ricardo J. Komotar","orcid":"0000-0002-7626-8869","position":17,"is_corresponding":false},{"id":258531,"name":"Michael E. Ivan","orcid":"0000-0002-4798-4989","position":18,"is_corresponding":false},{"id":1060625,"name":"Ashish H. Shah","orcid":"0000-0002-9396-7178","position":19,"is_corresponding":false},{"id":677570,"name":"Cameron Rivera","orcid":null,"position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T01:59:25.642507Z","pmid":"39180640","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":[]}