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Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study
H. Akbari, S. Bakas, C. Sako, A. Fathi Kazerooni, J. Villanueva-Meyer, JA. Garcia, E. Mamourian, F. Liu, Q. Cao, RT. Shinohara, U. Baid, A. Getka, S. Pati, A. Singh, E. Calabrese, S. Chang, J. Rudie, A. Sotiras, P. LaMontagne, DS. Marcus, M....
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články, multicentrická studie
Grantová podpora
U01CA242871
NCI NIH HHS - United States
U01 CA242871
NCI NIH HHS - United States
U24CA189523
NCI NIH HHS - United States
R01 CA269948
NCI NIH HHS - United States
U24 CA189523
NCI NIH HHS - United States
R01CA269948
NCI NIH HHS - United States
NIH HHS - United States
PubMed
39665363
DOI
10.1093/neuonc/noae260
Knihovny.cz E-zdroje
- MeSH
- dospělí MeSH
- glioblastom * patologie klasifikace mortalita diagnostické zobrazování MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- míra přežití MeSH
- mladý dospělý MeSH
- nádory mozku * patologie klasifikace mortalita diagnostické zobrazování MeSH
- následné studie MeSH
- prognóza MeSH
- senioři MeSH
- strojové učení * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. METHODS: We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). RESULTS: The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. CONCLUSIONS: Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.
B ARGO Group Institut Investigació Germans Trias i Pujol Catalonia Spain
Biostatistics Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA
Brain and Tumor Neurosurgery Neurosurgical Oncology Piedmont Health Atlanta Georgia USA
Brain Tumor Center Severance Hospital Yonsei University Health System Seoul Republic of Korea
Central Brain Tumor Registry of the United States Hinsdale Illinois USA
Department of Bioengineering School of Engineering Santa Clara University Santa Clara California USA
Department of Biomedical Engineering University of Wisconsin Madison Wisconsin USA
Department of Neurological Surgery Indiana University School of Medicine Indianapolis Indiana USA
Department of Neurological Surgery Okayama University Okayama Japan
Department of Neurology The University of Alabama at Birmingham Birmingham Alabama USA
Department of Neuropathology Royal Prince Alfred Hospital Camperdown Australia
Department of Neuroradiology Ruskin Wing King's College Hospital NHS Foundation Trust London UK
Department of Neuroradiology Technical University of Munich Munchen Germany
Department of Neurosurgery Chris O'Brien Lifehouse Camperdown Australia
Department of Neurosurgery Hamamatsu University School of Medicine Hamamatsu Shizuoka Japan
Department of Neurosurgery New York University Langone Health New York New York USA
Department of Neurosurgery University Hospital Río Hortega Valladolid Spain
Department of Neurosurgery University of Maryland School of Medicine Baltimore Maryland USA
Department of Neurosurgery University of Texas Medical Branch Galveston Texas USA
Department of Neurosurgery Yonsei University College of Medicine Seoul Republic of Korea
Department of Radiation Oncology Christiana Care Health System Philadelphia Pennsylvania USA
Department of Radiology Duke University Durham North Carolina USA
Department of Radiology Henry Ford Health System Detroit Michigan USA
Department of Radiology Hospital Clínic and IDIBAPS Barcelona Spain
Department of Radiology Na Homolce Hospital Prague Czechia
Department of Radiology New York University Langone Health New York New York USA
Department of Radiology University of California San Diego San Diego California USA
Department of Radiology University of Pittsburgh Pittsburgh Pennsylvania USA
Department of Radiology University of Wisconsin Madison Wisconsin USA
Department of Radiology Washington University School of Medicine St Louis Missouri USA
Department of Radiology Yonsei University College of Medicine Seoul Republic of Korea
Division of Neurosurgery Spedali Riuniti di Livorno Azienda USL Toscana Nord Ovest Livorno Italy
Faculty of Medicine and Health University of Sydney Camperdown Australia
Hillman Cancer Center University of Pittsburgh Medical Center Pittsburgh Pennsylvania USA
MD Anderson Cancer Center University of Texas Houston Texas USA
Research Unit Image Diagnosis Institute Badalona Spain
School of Biomedical Engineering and Imaging Sciences King's College London London UK
Sidney Kimmel Medical College Thomas Jefferson University Philadelphia Pennsylvania USA
Tata Memorial Centre Homi Bhabha National Institute Mumbai India
The Clatterbridge Cancer Centre NHS Foundation Trust Liverpool UK
Trans Divisional Research Program National Cancer Institute Bethesda Maryland USA
Citace poskytuje Crossref.org
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- $a Akbari, Hamed $u Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, California, USA $1 https://orcid.org/0000000197863707
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- $a Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study / $c H. Akbari, S. Bakas, C. Sako, A. Fathi Kazerooni, J. Villanueva-Meyer, JA. Garcia, E. Mamourian, F. Liu, Q. Cao, RT. Shinohara, U. Baid, A. Getka, S. Pati, A. Singh, E. Calabrese, S. Chang, J. Rudie, A. Sotiras, P. LaMontagne, DS. Marcus, M. Milchenko, A. Nazeri, C. Balana, J. Capellades, J. Puig, C. Badve, JS. Barnholtz-Sloan, AE. Sloan, V. Vadmal, K. Waite, M. Ak, RR. Colen, YW. Park, SS. Ahn, JH. Chang, YS. Choi, SK. Lee, GS. Alexander, AS. Ali, AP. Dicker, AE. Flanders, S. Liem, J. Lombardo, W. Shi, G. Shukla, B. Griffith, LM. Poisson, LR. Rogers, A. Kotrotsou, TC. Booth, R. Jain, M. Lee, A. Mahajan, A. Chakravarti, JD. Palmer, D. DiCostanzo, H. Fathallah-Shaykh, S. Cepeda, OS. Santonocito, AL. Di Stefano, B. Wiestler, ER. Melhem, GF. Woodworth, P. Tiwari, P. Valdes, Y. Matsumoto, Y. Otani, R. Imoto, M. Aboian, S. Koizumi, K. Kurozumi, T. Kawakatsu, K. Alexander, L. Satgunaseelan, AM. Rulseh, SJ. Bagley, M. Bilello, ZA. Binder, S. Brem, AS. Desai, RA. Lustig, E. Maloney, T. Prior, N. Amankulor, MP. Nasrallah, DM. O'Rourke, S. Mohan, C. Davatzikos, ReSPOND consortium
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- $a BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. METHODS: We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). RESULTS: The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. CONCLUSIONS: Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.
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