Abayazeed AH, Abbassy A, Müeller M, Hill M, Qayati M, Mohamed S, Mekhaimar M, Raymond C, Dubey P, Nael K, Rohatgi S, Kapare V, Kulkarni A, Shiang T, Kumar A, Andratschke N, Willmann J, Brawanski A, De Jesus R, Tuna I, Fung SH, Landolfi JC, Ellingson BM, Reyes M (2023) NS-HGlio: a generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics. Neuro-Oncol Adv 5(1):vdac184. https://doi.org/10.1093/noajnl/vdac184
Google Scholar
Akbari H, Kazerooni AF, Ware JB, Mamourian E, Anderson H, Guiry S, Sako C, Raymond C, Yao J, Brem S (2021) Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging. Sci Rep 11(1):15011
Google Scholar
Al-Rahbi A, Omar Al-Mahrouqi, and, Tariq Al-Saadi (2024) Uses of artificial intelligence in glioma: A systematic review. Med Int 4(4):40
Google Scholar
Barhoumi Y, Fattah AH, Bouaynaya N, Moron F, Kim J, Fathallah-Shaykh HM, Chahine RA, Sotoudeh H (2024) Robust AI-driven segmentation of glioblastoma T1c and FLAIR MRI series and the low variability of the MRIMath© smart manual contouring platform. Diagnostics 14(11):1066
Google Scholar
Beig N, Patel J, Prasanna P, Partovi S, Varadan V, Madabhushi A, Tiwari P (2017) Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma. Medical imaging 2017: computer-aided diagnosis. SPIE, Cergy Pontoise
Google Scholar
Belue MJ, Harmon SA, Chappidi S, Zhuge Y, Tasci E, Jagasia S, Joyce T, Camphausen K, Turkbey B, Krauze AV (2024) Diagnosing progression in glioblastoma-tackling a neuro-oncology problem using artificial-intelligence-derived volumetric change over time on magnetic resonance imaging to examine progression-free survival in glioblastoma. Diagnostics. https://doi.org/10.3390/diagnostics14131374
Google Scholar
Bhandari A, Scott L, Weilbach M, Marwah R, Lasocki A (2023) Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM). Neuroradiology 65(5):907–913. https://doi.org/10.1007/s00234-023-03126-9
Google Scholar
Bianconi A, Rossi LF, Bonada M, Zeppa P, Nico E, De Marco R, Lacroce P, Cofano F, Bruno F, Morana G (2023) Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment. Brain Inf 10(1):26
Google Scholar
Bolcaen J, Kleynhans J, Nair S, Verhoeven J, Goethals I, Sathekge M, Vandevoorde C, Ebenhan T (2021) A perspective on the radiopharmaceutical requirements for imaging and therapy of glioblastoma. Theranostics 11(16):7911
Google Scholar
Booth TC, Williams M, Luis A, Cardoso J, Ashkan K, Shuaib H (2020) Machine learning and glioma imaging biomarkers. Clin Radiol 75(1):20–32
Google Scholar
Breto AL, Cullison K, Zacharaki EI, Wallaengen V, Maziero D, Jones K, Valderrama A, de la Fuente MI, Meshman J, Azzam GA (2023) A deep learning approach for automatic segmentation during daily MRI-Linac radiotherapy of glioblastoma. Cancers 15(21):5241
Google Scholar
Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Clarke JL, Solomon DA, Cha S (2022a) Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neuro-Oncol Adv 4(1):vdac060. https://doi.org/10.1093/noajnl/vdac060
Google Scholar
Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Clarke JL, Solomon DA, Cha S (2022b) Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neuro-Oncol Adv 4(1):vdac060
Google Scholar
Cè M, Irmici G, Foschini C, Danesini GM, Falsitta LV, Serio ML, Fontana A, Martinenghi C, Oliva G, Cellina M (2023) Artificial intelligence in brain tumor imaging: a step toward personalized medicine. Current Oncology 30(3):2673–2701
Google Scholar
Cerina V, Rui CB, Di Cristofori A, Ferlito D, Carrabba G, Giussani C, Basso G, De Bernardi E (2025) Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas. Sci Rep 15(1):2160
Google Scholar
Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27(3):475–487
Google Scholar
Chatterjee S, Sil PC (2019) Targeting the crosstalks of Wnt pathway with Hedgehog and Notch for cancer therapy. Pharmacol Res 142:251–261. https://doi.org/10.1016/j.phrs.2019.02.027
Google Scholar
Chen J, Li Y, Yu TS, McKay RM, Burns DK, Kernie SG, Parada LF (2012) A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488(7412):522–526. https://doi.org/10.1038/nature11287
Google Scholar
Cheung EYW, Wu RWK, Li ASM, Chu ESM (2023) AI deployment on GBM diagnosis: a novel approach to analyze histopathological images using image feature-based analysis. Cancers (Basel). https://doi.org/10.3390/cancers15205063
Google Scholar
Chow D, Chang P, Weinberg BD, Bota DA, Grinband J, Filippi CG (2018) Imaging genetic heterogeneity in glioblastoma and other glial tumors: review of current methods and future directions. AJR Am J Roentgenol 210(1):30–38
Google Scholar
Comba A, Faisal SM, Varela ML, Hollon T, Al-Holou WN, Umemura Y, Nunez FJ, Motsch S, Castro MG, Lowenstein PR (2021a) Uncovering spatiotemporal heterogeneity of high-grade gliomas: from disease biology to therapeutic implications. Front Oncol. https://doi.org/10.3389/fonc.2021.703764
Google Scholar
Comba A, Faisal SM, Varela ML, Hollon T, Al-Holou WN, Umemura Y, Nunez FJ, Motsch S, Castro MG, Lowenstein PR (2021b) Uncovering spatiotemporal heterogeneity of high-grade gliomas: from disease biology to therapeutic implications. Front Oncol 11:703764
Google Scholar
Crivii C-B, Boșca AB, Melincovici CS, Constantin A-M, Mărginean M, Dronca E, Suflețel R, Gonciar D, Bungărdean M, Șovrea A (2022) Glioblastoma microenvironment and cellular interactions. Cancers 14(4):1092
Google Scholar
de Godoy LL, Chawla S, Brem S, Mohan S (2023) Taming glioblastoma in “Real Time”: integrating multimodal advanced neuroimaging/AI tools towards creating a robust and therapy agnostic model for response assessment in neuro-oncology. Clin Cancer Res 29(14):2588–2592. https://doi.org/10.1158/1078-0432.Ccr-23-0009
Google Scholar
Dorfner FJ, Patel JB, Kalpathy-Cramer J, Gerstner ER, Bridge CP (2025) A review of deep learning for brain tumor analysis in MRI. NPJ Precis Oncol 9(1):2
Google Scholar
El Atat O, Naser R, Abdelkhalek M, Abi Habib R, El Sibai M (2022) Molecular targeted therapy: a new avenue in glioblastoma treatment. Oncol Lett 25(2):46
Google Scholar
Erices JI, Bizama C, Niechi I, Uribe D, Rosales A, Fabres K, Navarro-Martínez G, Torres Á, San Martín R, Roa JC (2023) Glioblastoma microenvironment and invasiveness: new insights and therapeutic targets. International journal of molecular sciences 24(8):7047
Google Scholar
Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C (2020) Imaging signatures of glioblastoma molecular characteristics: a radiogenomics review. J Magn Reson Imaging 52(1):54–69
Google Scholar
Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP (2021a) Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine. Cancers (Basel). https://doi.org/10.3390/cancers13235921
Google Scholar
Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S (2021b) Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine. Cancers 13(23):5921
Google Scholar
Fathi Kazerooni A, Saxena S, Toorens E, Tu D, Bashyam V, Akbari H, Mamourian E, Sako C, Koumenis C, Verginadis I (2022) Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma. Sci Rep 12(1):8784
Google Scholar
Franco D, Granata V, Fusco R, Grassi R, Nardone V, Lombardi L, Cappabianca S, Conforti R, Briganti F, Grassi R, Caranci F (2023) Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool. Radiol Med 128(7):813–827. https://doi.org/10.1007/s11547-023-01655-0
Google Scholar
Garcia-Montano LA, Licon-Munoz Y, Martinez FJ, Keddari YR, Ziemke MK, Chohan MO, Piccirillo SG (2023) Dissecting intra-tumor heterogeneity in the glioblastoma microenvironment using fluorescence-guided multiple sampling. Mol Cancer Res 21(8):755–767
Google Scholar
Hambardzumyan D, Gutmann DH, Kettenmann H (2016) The role of microglia and macrophages in glioma maintenance and progression. Nat Neurosci 19(1):20–27. https://doi.org/10.1038/nn.4185
Google Scholar
Hamilton BE, Barajas R, Nesbit GM, Fu R, Ambady P, Taylor M, Neuwelt EA (2020) Ferumoxytol-enhanced MRI is not inferior to gadolinium-enhanced MRI in detecting intracranial metastatic disease and metastasis size. AJR Am J Roentgenol 215(6):1436–1442. https://doi.org/10.2214/ajr.19.22187
Google Scholar
Hardee ME, Zagzag D (2012) Mechanisms of glioma-associated neovascularization. Am J Pathol 181(4):1126–1141. https://doi.org/10.1016/j.ajpath.2012.06.030
Google Scholar
Heiland DH, Ravi VM, Behringer SP, Frenking JH, Wurm J, Joseph K, Garrelfs NWC, Strähle J, Heynckes S, Grauvogel J, Franco P, Mader I, Schneider M, Potthoff A-L, Delev D, Hofmann UG, Fung C, Beck J, Sankowski R, Prinz M, Schnell O (2019) Tumor-associated reactive astrocytes aid the evolution of immunosuppressive environment in glioblastoma. Nature Commun. https://doi.org/10.1038/s41467-019-10493-6
Google Scholar
Hoebel KV, Patel JB, Beers AL, Chang K, Singh P, Brown JM, Pinho MC, Batchelor TT, Gerstner ER, Rosen BR, Kalpathy-Cramer J (2021) Radiomics repeatability pitfalls in a scan-rescan MRI study of glioblastoma. Radiol: Artif Intell 3(1):e190199. https://doi.org/10.1148/ryai.2020190199
Google Scholar
Hooper GW, Ginat DT (2023) MRI radiomics and potential applications to glioblastoma. Front Oncol 13:1134109. https://doi.org/10.3389/fonc.2023.1134109
Google Scholar
Hossain SF, Amin MA, Liza IA, Ahmed S, Haque MM, Islam MA, Akter S (2023) AI-based brain MRI segmentation for early diagnosis and treatment planning of low-grade gliomas in the USA. Br J Nurs 3(2):37–55
Google Scholar
Hsieh KL, Chen Q, Salzillo TC, Zhang J, Jiang X, Bhattacharya PK, Shams S (2024) Hyperpolarized magnetic resonance imaging, nuclear magnetic resonance metabolomics, and artificial intelligence to interrogate the metabolic evolution of glioblastoma. Metabolites. https://doi.org/10.3390/metabo14080448
Google Scholar
Huang M, Law HKW, Tam SY (2025) Use of radiomics in characterizing tumor hypoxia. Int J Mol Sci. https://doi.org/10.3390/ijms26146679
Google Scholar
Iqbal MS, Bajwa UI, Raza R, Anwar MW (2025) Integrated brain tumor segmentation and MGMT promoter methylation status classification from multimodal MRI data using deep learning. Digit Health 11:20552076251332018. https://doi.org/10.1177/20552076251332018
Google Scholar
Islam M, Wijethilake N, Ren H (2021) Glioblastoma multiforme prognosis: MRI missing modality generation, segmentation and radiogenomic survival prediction. Comput Med Imaging Graph 91:101906. https://doi.org/10.1016/j.compmedimag.2021.101906
Google Scholar
Israel LL, Galstyan A, Holler E, Ljubimova JY (2020) Magnetic iron oxide nanoparticles for imaging, targeting and treatment of primary and metastatic tumors of the brain. J Control Release 320:45–62. https://doi.org/10.1016/j.jconrel.2020.01.009
Google Scholar
Jang K, Back M (2025) Spatial mapping of glioblastoma infiltration: diffusion tensor imaging-based radiomics and connectomics in recurrence prediction. Brain Sciences. https://doi.org/10.3390/brainsci15060576
Google Scholar
Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M (2024) Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol 8(1):80
Google Scholar
Khazaee Z, Langarizadeh M, Ahmadabadi MES (2022) Developing an artificial intelligence model for tumor gradingclassification, based on MRI sequences of human brain gliomas. Int J Cancer Manag 15(1):e120638
Google Scholar
Kumar M, Nanga RPR, Verma G, Wilson N, Brisset JC, Nath K, Chawla S (2022) Emerging MR imaging and spectroscopic methods to study brain tumor metabolism. Front Neurol 13:789355
Google Scholar
Kushol R, Parnianpour P, Wilman AH, Kalra S, Yang YH (2023) Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci Rep 13(1):16791. https://doi.org/10.1038/s41598-023-43715-5
Comments (0)