Bilgin M, Bilgin SS, Akkurt BH, Heindel W, Mannil M, Musigmann M. (2025) Computed tomography-image-based glioma grading using radiomics and machine learning: A proof-of-principle study. Cancers (Basel) [Internet]. 17(2). https://doi.org/10.3390/cancers17020322

Stadlbauer A, Marhold F, Oberndorfer S, Heinz G, Buchfelder M, Kinfe TM, et al. (2022) Radiophysiomics: Brain tumors classification by machine learning and physiological MRI data. Cancers (Basel) [Internet]. 14(10):2363. https://doi.org/10.3390/cancers14102363

Jeong J, Wang L, Ji B, Lei Y, Ali A, Liu T, et al. (2019) Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images: Introduction. Quant Imaging Med Surg [Internet]. 9(7):1201–13. https://doi.org/10.21037/qims.2019.07.01

Zhang S, Richter J, Veale J, Hieu Phan VM, Candy N, Poonnoose S et al (2025) Development of Hybrid radiomic Machine learning models for preoperative prediction of meningioma grade on multiparametric MRI. J Clin Neurosci [Internet] 135(111118):111118. https://doi.org/10.1016/j.jocn.2025.111118

Article  PubMed  Google Scholar 

Zhao G, Man P, Bai J, Li L, Wang P, Yang G et al (2022) AI-powered radiomics algorithm based on slice pooling for the glioma grading. IEEE Trans Industr Inform [Internet] 18(8):5383–5393. https://doi.org/10.1109/tii.2021.3105665

Article  Google Scholar 

Cepeda S, Arrese I, García-García S, Velasco-Casares M, Escudero-Caro T, Zamora T, et al. (2021) Meningioma consistency can be defined by combining the radiomic features of magnetic resonance imaging and ultrasound elastography. A pilot study using machine learning classifiers. World Neurosurg [Internet]. 146:e1147–59. https://doi.org/10.1016/j.wneu.2020.11.113

Li X, Lu Y, Xiong J, Wang D, She D, Kuai X et al (2019) Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis. J Neuroradiol 46(5):281–287. https://doi.org/10.1016/j.neurad.2019.05.013

Article  PubMed  Google Scholar 

Çinarer G, Emiroğlu BG, Yurttakal AH. (2020) Prediction of glioma grades using deep learning with wavelet radiomic features. Appl Sci (Basel) [Internet]. 10(18):6296. https://doi.org/10.3390/app10186296

Kong X, Mao Y, Luo Y, Xi F, Li Y, Ma J (2023) Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification. Acta Radiol [Internet] 64(11):2938–2947. https://doi.org/10.1177/02841851231199744

Article  PubMed  Google Scholar 

Kumar A, Jha AK, Agarwal JP, Yadav M, Badhe S, Sahay A, et al. (2023) Machine-learning-based radiomics for classifying glioma grade from magnetic resonance images of the brain. J Pers Med [Internet]. 13(6). https://doi.org/10.3390/jpm13060920

Xie Y, Li X, Yang S, Jia F, Han Y, Huang M, et al. (2025) Radiomics models using machine learning algorithms to differentiate the primary focus of brain metastasis. Transl Cancer Res [Internet]. 14(2):731–42. https://doi.org/10.21037/tcr-24-1355

Kim M, Jung SY, Park JE, Jo Y, Park SY, Nam SJ et al (2020) Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma. Eur Radiol 30(4):2142–2151. https://doi.org/10.1007/s00330-019-06548-3

Article  PubMed  Google Scholar 

Reyes JS, Lohia VN, Almeida T, Niranjan A, Lunsford LD, Hadjipanayis CG (2025) Artificial intelligence in neurosurgery: a systematic review of applications, model comparisons, and ethical implications. Neurosurg Rev [Internet] 48(1):455. https://doi.org/10.1007/s10143-025-03597-9

Article  PubMed  Google Scholar 

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med [Internet]. 6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097

Ottawa hospital research institute [Internet]. Ohri.ca. [cited 2025 Jun 23]. Available from: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp

Teng Y, Chen C, Zhang Y, Xu J. (2022) The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma. Transl Cancer Res [Internet]. 2022;11(11):4079–88. https://doi.org/10.21037/tcr-22-1390

Lin J, Su C-Q, Tang W-T, Xia Z-W, Lu S-S, Hong X-N (2024) Radiomic features on multiparametric MRI for differentiating pseudoprogression from recurrence in high-grade gliomas. Acta Radiol 65(11):1390–1400. https://doi.org/10.1177/02841851241283781

Article  PubMed  Google Scholar 

Hosseini SA, Hosseini E, Hajianfar G, Shiri I, Servaes S, Rosa-Neto P et al (2023) MRI-based radiomics combined with deep learning for distinguishing IDH-mutant WHO grade 4 astrocytomas from IDH-wild-type glioblastomas. Cancers (Basel) 15(3):951. https://doi.org/10.3390/cancers15030951

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sun P, Wang D, Mok VC, Shi L (2019) Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading. IEEE Access [Internet] 7:102010–102020. https://doi.org/10.1109/access.2019.2928975

Article  Google Scholar 

Martínez Barbero JP, García FJP, López Cornejo D, García Cerezo M, Gutiérrez PMJ, Balderas L et al (2025) A combined approach using T2*-weighted Dynamic Susceptibility Contrast MRI perfusion parameters and radiomics to differentiate between radionecrosis and glioma progression: A proof-of-concept study. Life (Basel) 15(4):606. https://doi.org/10.3390/life15040606

Article  PubMed  PubMed Central  Google Scholar 

Wu S, Meng J, Yu Q, Li P, Fu S (2019) Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas. J Cancer Res Clin Oncol [Internet] 145(3):543–550. https://doi.org/10.1007/s00432-018-2787-1

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fan Y, Chen C, Zhao F, Tian Z, Wang J, Ma X et al (2019) Radiomics-based machine learning technology enables better differentiation between glioblastoma and anaplastic oligodendroglioma. Front Oncol [Internet] 9:1164. https://doi.org/10.3389/fonc.2019.01164

Article  PubMed  PubMed Central  Google Scholar 

Zhu F-Y, Sun Y-F, Yin X-P, Zhang Y, Xing L-H, Ma Z-P et al (2023) Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes. Discov Oncol [Internet] 14(1):224. https://doi.org/10.1007/s12672-023-00837-6

Article  PubMed  PubMed Central  Google Scholar 

Yang WL, Su XR, Li S, Zhao KY, Yue Q (2024) Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases. Front Neurol [Internet] 15:1474461. https://doi.org/10.3389/fneur.2024.1474461

Article  CAS  PubMed  Google Scholar 

Kandalgaonkar P, Sahu A, Saju AC, Joshi A, Mahajan A, Thakur M et al (2022) Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach. Front Oncol 12:879376. https://doi.org/10.3389/fonc.2022.879376

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bathla G, Priya S, Liu Y, Ward C, Le NH, Soni N et al (2021) Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques. Eur Radiol 31(11):8703–8713. https://doi.org/10.1007/s00330-021-07845-6

Article  CAS  PubMed  Google Scholar 

Kalasauskas D, Kosterhon M, Kurz E, Schmidt L, Altmann S, Grauhan NF et al (2024) Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics. Sci Rep 14(1):20586. https://doi.org/10.1038/s41598-024-71200-0

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhao Z, Nie C, Zhao L, Xiao D, Zheng J, Zhang H et al (2024) Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas. Eur Radiol [Internet] 34(4):2468–2479. https://doi.org/10.1007/s00330-023-10252-8

Article  PubMed  Google Scholar 

Saxena S, Agrawal A, Dash P, Jena B, Khanna NN, Paul S et al (2023) Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework. Neural Comput Appl [Internet] 35(18):13647–13663. https://doi.org/10.1007/s00521-023-08405-3

Article  Google Scholar 

Tariciotti L, Caccavella VM, Fiore G, Schisano L, Carrabba G, Borsa S et al (2022) A deep learning model for preoperative differentiation of glioblastoma, brain metastasis and primary central nervous system lymphoma: a pilot study. Front Oncol 12:816638. https://doi.org/10.3389/fonc.2022.816638

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wu M, Jiang T, Guo M, Duan Y, Zhuo Z, Weng J et al (2024) Amide proton transfer-weighted imaging and derived radiomics in the classification of adult-type diffuse gliomas. Eur Radiol 34(5):2986–2996. https://doi.org/10.1007/s00330-023-10343-6

Article  CAS  PubMed  Google Scholar 

Takahashi S, Takahashi W, Tanaka S, Haga A, Nakamoto T, Suzuki Y et al (2019) Radiomics analysis for glioma malignancy evaluation using diffusion kurtosis and tensor imaging. Int J Radiat Oncol Biol Phys [Internet] 105(4):784–791. https://doi.org/10.1016/j.ijrobp.2019.07.011

Article  PubMed  Google Scholar 

Wang Z, He C, Hu Y, Luo H, Li C, Wu X et al (2024) A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors. Front Oncol 14:1363756. https://doi.org/10.3389/fonc.2024.1363756

Article  PubMed  PubMed Central  Google Scholar 

Müller M, Winz O, Gutsche R, Leijenaar RTH, Kocher M, Lerche C et al (2022) Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression. J Neurooncol [Internet] 159(3):519–529. https://doi.org/10.1007/s11060-022-04089-2

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bai J, He M, Gao E, Yang G, Yang H, Dong J et al (2023) Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis. BMC Cancer 23(1):1231. https://doi.o