Hansford JR, Das A, McGee RB, Nakano Y, Brzezinski J, Scollon SR et al (2024) Update on cancer predisposition syndromes and surveillance guidelines for childhood brain tumors. Clin Cancer Res [Internet].;30(11):2342–50. Available from: https://doi.org/10.1158/1078-0432.CCR-23-4033
Ostrom QT, Adel Fahmideh M, Cote DJ, Muskens IS, Schraw JM, Scheurer ME et al (2019) Risk factors for childhood and adult primary brain tumors. Neuro Oncol [Internet].;21(11):1357–75. Available from: https://doi.org/10.1093/neuonc/noz123
Forghani R (2020) Precision digital Oncology: Emerging role of radiomics-based biomarkers and artificial intelligence for advanced imaging and characterization of brain tumors. Radiol Imaging Cancer [Internet].;2(4):e190047. Available from: https://doi.org/10.1148/rycan.2020190047
Majzner RG, Theruvath JL, Nellan A, Heitzeneder S, Cui Y, Mount CW et al (2019) CAR T cells targeting B7-H3, a pan-cancer antigen, demonstrate potent preclinical activity against pediatric solid tumors and brain tumors. Clin Cancer Res [Internet].;25(8):2560–74. Available from: https://doi.org/10.1158/1078-0432.CCR-18-0432
Tran S, Bielle F (2022) WHO 2021 and beyond: new types, molecular markers and tools for brain tumor classification. Curr Opin Oncol [Internet].;34(6):670–5. Available from: https://doi.org/10.1097/CCO.0000000000000903
Lago C, Federico A, Leva G, Mack NL, Schwalm B, Ballabio C et al (2023) Patient- and xenograft-derived organoids recapitulate pediatric brain tumor features and patient treatments. EMBO Mol Med [Internet].;15(12):e18199. Available from: https://doi.org/10.15252/emmm.202318199
Madhogarhia R, Haldar D, Bagheri S, Familiar A, Anderson H, Arif S et al (2022) Radiomics and radiogenomics in pediatric neuro-oncology: A review. Neurooncol Adv [Internet].;4(1):vdac083. Available from: https://doi.org/10.1093/noajnl/vdac083
Chilaca-Rosas M-F, Contreras-Aguilar M-T, Garcia-Lezama M, Salazar-Calderon D-R, Vargas-Del-Angel R-G, Moreno-Jimenez S et al (2023) Identification of radiomic signatures in brain MRI sequences T1 and T2 that differentiate tumor regions of Midline gliomas with H3.3K27M mutation. Diagnostics (Basel) [Internet].;13(16):2669. Available from: https://doi.org/10.3390/diagnostics13162669
Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A et al (2021) Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging [Internet].;12(1):152. Available from: https://doi.org/10.1186/s13244-021-01102-6
Sanvito F, Castellano A, Falini A (2021) Advancements in neuroimaging to unravel biological and molecular features of brain tumors. Cancers (Basel) [Internet].;13(3):424. Available from: https://doi.org/10.3390/cancers13030424
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. Available from: https://doi.org/10.1371/journal.pmed.1000097
Ottawa hospital research institute [Internet] Ohri.ca. [cited 2025 Jul 20]. Available from: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
Tejani AS, Klontzas ME, Gatti AA, Mongan JT, Moy L, Park SH et al (2024) Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update. Radiology: Artificial Intelligence.;6(4). Available from: https://doi.org/10.1148/ryai.240300 https://pubs.rsna.orghttps://doi.org/10.1148/ryai.240300
Harris CR, Millman KJ, van der Walt SJ et al (2020) Array programming with NumPy. Nature 585:357–362. Available from: https://doi.org/10.1038/s41586-020-2649-2
Virtanen P, Gommers R, Oliphant TE et al (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17:261–272. Available from: https://doi.org/10.1038/s41592-019-0686-2
The pandas development team (2020) pandas-dev/pandas: Pandas. Zenodo. Available from: https://doi.org/10.5281/zenodo.3509134
Seabold S, Perktold J (2010) Statsmodels: Econometric and Statistical Modeling with Python. Available from: https://doi.org/10.25080/Majora-92bf1922-011
Yarkoni T, Salo T, Peraza JA, Nichols TE (2022) PyMARE: 0.0.10. Zenodo. Available from: https://zenodo.org/records/15298658
Ladefoged CN, Marner L, Hindsholm A, Law I, Højgaard L, Andersen FL (2018) Deep learning based attenuation correction of PET/MRI in pediatric brain tumor patients: Evaluation in a clinical setting. Front Neurosci [Internet].;12:1005. Available from: https://doi.org/10.3389/fnins.2018.01005
Voicu IP, Dotta F, Napolitano A, Caulo M, Piccirilli E, D’Orazio C et al (2024) Machine learning analysis in diffusion kurtosis imaging for discriminating pediatric posterior Fossa tumors: A repeatability and accuracy pilot study. Cancers (Basel) [Internet].;16(14):2578. Available from: https://doi.org/10.3390/cancers16142578
Liu Z, Ren S, Zhang H, Liao Z, Liu Z, An X et al (2025) Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma. Eur Radiol [Internet].;35(8):5053–63. Available from: https://doi.org/10.1007/s00330-025-11385-8
Zhang M, Wong SW, Lummus S, Han M, Radmanesh A, Ahmadian SS et al (2021) Radiomic phenotypes distinguish atypical teratoid/rhabdoid tumors from medulloblastoma. AJNR Am J Neuroradiol [Internet].;42(9):1702–8. Available from: https://doi.org/10.3174/ajnr.A7200
Zhang M, Wong SW, Wright JN, Wagner MW, Toescu S, Han M et al (2022) MRI Radiogenomics of pediatric medulloblastoma: A multicenter study. Radiology [Internet].;304(2):406–16. Available from: https://doi.org/10.1148/radiol.212137
Dong J, Li L, Liang S, Zhao S, Zhang B, Meng Y et al (2021) Differentiation between ependymoma and medulloblastoma in children with radiomics approach. Acad Radiol [Internet].;28(3):318–27. Available from: https://doi.org/10.1016/j.acra.2020.02.012
Mahajan A, Burrewar M, Agarwal U, Kss B, Mlv A, Guha A et al (2023) Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction. Explor Target Antitumor Ther [Internet].;4(4):669–84. Available from: https://doi.org/10.37349/etat.2023.00159
Zhang M, Wong SW, Wright JN, Toescu S, Mohammadzadeh M, Han M et al (2021) Machine assist for pediatric posterior Fossa tumor diagnosis: A multinational study. Neurosurgery [Internet].;89(5):892–900. Available from: https://doi.org/10.1093/neuros/nyab311
Liu Z, Hong X, Wang L, Ma Z, Guan F, Wang W et al (2023) Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas. BMC Cancer [Internet].;23(1):848. Available from: https://doi.org/10.1186/s12885-023-11338-8
Chang F-C, Wong T-T, Wu K-S, Lu C-F, Weng T-W, Liang M-L et al (2021) Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma. PLoS One [Internet].;16(7):e0255500. Available from: https://doi.org/10.1371/journal.pone.0255500
Novak J, Zarinabad N, Rose H, Arvanitis T, MacPherson L, Pinkey B et al (2021) Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Sci Rep [Internet].;11(1):2987. Available from: https://doi.org/10.1038/s41598-021-82214-3
Wang S, Wang G, Zhang W, He J, Sun W, Yang M et al (2022) MRI-based whole-tumor radiomics to classify the types of pediatric posterior fossa brain tumor. Neurochirurgie [Internet].;68(6):601–7. Available from: https://doi.org/10.1016/j.neuchi.2022.05.004
Zhou H, Hu R, Tang O, Hu C, Tang L, Chang K et al (2020) Automatic machine learning to differentiate pediatric posterior Fossa tumors on routine MR imaging. AJNR Am J Neuroradiol [Internet].;41(7):1279–85. Available from: https://doi.org/10.3174/ajnr.A6621
Vafaeikia P, Wagner MW, Hawkins C, Tabori U, Ertl-Wagner BB, Khalvati F (2024) MRI-based end-to-end pediatric low-grade glioma segmentation and classification. Can Assoc Radiol J [Internet].;75(1):153–60. Available from: https://doi.org/10.1177/08465371231184780
Yimit Y, Yasin P, Tuersun A, Wang J, Wang X, Huang C et al (2024) Multiparametric MRI-based interpretable radiomics machine learning model differentiates medulloblastoma and Ependymoma in children: A two-center study. Acad Radiol [Internet].;31(8):3384–96. Available from: https://doi.org/10.1016/j.acra.2024.02.040
Zhou T, Qiao B, Peng B, Liu Y, Gong Z, Kang M et al (2024) Predicting histological grade in pediatric glioma using multiparametric radiomics and conventional MRI features. Sci Rep [Internet].;14(1):13683. Available from: https://doi.org/10.1038/s41598-024-63222-5
Zheng H, Li J, Liu H, Wu C, Gui T, Liu M et al (2021) Clinical-MRI radiomics enables the prediction of preoperative cerebral spinal fluid dissemination in children with medulloblastoma. World J Surg Oncol [Internet].;19(1):134. Available from: https://doi.org/10.1186/s12957-021-02239-w
Rodriguez Gutierrez D, Awwad A, Meijer L, Manita M, Jaspan T, Dineen RA et al (2014) Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors. AJNR Am J Neuroradiol [Internet].;35(5):1009–15. Available from: https://doi.org/10.3174/ajnr.A3784
Xu J, Lai M, Li S, Ye K, Li L, Hu Q et al (2022) Radiomics features based on MRI predict BRAF V600E mutation in pediatric low-grade gliomas: A non-invasive method for molecular diagnosis. Clin Neurol Neurosurg [Internet].;222(107478):107478. Available from: https://doi.org/10.1016/j.clineuro.2022.107478
Wang Y, Wang L, Qin B, Hu X, Xiao W, Tong Z et al (2023) Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters. Front Neurosci [Internet].;17:1157858. Available from: https://doi.org/10.3389/fnins.2023.1157858
Dong J, Li S, Li L, Liang S, Zhang B, Meng Y et al (2022) Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions. Br J Radiol [Internet].;95(1129):20201302. Available from: https://doi.org/10.1259/bjr.20201302
Iv M, Zhou M, Shpanskaya K, Perreault S, Wang Z, Tranvinh E et al (2019) MR imaging-based radiomic signatures of distinct molecular subgroups of medulloblastoma. AJNR Am J Neuroradiol [Internet].;40(1):154–61. Available from: https://doi.org/10.3174/ajnr.A5899
Soldatelli MD, Namdar K, Tabori U, Hawkins C, Yeom K, Khalvati F et al (2024) Identification of multiclass pediatric low-grade neuroepithelial tumor molecular subtype with ADC MR imaging and machine learning. AJNR Am J Neuroradiol [Internet].;45(6):753–60. Available from: https://doi.org/10.3174/ajnr.A8199
Kudus K, Wagner MW, Namdar K, Bennett J, Nobre L, Tabori U et al (2024) Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas. Sci Rep [Internet].;14(1):19102. Available from: https://doi.org/10.1038/s41598-024-69870-x
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