Malbari F. Pediatric neuro-oncology. Neurol Clin. 2021;39:829–45.

Article  PubMed  Google Scholar 

Wen PY, Packer RJ. The 2021 WHO classification of tumors of the central nervous system: clinical implications. Neuro Oncol. 2021;23:1215–7.

Article  PubMed  PubMed Central  Google Scholar 

Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23:1231–51.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Partap S, Monje M. Pediatric brain tumors. Continuum (Minneap Minn). 2020;26:1553–83.

PubMed  Google Scholar 

Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, et al. Introduction to radiomics. J Nucl Med. 2020;61:488–95.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Albalkhi I, Bhatia A, Lösch N, Goetti R, Mankad K. Current state of radiomics in pediatric neuro-oncology practice: a systematic review. Pediatr Radiol. 2023;53:2079–91.

Article  PubMed  Google Scholar 

Jaju A, Yeom KW, Ryan ME. MR Imaging of pediatric brain tumors. Diagnostics (Basel). 2022;12:961.

Article  PubMed  Google Scholar 

Chilaca Rosas MF, Contreras Aguilar MT, Garcia Lezama M, Salazar Calderon DR, Vargas Del Angel RG, Moreno Jimenez S, et al. Identification of radiomic signatures in brain MRI sequences T1 and T2 that differentiate tumor regions of midline gliomas with H3.3K27M mutation. Diagnostics (Basel). 2023;13:2669.

Article  PubMed  Google Scholar 

Cooney TM, Cohen KJ, Guimaraes CV, Dhall G, Leach J, Massimino M, et al. Response assessment in diffuse intrinsic pontine glioma: recommendations from the response assessment in pediatric neuro-oncology (RAPNO) working group. Lancet Oncol. 2020;21:e330–6.

Article  PubMed  Google Scholar 

Erker C, Tamrazi B, Poussaint TY, Mueller S, Mata-Mbemba D, Franceschi E, et al. Response assessment in paediatric high-grade glioma: recommendations from the response assessment in pediatric neuro-oncology (RAPNO) working group. Lancet Oncol. 2020;21:e317–29.

Article  CAS  PubMed  Google Scholar 

Fangusaro J, Witt O, HernáizDriever P, Bag AK, de Blank P, Kadom N, et al. Response assessment in paediatric low-grade glioma: recommendations from the response assessment in pediatric neuro-oncology (RAPNO) working group. Lancet Oncol. 2020;21:e305–16.

Article  PubMed  Google Scholar 

Warren KE, Vezina G, Poussaint TY, Warmuth-Metz M, Chamberlain MC, Packer RJ, et al. Response assessment in medulloblastoma and leptomeningeal seeding tumors: recommendations from the response assessment in pediatric neuro-oncology committee. Neuro Oncol. 2018;20:13–23.

Article  CAS  PubMed  Google Scholar 

Baehring JM, Fulbright RK. Diffusion-weighted MRI in neuro-oncology. CNS. Oncol. 2012;1:155–67.

CAS  Google Scholar 

Aboian MS, Kline CN, Li Y, Solomon DA, Felton E, Banerjee A, et al. Early detection of recurrent medulloblastoma: the critical role of diffusion-weighted imaging. Neurooncol Pract. 2018;5:234–40.

PubMed  PubMed Central  Google Scholar 

Tong KA, Ashwal S, Obenaus A, Nickerson JP, Kido D, Haacke EM. Susceptibility-weighted MR imaging: a review of clinical applications in children. AJNR Am J Neuroradiol. 2008;29:9–17.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lequin M, Hendrikse J. Advanced MR imaging in pediatric brain tumors, clinical applications. Neuroimaging Clin N Am. 2017;27:167–90.

Article  PubMed  Google Scholar 

Panigrahy A, Blüml S. Neuroimaging of pediatric brain tumors: from basic to advanced magnetic resonance imaging (MRI). J Child Neurol. 2009;24:1343–65.

Article  PubMed  Google Scholar 

Zhao B. Understanding sources of variation to improve the reproducibility of radiomics. Front Oncol. 2021;11:633176.

Article  PubMed  PubMed Central  Google Scholar 

Kalpathy-Cramer J, Zhao B, Goldgof D, Gu Y, Wang X, Yang H, et al. A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study. J Digit Imaging. 2016;29:476–87.

Article  PubMed  PubMed Central  Google Scholar 

Peng J, Kim DD, Patel JB, Zeng X, Huang J, Chang K, et al. Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors. Neuro Oncol. 2022;24:289–99.

Article  PubMed  Google Scholar 

Zhou H, Hu R, Tang O, Hu C, Tang L, Chang K, et al. Automatic machine learning to differentiate pediatric posterior fossa tumors on routine MR imaging. AJNR Am J Neuroradiol. 2020;41:1279–85.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang M, Tam L, Wright J, Mohammadzadeh M, Han M, Chen E, et al. Radiomics can distinguish pediatric supratentorial embryonal tumors, high-grade gliomas, and ependymomas. AJNR Am J Neuroradiol. 2022;43:603–10.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li M, Wang H, Shang Z, Yang Z, Zhang Y, Wan H. Ependymoma and pilocytic astrocytoma: differentiation using radiomics approach based on machine learning. J Clin Neurosci. 2020;78:175–80.

Article  PubMed  Google Scholar 

Dong J, Li L, Liang S, Zhao S, Zhang B, Meng Y, et al. Differentiation between ependymoma and medulloblastoma in children with radiomics approach. Acad Radiol. 2021;28:318–27.

Article  PubMed  Google Scholar 

Novak J, Zarinabad N, Rose H, Arvanitis T, MacPherson L, Pinkey B, et al. Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Sci Rep. 2021;11:2987.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Grist JT, Withey S, MacPherson L, Oates A, Powell S, Novak J, et al. Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study. Neuroimage Clin. 2020;25:102172.

Article  PubMed  PubMed Central  Google Scholar 

Chilaca-Rosas MF, Contreras-Aguilar MT, Salazar-Calderón DR, García-Lezama M, Roldán-Valadez E. Characterization of central nervous system gliomas in adult patients using radiomics as an emerging technology for precision medicine. Gac Med Mex. 2023;159:432–5.

PubMed  Google Scholar 

Iv M, Zhou M, Shpanskaya K, Perreault S, Wang Z, Tranvinh E, et al. MR imaging-based radiomic signatures of distinct molecular subgroups of medulloblastoma. AJNR Am J Neuroradiol. 2019;40:154–61.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Haldar D, Kazerooni AF, Arif S, Familiar A, Madhogarhia R, Khalili N, et al. Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers. Neoplasia. 2023;36:100869.

Article  CAS  PubMed  Google Scholar 

Gemini L, Tortora M, Giordano P, Prudente ME, Villa A, Vargas O, et al. Vasari scoring system in discerning between different degrees of glioma and IDH status prediction: a possible machine learning application? J Imaging. 2023;9:75.

Article  PubMed  PubMed Central  Google Scholar 

García-Lezama M, Carrillo-Ruiz JD, Moreno-Jiménez S, Roldán-Valadez E. WHO CNS5 2021 includes specific mutations in gliomas that can be identified with MRI quantitative biomarkers. Gac Med Mex. 2023;159:161–8.

PubMed  Google Scholar 

Park JE, Kickingereder P, Kim HS. Radiomics and deep learning from research to clinical workflow: neuro-oncologic imaging. Korean J Radiol. 2020;21:1126–37.

Article  PubMed  PubMed Central