Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61(4):488–495. https://doi.org/10.2967/jnumed.118.222893

Article  CAS  PubMed  PubMed Central  Google Scholar 

McCague C, Ramlee S, Reinius M et al (2023) Introduction to radiomics for a clinical audience. Clin Radiol 78(2):83–98. https://doi.org/10.1016/j.crad.2022.08.149

Article  CAS  PubMed  Google Scholar 

Moawad AW, Fuentes DT, ElBanan MG et al (2022) Artificial Intelligence in Diagnostic Radiology: where do we stand, challenges, and opportunities. J Comput Assist Tomogr 46(1):78–90. https://doi.org/10.1097/rct.0000000000001247

Article  PubMed  Google Scholar 

Rogers W, Thulasi Seetha S, Refaee TAG et al (2020) Radiomics: from qualitative to quantitative imaging. Br J Radiol 93(1108):20190948. https://doi.org/10.1259/bjr.20190948

Article  PubMed  PubMed Central  Google Scholar 

Decoux A, Duron L, Habert P et al (2023) Comparative performances of machine learning algorithms in radiomics and impacting factors. Sci Rep 13(1):14069. https://doi.org/10.1038/s41598-023-39738-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37(7):2113–2131. https://doi.org/10.1148/rg.2017170077

Article  PubMed  Google Scholar 

Mohammadzadeh I, Hajikarimloo B, Eini P et al (2025) Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis. Neuroradiology 67(7):1733–1749. https://doi.org/10.1007/s00234-025-03657-3

Article  PubMed  Google Scholar 

Mohammadzadeh I, Hajikarimloo B, Niroomand B et al (2025) Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants. Neurosurg Rev 48(1):240. https://doi.org/10.1007/s10143-025-03419-y

Article  PubMed  Google Scholar 

Lim JS, Au Yong TPT, Boon CS, Boon IS (2024) Radiomics in oncology and radiology: statistical significance versus clinical significance. Clin Oncol (R Coll Radiol) 36(9):e342. https://doi.org/10.1016/j.clon.2024.05.003

Article  CAS  PubMed  Google Scholar 

Li S, Zhou B (2022) A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat Oncol 17(1):217. https://doi.org/10.1186/s13014-022-02192-2

Article  PubMed  PubMed Central  Google Scholar 

Abdel Razek AAK, Alksas A, Shehata M et al (2021) Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 12(1):152. https://doi.org/10.1186/s13244-021-01102-6

Article  PubMed  PubMed Central  Google Scholar 

Liu J, Shan Y, Gao G (2022) The application value of CT radiomics features in predicting pressure amplitude correlation index in patients with severe traumatic brain injury. Front Neurol 13:905655. https://doi.org/10.3389/fneur.2022.905655

Article  PubMed  PubMed Central  Google Scholar 

Madhogarhia R, Haldar D, Bagheri S et al (2022) Radiomics and radiogenomics in pediatric neuro-oncology: A review. Neurooncol Adv Jan-Dec 4(1):vdac083. https://doi.org/10.1093/noajnl/vdac083

Article  Google Scholar 

Taha B, Boley D, Sun J, Chen C (2021) Potential and limitations of radiomics in neuro-oncology. J Clin Neurosci Aug 90:206–211. https://doi.org/10.1016/j.jocn.2021.05.015

Article  Google Scholar 

McKee AC, Daneshvar DH (2015) The neuropathology of traumatic brain injury. Handb Clin Neurol 127:45–66. https://doi.org/10.1016/b978-0-444-52892-6.00004-0

Article  PubMed  PubMed Central  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to Decode the radiographic phenotype. Cancer Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lohmann P, Galldiks N, Kocher M et al (2021) Radiomics in neuro-oncology: Basics, workflow, and applications. Methods Apr 188:112–121. https://doi.org/10.1016/j.ymeth.2020.06.003

Article  CAS  Google Scholar 

He K, Zhang X, Ren S, Sun J Deep Residual Learning for Image Recognition. (2016) IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015:770–778

Chollet F, Xception Deep Learning with Depthwise Separable Convolutions. 2017:1800–1807

Koçak B, Durmaz E, Ateş E, Kılıçkesmez Ö (2019) Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol Nov 25(6):485–495. https://doi.org/10.5152/dir.2019.19321

Article  Google Scholar 

Grossen AA, Evans AR, Ernst GL, Behnen CC, Zhao X, Bauer AM (2024) The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment. Front Neurol 15:1398876. https://doi.org/10.3389/fneur.2024.1398876

Article  PubMed  PubMed Central  Google Scholar 

Maegele M (2021) Coagulopathy and progression of intracranial hemorrhage in traumatic brain injury: Mechanisms, Impact, and therapeutic considerations. Neurosurgery Nov 18(6):954–966. https://doi.org/10.1093/neuros/nyab358

Article  Google Scholar 

Shih YJ, Liu YL, Chen JH et al (2022) Prediction of intraparenchymal hemorrhage progression and neurologic outcome in traumatic brain injury patients using radiomics score and clinical parameters. Diagnostics. https://doi.org/10.3390/diagnostics12071677

Article  PubMed  PubMed Central  Google Scholar 

Lin F, Wang K, Lai M et al (2025) Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning. Sci Rep 15(1):5805. https://doi.org/10.1038/s41598-024-72539-0

Article  PubMed  PubMed Central  Google Scholar 

Wei X, Tang X, You D, Ding E, Pan C (2023) A clinical-radiomics based nomogram to predict progressive intraparenchymal hemorrhage in mild to moderate traumatic injury patients. Eur J Radiol 163:110785. https://doi.org/10.1016/j.ejrad.2023.110785

Article  PubMed  Google Scholar 

Zhang L, Zhuang Q, Wu G et al (2022) Combined radiomics model for prediction of hematoma progression and clinical outcome of cerebral contusions in traumatic brain injury. Neurocrit Care 36(2):441–451. https://doi.org/10.1007/s12028-021-01320-2

Article  CAS  PubMed  Google Scholar 

He H, Liu J, Li C et al (2024) Predicting hematoma expansion and prognosis in cerebral contusions: a radiomics-clinical approach. J Neurotrauma 41:1337–1352. https://doi.org/10.1089/neu.2023.0410

Article  PubMed  Google Scholar 

Fu Z, Peng L, Guo L et al (2025) Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy. Front Med Technol 7:1485244. https://doi.org/10.3389/fmedt.2025.1485244

Article  PubMed  PubMed Central  Google Scholar 

Li Y, Zhang G, Shan Y et al (2023) Non-invasive assessment of intracranial hypertension in patients with traumatic brain injury using computed tomography radiomic features: a pilot study. J Neurotrauma 40:250–259. https://doi.org/10.1089/neu.2022.0277

Article  PubMed  Google Scholar 

Shan Y, Xue Y, Zhu J et al (2025) Development and validation of intracranial hypertension prediction models based on radiomic features in patients with traumatic brain injury: an exploratory study based on CENTER-TBI data. Crit Care 29(1):100. https://doi.org/10.1186/s13054-025-05328-4

Article  PubMed  PubMed Central  Google Scholar 

Qin J, Shen R, Fu J, Sun J (2025) Research on machine learning models based on cranial CT scan for assessing prognosis of emergency brain injury. World Neurosurg 124100. https://doi.org/10.1016/j.wneu.2025.124100

Article  PubMed  Google Scholar 

Brossard C, Grèze J, de Busschère JA et al (2023) Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans. Sci Rep 13(1):20155. https://doi.org/10.1038/s41598-023-46945-9