Machine learning and glioma imaging biomarkers.
Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant sea...
| Publicado en: | Clinical Radiology Vol. 75; no. 1; pp. 20 - 33 |
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| Autores principales: | , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145435773&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145435773 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099260 23Q jtl: Clinical Radiology issn: 00099260 maglogo: N pubinfo: dt: Jan2020 vid: 75 iid: 1 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 145435773 145435773 NLM31371027 145435773 10.1016/j.crad.2019.07.001 NLM31371027 145435773 ppf: 20 ppct: 13 formats: tig: atl: Machine learning and glioma imaging biomarkers. aug: au: Booth, T.C. Williams, M. Luis, A. Cardoso, J. Ashkan, K. Shuaib, H. affil: School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK sug: subj: Brain Neoplasms Glioma Magnetic Resonance Imaging Methods Image Interpretation, Computer Assisted Brain Neoplasms Pathology Neoplasm Grading Glioma Therapy Brain Neoplasms Therapy Glioma Pathology ab: Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction.Results: Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).Conclusion: Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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