Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance.
Purpose: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify...
| Publicado en: | Neuroradiology Vol. 63; no. 12; pp. 2047 - 2057 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2021
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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=153556612&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153556612 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2021 vid: 63 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153556612 150624193 153556612 153556612 10.1007/s00234-021-02719-6 153556612 ppf: 2047 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance. aug: au: Siakallis, Loizos Sudre, Carole H. Mulholland, Paul Fersht, Naomi Rees, Jeremy Topff, Laurens Thust, Steffi Jager, Rolf Cardoso, M. Jorge Panovska-Griffiths, Jasmina Bisdas, Sotirios affil: Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, WC1N 3BG, London, UK sug: subj: Magnetic Resonance Imaging Methods Machine Learning Glioma Classification Disease Surveillance Sensitivity and Specificity Human Support Vector Machine Retrospective Design Neuroradiography Professional Role Radiologists Descriptive Statistics Image Processing, Computer Assisted ab: Purpose: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem). Methods: Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists' classifications. Results: SVM classification based on combined perfusion and structural features outperformed radiologists' classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists' classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists). Conclusion: Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001). pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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