Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Purpose: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme usin...
| Publicado en: | Neuroradiology Vol. 61; no. 7; pp. 757 - 766 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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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=136828393&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136828393 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2019 vid: 61 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136828393 136828393 136828393 10.1007/s00234-019-02195-z 136828393 ppf: 757 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. aug: au: Shrot, Shai Salhov, Moshe Dvorski, Nir Konen, Eli Averbuch, Amir Hoffmann, Chen affil: Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel, 2 Sheba Rd, 52621, Ramat Gan, Israel sug: subj: Perfusion Imaging Magnetic Resonance Imaging Machine Learning Brain Neoplasms Classification Human Glioma Brain Neoplasms Diagnosis Artificial Intelligence Meningioma Neoplasm Metastasis Algorithms Decision Support Systems, Clinical ab: Purpose: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. Methods: The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. Results: A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. Conclusion: A machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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