Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
Purpose: To determine the feasibility of using high dimensional computer-extracted features, known as radiomics features, in differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma on multi-parametric MR imaging including diffusion-weighted imaging.Methods: Retrospective ev...
| Publicado en: | Neuroradiology Vol. 60; no. 12; pp. 1297 - 1306 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2018
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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=133106535&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133106535 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2018 vid: 60 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133106535 133106535 133106535 10.1007/s00234-018-2091-4 133106535 ppf: 1297 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. aug: au: Kim, Yikyung Cho, Hwan-ho Kim, Sung Tae Park, Hyunjin Nam, Dohyun Kong, Doo-Sik affil: Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea sug: subj: Glioma Diagnosis Central Nervous System Neoplasms Diagnosis Lymphoma Diagnosis Magnetic Resonance Imaging Methods Human Retrospective Design Contrast Media Diagnostic Use Logistic Regression Sensitivity and Specificity ab: Purpose: To determine the feasibility of using high dimensional computer-extracted features, known as radiomics features, in differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma on multi-parametric MR imaging including diffusion-weighted imaging.Methods: Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 143 patients (two independent cohorts for discovery [n = 86; glioblastoma = 49, PCNSL = 37] and validation [n = 57; glioblastoma = 29, PCNSL = 28]) with newly diagnosed glioblastoma and PCNSL were subjected to radiomics analysis using the multi-parametric MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging). Radiomics analyses were performed for two types of regions of interest (ROI) covering contrast-enhancing tumor and whole (enhancing or non-enhancing) tumor plus peritumoral edema. A total of 127 radiomics features were calculated. Feature selection was performed to identify the most discriminating features for every MR image in the discovery cohort. The identified features were used to calculate radiomics scores, which were later used in logistic regression to distinguish between PCNSL and glioblastoma. The classification model was further tested on the independent validation cohort.Results: Fifteen features were selected as significant features in the discovery cohort. Using the identified features and calculated radiomics scores, the logistic regression-based classifier yielded an area under the curve (AUC) of 0.979, sensitivity of 0.938, and specificity of 0.944 in the discovery cohort to distinguish between glioblastoma and PCNSL. A similarly high rate of performance was observed in the validation cohort (AUC = 0.956).Conclusions: Radiomics features derived from multi-parametric MRI can be used to differentiate PCNSL from glioblastoma effectively. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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