Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.
Objective: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrimination of AVM-related hematomas from those caused by other etiologies.Methods: Two hundred sixty-one cases with intraparenchymal hematomas underwent...
| Publicado en: | European Radiology Vol. 29; no. 4; pp. 2157 - 2166 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
|
| 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=135371942&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135371942 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2019 vid: 29 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135371942 135371942 NLM30306329 135371942 10.1007/s00330-018-5747-x NLM30306329 135371942 ppf: 2157 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. aug: au: Zhang, Yupeng Zhang, Baorui Liang, Fei Liang, Shikai Zhang, Yuxiang Yan, Peng Ma, Chao Liu, Aihua Guo, Feng Jiang, Chuhan affil: Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Room 603, No. 6 Tiantan Xili, Dongcheng District, Beijing, China sug: subj: Brain Diseases Diagnosis Tomography, X-Ray Computed Methods Image Enhancement Methods Hematoma Diagnosis Aged Child Aged, 80 and Over Middle Age Female Adolescence Reproducibility of Results Adult Young Adult Male Funding Source Aged: 65+ years Child: 6-12 years Aged, 80 & over Middle Aged: 45-64 years Adolescent: 13-18 years Adult: 19-44 years Female Male ab: Objective: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrimination of AVM-related hematomas from those caused by other etiologies.Methods: Two hundred sixty-one cases with intraparenchymal hematomas underwent baseline CT scan between 2012 and 2017 in our center. Cases were split into a training dataset (n = 180) and a test dataset (n = 81). Hematoma types were dichotomized into two classes, namely, AVM-related hematomas (AVM-H) and hematomas caused by other etiologies. A total of 576 radiomics features of 6 feature groups were extracted from NECT. We applied 11 feature selection methods to select informative features from each feature group. Selected radiomics features and the clinical feature age were then used to fit machine learning classifiers. In combination of the 11 feature selection methods and 8 classifiers, we constructed 88 predictive models. Predictive models were evaluated and the optimal one was selected and evaluated.Results: The selected radiomics model was RELF_Ada, which was trained with Adaboost classifier and features selected by Relief method. Cross-validated area under the curve (AUC) on training dataset was 0.988 and the relative standard deviation (RSD%) was 0.062. AUC on the test dataset was 0.957. Accuracy (ACC), sensitivity, specificity, positive prediction value (PPV), and negative predictive value (NPV) were 0.926, 0.889, 0.937, 0.800, and 0.967, respectively.Conclusions: Machine learning models with radiomics features extracted from NECT scan accurately discriminated AVM-related intraparenchymal hematomas from those caused by other etiologies. This technique provided a fast, non-invasive approach without use of contrast to diagnose this disease.Key Points: • Radiomics features from non-contrast-enhanced CT accurately discriminated AVM-related hematomas from those caused by other etiologies. • AVM-related hematomas tended to be larger in diameter, coarser in texture, and more heterogeneous in composition. • Adaboost classifier is an efficient approach for analyzing radiomics features. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|