BROWSE

Related Scientist

Researcher

뇌과학이미징연구단
뇌과학 이미징 연구단
more info

Classification of the glioma grading using radiomics analysis

Cited 0 time in webofscience Cited 0 time in scopus
14 Viewed 1 Downloaded
Title
Classification of the glioma grading using radiomics analysis
Author(s)
Hwan-ho Cho; Seung-hak Lee; Jonghoon Kim; Hyunjin Park
Publication Date
2018-11
Journal
PEERJ, v.6, no., pp.e5982 -
Publisher
PEERJ INC
Abstract
Background. Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. Methods. We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort. Results. Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts. Discussion. Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas. Copyright 2018 Cho et al.
URI
https://pr.ibs.re.kr/handle/8788114/5326
ISSN
2167-8359
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
Files in This Item:
59_박현진 Classification_of_the_glioma_grading_using_radiomi.pdfDownload

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse