scholarly journals Radiogenomics of Glioblastoma: Machine Learning–based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features

Radiology ◽  
2016 ◽  
Vol 281 (3) ◽  
pp. 907-918 ◽  
Author(s):  
Philipp Kickingereder ◽  
David Bonekamp ◽  
Martha Nowosielski ◽  
Annekathrin Kratz ◽  
Martin Sill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mr Imaging ◽  
Molecular Characteristics ◽  
Imaging Features

Liver Adenomatosis: Classification of MR Imaging Features and Comparison with Pathologic Findings

Radiology ◽  
2006 ◽  
Vol 241 (2) ◽  
pp. 433-440 ◽  
Author(s):  
Maïté Lewin ◽  
Adriana Handra-Luca ◽  
Lionel Arrivé ◽  
Dominique Wendum ◽  
Valérie Paradis ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Imaging Features ◽  
Pathologic Findings

scholarly journals Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2606
Author(s):  
Evi J. van Kempen ◽  
Max Post ◽  
Manoj Mannil ◽  
Benno Kusters ◽  
Mark ter Laan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Molecular Characteristics ◽  
Aggregated Data ◽  
Molecular Features

Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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