Classification of brain tumours using radiomic features on MRI

Çınarer, Gökalp

doi:10.18844/gjpaas.v0i12.4989

Cited by 3 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When genomic data are involved in radiomics, the latter is then termed as radiogenomics. Previous researches have evaluated the role of radiomics in predicting the tumor grade [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], the IDH mutation status [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], the 1p/19q codeletion status [ 30 , 31 , 32 , 33 ], or all three glioma labels [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion

Lawrance

Quillent

et al. 2022

Cancers

View full text Add to dashboard Cite

Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

show abstract

Section: Introductionmentioning

confidence: 99%

Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion

Lawrance

Quillent

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…The main contribution of this paper was to identify the brains automatically, which was evaluated through five matrices using a publicly available dataset. Similarly, the glioma disease was analyzed by [ 8 ], where they utilized the Gaussian Naïve Bayes technique. In their approach, they employed the grow cut method followed by 3D features on MRI images.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Machine Learning Approach for Brain MRI Classification

2022

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space’s dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

show abstract

Derin Öğrenme ile Artırılmış Görüntü Seti üzerinden Cilt Kanseri Tespiti

Ergun

Kiliç

2021

Black Sea Journal of Engineering and Science

View full text Add to dashboard Cite

Bu çalışmada, son yıllarda görüntü sınıflandırmada artan oranda ilgi gören derin öğrenme ve görüntü işleme yöntemleri kullanılarak kötü huylu (malignant) cilt lezyonlarının erken teşhisini kolaylaştırıcı yapay zekâ tabanlı sınıflandırma deneyleri gerçekleştirilmiştir. Melanom, en kötü huylu ve az görülen bir kanser türü olduğundan dolayı derin öğrenme mimarisini eğitmek için yeterli sayıda eğitim ve test görüntüsü bulmak zordur. Bu nedenle artırılmış veri seti oluşturulmuş ve 6 farklı derin öğrenme mimarisi ile eğitim yapılmıştır. Kötü huylu ve iyi huylu cilt lezyonlarını sınıflandırmak için popüler olan AlexNet, DenseNet-121, ResNet-18, ResNet-34, SqueezeNet ve VGGNet-16 mimarileri kullanılmıştır. Deneyler HAM10000 veri seti üzerinde artırma yapılarak gerçekleştirilmiştir. Deneyler sonucunda en başarılı sonuçları veren Resnet-34 mimarisi ile ortalama %87,5 doğruluk oranı, %94 AUC skoru, %84,5 F-skoru, %87,6 kesinlik değeri elde edilmiştir. Diğer derin öğrenme mimarilerinden elde edilen sonuçlar ve karşılaştırmalı analizler de çalışmada ayrıca sunulmuştur.

show abstract

Classification of brain tumours using radiomic features on MRI

Cited by 3 publications

References 29 publications

Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion

Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion

An Enhanced Machine Learning Approach for Brain MRI Classification

Derin Öğrenme ile Artırılmış Görüntü Seti üzerinden Cilt Kanseri Tespiti

Contact Info

Product

Resources

About