Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature

Li, Yiming; Liu, Xing; Qian, Zenghui; Sun, Zimin; Xu, Kaibin; Wang, Kai; Fan, Xing; Zhong, Zhang; Li, Shaowu; Wang, Yinyan; Jiang, Tao

doi:10.1007/s00330-017-5267-0

Cited by 89 publications

(66 citation statements)

References 39 publications

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“…Higher values of information measure of correlation are associated significantly with WT ATRX status. This is in agreement with an ATRX mutation prediction study by Li et al 62 (Table 3) in patients with low-grade glioma, where the authors use MRI texture feature and LASSO regression model. In their model, the information measure of correlation is one of the features that is used to predict ATRX mutation.…”

Section: Discussionsupporting

confidence: 90%

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features

Shboul

Chen

Iftekharuddin

2020

Sci Rep

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Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out crossvalidation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83 ± 0.04, 0.84 ± 0.03, 0.80 ± 0.04, 0.70 ± 0.09, and 0.82 ± 0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification. Diffuse low-grade gliomas (LGG) are World Health Organization (WHO) Grade II and III gliomas. They are infiltrative in their nature and arising from glial cells (astrocytes or oligodendrocytes) of the central nervous system (CNS) 1,2. Recurrence and malicious progression are possible because of the difficulty in complete tumor resection 3. A group of these tumors may also develop into glioblastoma (GBM). An updated classification of diffuse LGG was included in the 2016 WHO Classification of Tumors of the CNS 4. The new classification of the diffuse LGG depends on the genetic driver mutations (IDH mutations, 1p/19q co-deletion, TERT mutations, and ATRX mutations). This new classification correlates well with patients' treatment and survival, for example, oligodendroglioma, defined by the 1p/19q co-deletion, are associated with longer survival compared to astrocytoma, which do not harbor the 1p/19q co-deletion 5. Molecular mutations are determined using invasive methods by obtaining usable tissue samples that have an increase in proliferation and neovascularization 6. Tissue sampling may also be associated with high cost, morbidity, and even mortality 7 , and depending on the sample, may undersample tumor components, especially in heterogeneous tumors. Consequently, developing alternative methods and non-invasively classify diffuse LGG into its different subtypes using imaging features and machine learning techniques have emerged as a promising body of research. In this work, we propose a non-invasive imaging-based...

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Section: Discussionsupporting

confidence: 90%

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features

Shboul

Chen

Iftekharuddin

2020

Sci Rep

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“…Despite the considerable data heterogeneity, they successfully predicted 1p/19q codeletion and discriminated LGGs on the basis of 1p/19q-codeletion status with an accuracy of 87% by extracting the top 39 texture features, mostly from CE-MR imaging and T2WI. Li et al [56][57][58] accurately predicted alpha thalassemia mentalretardation syndrome, epidermal growth factor receptor, and p53 status in patients with LGG on T2WI. In general, the secondorder TA on CE-MR imaging and FLAIR images mostly contributed to the high accuracy for predicting genomic status.…”

Section: Glioma Radiogenomicsmentioning

confidence: 99%

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

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Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging. ABBREVIATIONS:AUC ϭ area under the curve; CE ϭ contrast-enhanced; GLCM ϭ gray-level co-occurrence matrix; GLRLM ϭ gray-level run-length matrix; HGG ϭ high-grade glioma; IDH ϭ isocitrate dehydrogenase; IDM ϭ inverse difference moment; LGG ϭ low-grade glioma; MRTA ϭ MR imaging texture analysis; PCNSLϭ primary central nervous system lymphoma; PCA ϭ principal component analysis; SVM ϭ support vector machine; TA ϭ texture analysis Indicates open access to non-subscribers at www.ajnr.org Indicates article with supplemental on-line tables.

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“…We did not consider pixel rescaling because it was homogeneous in all patients. Third, we included only T2-weighted MRI images, because they are widely used in radiomic works (26,27). This study can be expanded using other sequences in future studies.…”

Section: Discussionmentioning

confidence: 99%

Reliability of 2D Magnetic Resonance Imaging Texture Analysis in Cerebral Gliomas: Influence of Slice Selection Bias on Reproducibility of Radiomic Features

Koçak¹

2019

Istanbul Med J

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Amaç: Bu çalışmadaki amacımız serebral gliomlardaki iki boyutlu (2D) manyetik rezonans görüntüleme (MRG) yapısal analizinde kesit seçiminin radyomik özelliklerin yeniden üretilebilirliğine etkisini ardışık kesitler kullanarak araştırmak. Yöntemler: Bu metodolojik çalışmaya halka açık bir veri bankasından düşük dereceli gliomu bulunan 25 hastanın T2 ağırlıklı MRG görüntüleri ve semi-otomatik segmentasyon verileri dahil edildi. Sadece iki ilgili bölge veya segmentasyon alanı kullanıldı: (i), en büyük kesitten elde edilen ve (ii), hemen komşuluğundaki kesitten elde edilen. Radyomik özellikler açık kaynak kodlu PyRadiomics yazılımı kullanılarak elde edildi. 6 farklı özellik sınıfından 3 farklı görüntü tipi kullanılarak toplamda 1116 yapısal özellik elde edildi. Güvenilirlik analizi %95 güven aralığı (GA) kullanılarak ve kullanılmadan sınıf içi katsayısı (SİK) ile yapıldı. Mükemmel yeniden üretilebilirlik için SİK eşik değeri 0,9 idi. Bulgular: %95 GA kullanılmadan yapılan güvenilirlik analizinde, yapısal özelliklerin %28'i mükemmel yeniden üretilebilirliğe sahipti. Bunun yanında, %95 GA kullanılarak yapılan güvenilirlik analizinde ise, yapısal özelliklerin sadece %10'u mükemmel yeniden üretilebilirliğe sahipti. Ardışık MRG kesitlerinde ne bir özellik sınıfı (aralık, %95 GA kullanılmadan %21,2-%34,4; kullanılarak, %2,1-%18,3) ne de bir görüntü tipi (aralık, %95 GA kullanılmadan, %22,3-%41,9; kullanılarak, %9,1-%14) önemli düzeyde yeniden üretilebilirliğe sahipti. Sonuç: Serebral gliomlardaki 2D T2 ağırlıklı MRG yapısal analizi kesit seçimine duyarlıdır. Bu durum dikkate alınmadığında radyomik çalışmalarda yeniden üretilebilirlik sorunlarına neden olabilir.

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Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature

Cited by 89 publications

References 39 publications

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Reliability of 2D Magnetic Resonance Imaging Texture Analysis in Cerebral Gliomas: Influence of Slice Selection Bias on Reproducibility of Radiomic Features

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