Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma

Nakagawa, Masataka; Nakaura, Takeshi; Namimoto, Tomohiro; Kitajima, Mika; Uetani, Hiroyuki; Tateishi, Machiko; Oda, Shigeto; Utsunomiya, Daisuke; Makino, Keishi; Nakamura, Hideo; Mukasa, Akitake; Hirai, Toshinori; Yamashita, Yasuyuki

doi:10.1016/j.ejrad.2018.09.017

Cited by 41 publications

(26 citation statements)

References 26 publications

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“…Given that MR scan is the conventional radiological examination for patients, TA on T1C has the potential to serve as a feasible solution in clinical application without requiring additional fees. Previous studies have illustrated that TA combined with machine learning could assist in the diagnosis of various brain tumors, such as GBM from primary central nerve system lymphoma and meningioma from GBM (16, 17). Moreover, it has also been applied in tumor grade system and gene mutation prediction (18–22).…”

Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Chen

Wang

et al. 2019

Front. Oncol.

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Purpose: To investigative the diagnostic performance of radiomics-based machine learning in differentiating glioblastomas (GBM) from metastatic brain tumors (MBTs). Method: The current study involved 134 patients diagnosed and treated in our institution between April 2014 and December 2018. Radiomics features were extracted from contrast-enhanced T1 weighted imaging (T1C). Thirty diagnostic models were built based on five selection methods and six classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen. Result : Two models represented promising diagnostic performance with AUC of 0.80. The first model was a combination of Distance Correlation as the selection method and Linear Discriminant Analysis (LDA) as the classification algorithm. In the training group, the sensitivity, specificity, accuracy, and AUC were 0.75, 0.85, 0.80, and 0.80, respectively; and in the testing group, the sensitivity, specificity, accuracy, and AUC of the model were 0.69, 0.86, 0.78, and 0.80, respectively. The second model was the Distance Correlation as the selection method and logistic regression (LR) as the classification algorithm, with sensitivity, specificity, accuracy, and AUC of 0.75, 0.85, 0.80, 0.80 in the training group and 0.69, 0.86, 0.78, 0.80 in the testing group. Conclusion: Radiomic-based machine learning has potential to be utilized in differentiating GBM from MBTs.

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Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Chen

Wang

et al. 2019

Front. Oncol.

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“…Moreover, with analyzable statistics converted from images, the novel computer technology could be employed. Similar researches suggested that radiomics combined with machinelearning algorithms displayed promising potential in various fields, including differential diagnosis of glioblastoma, presurgical grading of glioma, and prediction of patient survival outcomes (8,(26)(27)(28). It is worth noting that previous studies primarily focused the value of radiomics in distinguishing low-grade glioma vs. high-grade glioma, whereas the possible different characteristics among the histological subtypes of glioma were not taken into consideration (29)(30)(31).…”

Section: Discussionmentioning

confidence: 95%

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

et al. 2019

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Objectives: To investigate the ability of radiomics features from MRI in differentiating anaplastic oligodendroglioma (AO) from atypical low-grade oligodendroglioma using machine-learning algorithms. Methods: A total number of 101 qualified patients (50 participants with AO and 51 with atypical low-grade oligodendroglioma) were enrolled in this retrospective, single-center study. Forty radiomics features of tumor images derived from six matrices were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images. Three selection methods were performed to select the optimal features for classifiers, including distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Then three machine-learning classifiers were adopted to generate discriminative models, including linear discriminant analysis, support vector machine, and random forest (RF). Receiver operating characteristic analysis was conducted to evaluate the discriminative performance of each model. Results: Nine predictive models were established based on radiomics features from T1C images and FLAIR images. All of the classifiers represented feasible ability in differentiation, with AUC more than 0.840 when combined with suitable selection method. For models based on T1C images, the combination of LASSO and RF classifier represented the highest AUC of 0.904 in the validation group. For models based on FLAIR images, the combination of GBDT and RF classifier showed the highest AUC of 0.861 in the validation group. Conclusion: Radiomics-based machine-learning approach could potentially serve as a feasible method in distinguishing AO from atypical low-grade oligodendroglioma.

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“…To obtain reliable values in a quantitative method, the parameter values obtained must be resistant to various factors such as segmentation variability, acquisition differences or use of different scanners from different vendors. Although much work has been done using 2D MRI texture analysis in cerebral gliomas (10)(11)(12)(13), there is a scarcity of papers regarding the reliability of the technique. Only few papers draw our attention to the in vivo stability of the texture feature parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Although three-dimensional segmentation is the most representative for tumor texture, several studies have been published using a single image slice in the texture analysis of gliomas (10)(11)(12)(13). However, this technique is prone to slice selection bias.…”

Section: Introductionmentioning

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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Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma

Cited by 41 publications

References 26 publications

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

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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