Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma

Yang, Fan; Chen, Chaoyue; Zhao, Fang; Tian, Zerong; Wang, Jian; Ma, Xuelei; Xu, Jianguo

doi:10.3389/fonc.2019.01164

Cited by 32 publications

(33 citation statements)

References 40 publications

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“…During the process of radiomics model building, we investigated different techniques, including supervised machine learning and LASSO regression analysis, to explore characteristics and identify the optimal features for model construction. LASSO turned out to several advantages as it reduces redundancy, dependency, and dimensionality of the features and thus enhances model accuracy 19 . In addition, LASSO enables the generation of interpretable models using variable selection and regularization as well as integration of selected features into a radiomics signature 19 .…”

Section: Discussionmentioning

confidence: 99%

“…LASSO turned out to several advantages as it reduces redundancy, dependency, and dimensionality of the features and thus enhances model accuracy 19 . In addition, LASSO enables the generation of interpretable models using variable selection and regularization as well as integration of selected features into a radiomics signature 19 . As for classification algorithms, we used both binary and multinomial classification.…”

Section: Discussionmentioning

confidence: 99%

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A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI

Elkilany

Fehrenbach

Auer

et al. 2021

Sci Rep

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The implementation of radiomics in radiology is gaining interest due to its wide range of applications. To develop a radiomics-based model for classifying the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI, 248 patients with a known etiology of liver cirrhosis who underwent 306 gadoxetic acid-enhanced MRI examinations were included in the analysis. MRI examinations were classified into 6 groups according to the etiology of liver cirrhosis: alcoholic cirrhosis, viral hepatitis, cholestatic liver disease, nonalcoholic steatohepatitis (NASH), autoimmune hepatitis, and other. MRI examinations were randomized into training and testing subsets. Radiomics features were extracted from regions of interest segmented in the hepatobiliary phase images. The fivefold cross-validated models (2-dimensional—(2D) and 3-dimensional—(3D) based) differentiating cholestatic cirrhosis from noncholestatic etiologies had the best accuracy (87.5%, 85.6%), sensitivity (97.6%, 95.6%), predictive value (0.883, 0.877), and area under curve (AUC) (0.960, 0.910). The AUC was larger in the 2D-model for viral hepatitis, cholestatic cirrhosis, and NASH-associated cirrhosis (P-value of 0.05, 0.05, 0.87, respectively). In alcoholic cirrhosis, the AUC for the 3D model was larger (P = 0.01). The overall intra-class correlation coefficient (ICC) estimates and their 95% confident intervals (CI) for all features combined was 0.68 (CI 0.56–0.87) for 2D and 0.71 (CI 0.61–0.93) for 3D measurements suggesting moderate reliability. Radiomics-based analysis of hepatobiliary phase images of gadoxetic acid-enhanced MRI may be a promising noninvasive method for identifying the etiology of liver cirrhosis with better performance of the 2D- compared with the 3D-generated models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI

Elkilany

Fehrenbach

Auer

et al. 2021

Sci Rep

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“…In addition to finding the best diagnostic model, we also found that some of the models performed poorly. A previous study used LDA and SVM classifier machine learning methods to identify glioblastoma (GBM) and anaplastic oligodendrocytoma (AO), and found that the AUC of testing set was all above 0.90 ( 47 ). However, in our study, these two classification algorithms do not show good diagnostic performance.…”

Section: Discussionmentioning

confidence: 99%

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Zhang

Liu

et al. 2021

Front. Oncol.

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PurposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.Materials and MethodsA retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.ResultOur analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.ConclusionIn conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

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“…In neuroradiology, machine learning models have started to be used in grading glial tumors. Especially in the preoperative period, to predict molecular subtype and survival [15][16][17][18] . Our study developed a machine learning model for predicting survival based on texture analysis data obtained from contrasted T1-weighted images obtained in the preoperative period.…”

Section: Discussionmentioning

confidence: 99%

The success of machine learning algorithms developed with radiomic features obtained from preoperative contrast-enhanced MRI in the prediction of short-term survival in patients with glioblastoma

Özdaş

Demirel

Bılgın

et al. 2021

Cukurova Medical Journal

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This study aimed to evaluate the predictability of survival in patients with glioblastoma using a machine learning (ML) model developed with tissue analysis features obtained through preoperative post-contrast T1weighted images(T1WI). Materials and Methods: The radiomic features of tumors were obtained from postcontrast T1WI of 60 glioblastoma patients. Radiomic properties, density, shape, and textural properties obtained from six matrices were included in the analysis. The patients' three-and six-month survival rates were recorded. Five different ML algorithms were applied to create predictive models [random forest, neural network, linear discriminant analysis(LDA), stochastic gradient descent (SGD), and support vector machine(SMV)]. Results: The mean survival time of the patients was 295.4 days, and the median value was 211.5 (17-1357) days. Among the models developed for three-and six-month survival prediction, the highest success was obtained from the LDA algorithm, in which the AUC values were calculated as 0.88 and 0.78, respectively. Conclusion: Using ML techniques, the success of predicting imaging-based patient survival was very high. With the development and widespread adoption of these techniques, ML models will be useful in deciding on treatment according to survival prediction in glioblastoma. Amaç: Bu çalışma ameliyat öncesi kontrastlı T1 ağırlıklı görüntülerden(T1AG) elde edilen doku analizi(radyomiks) özellikleriyle geliştirilen makine öğrenimi(MÖ) modeli kullanılarak glioblastomlu hastalarda sağkalımın öngörülebilirliğini değerlendirmeyi amaçlamaktadır. Gereç ve Yöntem: Tümörlerin radyomiks özellikleri 60 glioblastoma hastasının kontrastlı T1AG'den elde edildi. Altı matristen elde edilen radyomik özellikler, yoğunluk, şekil ve dokusal özellikler analize dahil edilmiştir. Hastaların üç ve altı aylık sağkalım oranları kaydedildi. Tahmine dayalı modeller [random forest, neural network, linear discriminant analysis(LDA), stochastic gradient descent (SGD), support vector machine(SMV)] oluşturmak için beş farklı MÖ algoritması uygulandı. Bulgular: Hastaların ortalama sağkalım süresi 295,4 gün, medyan değeri 211,5 (17-1357) gündü. Üç ve altı aylık sağkalım tahmini için geliştirilen modellerden en yüksek başarı, EAA değerlerinin sırasıyla 0,88 ve 0,78 olarak hesaplandığı LDA algoritmasından elde edilmiştir. Sonuç: MÖ tekniklerini kullanarak, görüntülemeye dayalı hasta sağkalımını tahmin etme başarısı çok yüksekti. Bu tekniklerin gelişmesi ve yaygınlaşması ile MÖ modelleri, glioblastomda sağkalım tahminine göre tedaviye karar vermede faydalı olacaktır.

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Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma

Cited by 32 publications

References 40 publications

A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI

A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

The success of machine learning algorithms developed with radiomic features obtained from preoperative contrast-enhanced MRI in the prediction of short-term survival in patients with glioblastoma

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