A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Emblem, Kyrre E.; Pinho, Marco da Cunha; Zöllner, Frank G.; Due‐Tønnessen, Paulina; Hald, John; Schad, Lothar R.; Meling, Torstein R.; Rapalino, Otto; Bjørnerud, Atle

doi:10.1148/radiol.14140770

Cited by 97 publications

(70 citation statements)

References 24 publications

(52 reference statements)

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“…There are several studies that go beyond the utilization of hybrid imaging and incorporate additional non-imaging data for increased predictive accuracy [180][181][182]. This kind of approach successfully increased risk assessment of head-and-neck cancer built on in vivo and clinical variables with utilizing random forest ML approaches [29].…”

Section: Holomicsmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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Section: Holomicsmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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“…16 In recent years, machine-learning algorithms have been applied to imaging studies of gliomas to predict genotype and patient survival outcomes based on imaging features extracted from conventional MRI. [17][18][19][20] In this study, we retrospectively examined the preoperative MRI of 120 patients diagnosed with either primary grade III or IV glioma with known IDH genotype. We hypothesized that a model integrating multimodal MRI features using a machine-learning approach could accurately predict IDH genotype in HGGs.…”

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confidence: 99%

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

et al. 2016

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Background. High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI. Methods. Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype. Results. Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features. Conclusion. Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.

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“…High‐grade gliomas (HGGs) demonstrate higher heterogeneity than low‐grade gliomas (LGGs), making the image texture attributes of advanced MRI combined with support vector machine (SVM) model a promising strategy to improve glioma grading efficacy based on its power to quantify the heterogeneous distribution of the gray‐level within the region of interest (ROI) using varied analysis models . Compared with previous receiver operating characteristics analysis, SVM is able to automatically learn the discrimination patterns from the existing data and establish the corresponding model to predict the individual glioma grade . A limited number of parametric image texture attributes from single modal MRI have been previously applied, including ASL or multi‐b‐value DWI (MB‐DWI) attributes, respectively.…”

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confidence: 99%

“…13 Compared with previous receiver operating characteristics analysis, 14 SVM is able to automatically learn the discrimination patterns from the existing data and establish the corresponding model to predict the individual glioma grade. 15 A limited number of parametric image texture attributes from single modal MRI have been previously applied, including ASL 16 or multi-b-value DWI (MB-DWI) attributes, 9,16 respectively. However, the fundamental question of which attribute-retrieving model is most suitable to achieve satisfying classifying accuracy has not yet been answered, especially when a large number of parametric image texture attributes from multimodal MRI are comprehensively considered.…”

mentioning

confidence: 99%

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Yang

Yan

Zhang

et al. 2019

Magnetic Resonance Imaging

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Background Accurate glioma grading plays an important role in patient treatment. Purpose To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM). Study Type Retrospective. Population In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007. Field Strength/Sequence 3.0T MRI/ T1WI, T2 fluid‐attenuated inversion recovery, contrast enhanced T1, arterial spinal labeling, diffusion‐weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2), and dynamic contrast‐enhanced. Assessment Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray‐level co‐occurrence matrix (GLCM), gray‐level run‐length matrix (GLRLM), and gray‐level size‐zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF‐SVM) combined with attribute selection using SVM‐recursive feature elimination (SVM‐RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10‐fold crossvalidation. The model performance was further tested using the remaining 20% data. Statistical Tests Ten‐fold crossvalidation was used to validate the model performance. Results Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM‐RFE was able to reduce attribute redundancy as well as improve RBF‐SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray‐level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM‐RFE to provide comparable classifying efficacy. Data Conclusion When using image textures‐based SVM classification of gliomas, the GLSZM model in combination with gray‐level 64 and attribute selection may be an optimized solution. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263–1274.

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A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Cited by 97 publications

References 24 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

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