Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

Zhang, Xin; Yan, Lin‐Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhicheng; Tian, Qiang; Han, Ziyang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guangbin

doi:10.18632/oncotarget.18001

Cited by 117 publications

(97 citation statements)

References 32 publications

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“…It is reasonable that the difference of angiogenesis and blood perfusion status between Grade III and IV gliomas was relatively less influential than that between LGGs and HGGs. In addition, the performance of classifying Grade III from IV gliomas was better than that of classifying LGGs from HGGs, which is consistent with the results of a previous study …”

Section: Discussionsupporting

confidence: 91%

Radiomics strategy for glioma grading using texture features from multiparametric MRI

Tian

林燕

Zhang

et al. 2018

Magnetic Resonance Imaging

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Section: Discussionsupporting

confidence: 91%

Radiomics strategy for glioma grading using texture features from multiparametric MRI

Tian

林燕

Zhang

et al. 2018

Magnetic Resonance Imaging

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177

134

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“…A sample augmentation method termed Synthetic Minority Oversampling Technique (SMOTE) was usually applied to solve this issue. Its merit has been confirmed in a previous study . In this study, the patient number in training data of grade II and grade III were augmented to that of grade IV, ie, 50.…”

Section: Methodsmentioning

confidence: 52%

“…The study data of the current project were derived from a diagnostic trial that has been registered to ClinicalTrials.gov with the trial protocol published and the study protocol was approved. The study population is the same as the one described in a previous work and written informed consent was obtained from all individuals . A total of 117 histologically confirmed (WHO 2007) glioma patients were retrospectively enrolled from July 2014 to January 2016, involving 25 WHO grade II, 29 WHO grade III, and 63 WHO grade IV.…”

Section: Methodsmentioning

confidence: 99%

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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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“…Imaging plays an important role in the initial diagnosis of brain tumors and is a routine part of both initial and subsequent evaluation. The complex imaging features of brain tumors, as well as the frequent genetic heterogeneity within tumor types and the invasive nature of the procedures needed to obtain a tissue diagnosis, give rise to diagnostic dilemmas in this field (Table ) …”

Section: Cns Tumor Imagingmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

Cited by 117 publications

References 32 publications

Radiomics strategy for glioma grading using texture features from multiparametric MRI

Radiomics strategy for glioma grading using texture features from multiparametric MRI

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

Artificial intelligence in cancer imaging: Clinical challenges and applications

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