Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers

Qian, Zenghui; Li, Yiming; Wang, Yongzhi; Li, Lianwang; Zhao, Yuanli; Wang, Kai; Li, Shaowu; Tang, Ke; Zhang, Chuanbao; Fan, Xing; Chen, Baoshi; Li, Wenbin

doi:10.1016/j.canlet.2019.02.054

Cited by 150 publications

(128 citation statements)

References 33 publications

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“…Qian et al (45) addressed this important question using MRI radiomics. A large group of patients (n = 412) with untreated brain metastases (n = 170) and treatment naive, newly diagnosed glioblastomas (n = 242) was divided into a training (n = 227) and a test cohort (n = 180).…”

Section: Differentiation Of Brain Metastases From Glioblastomamentioning

confidence: 99%

PET/MRI Radiomics in Patients With Brain Metastases

et al. 2020

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Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.

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Section: Differentiation Of Brain Metastases From Glioblastomamentioning

confidence: 99%

PET/MRI Radiomics in Patients With Brain Metastases

et al. 2020

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“…Survival data are modeled via a Cox proportional hazard layer. Wang et al (Qian et al, 2019) implemented an analysis pipeline to classify glioma cases into grades II, III, and IV gliomas using whole slide tissue images (WSIs) from H&E and Ki-67 stained tissue samples. The pipeline consists of multiple steps, including region-of-interest (ROI) identification, image feature extraction, feature selection, automated grading of slides, and interpretation of the grading results.…”

Section: Related Workmentioning

confidence: 99%

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

et al. 2020

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“…A variety of quantitative radiological features could be extracted from medical images to reveal the information on tumors [14]. Radiomics feature has been applied as the noninvasive alternative to identify the genomic and proteomic changes in tumors, which also broadly utilized in tumor diagnosis, prognosis prediction, treatment selection, gene prediction, and so on [15][16][17][18]. However, the use of radiomics analysis to predict the mutant status of the TERT promoter has not been widely reported.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Prediction of TERT Promoter Mutations in High‐Grade Glioma by Radiomics Analysis Based on Multiparameter MRI

Tian

et al. 2020

BioMed Research International

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Objectives. To investigate the predictors of telomerase reverse transcriptase (TERT) promoter mutations in adults suffered from high-grade glioma (HGG) through radiomics analysis, develop a noninvasive approach to evaluate TERT promoter mutations. Methods. 126 adult patients with HGG (88 in the training cohort and 38 in the validation cohort) were retrospectively enrolled. Totally 5064 radiomics features were, respectively, extracted from three VOIs (necrosis, enhanced, and edema) in MRI. Firstly, an optimal radiomics signature (Radscore) was established based on LASSO regression. Secondly, univariate and multivariate logistic regression analyses were performed to investigate important potential variables as predictors of TERT promoter mutations. Besides, multiparameter models were established and evaluated. Eventually, an optimal model was visualized as radiomics nomogram for clinical evaluations. Results. 6 radiomics features were selected to build Radscore signature through LASSO regression. Among them, 5 were from necrotic VOIs and 1 was from enhanced ones. With univariate and multivariate analysis, necrotic volume percentages of core (CNV), Age, Cho/Cr, Lac, and Radscore were significantly higher in TERTm than in TERTw (p<0.05). 4 models were built in our study. Compared with Model B (Age, Cho/Cr, Lac, and Radscore), Model A (Age, Cho/Cr, Lac, Radscore, and CNV) has a larger AUC in both training (0.955 vs. 0.917, p=0.049) and validation (0.889 vs. 0.868, p=0.039) cohorts. It also has higher performances in net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) evaluation. Conclusively, Model A was visualized as a radiomics nomogram. Calibration curve shows a good agreement between estimated and actual probabilities. Conclusions. Age, Cho/Cr, Lac, CNV, and Radscore are important indicators for TERT promoter mutation predictions in HGG. Tumor necrosis seems to be closely related to TERT promoter mutations. Radiomics nomogram based on multiparameter MRI and CNV has higher prediction accuracies.

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Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers

Cited by 150 publications

References 33 publications

PET/MRI Radiomics in Patients With Brain Metastases

PET/MRI Radiomics in Patients With Brain Metastases

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Noninvasive Prediction of TERT Promoter Mutations in High‐Grade Glioma by Radiomics Analysis Based on Multiparameter MRI

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