Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging

Hale, Andrew T.; Stonko, David P.; Wang, Li; Strother, Megan K.; Chambless, Lola B.

doi:10.3171/2018.8.focus18191

Cited by 57 publications

(35 citation statements)

References 32 publications

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“…Altogether, these results indicate that newly developed analysis algorithms are promising tools for a more nuanced tumor stratification. Machine learning algorithms that were implemented to predict meningioma grade on preoperative MRIs have been shown to be highly accurate [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

Kalasauskas

Kronfeld

Renovanz

et al. 2020

Cancers

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Up to 60% of atypical meningiomas (World Health Organization (WHO) grade II) reoccur within 5 years after resection. However, no clear radiological criteria exist to identify tumors with higher risk of relapse. In this study, we aimed to assess the association of certain radiomic and semantic features of atypical meningiomas in MRI with tumor recurrence. We identified patients operated on primary atypical meningiomas in our department from 2007 to 2017. An analysis of 13 quantitatively defined radiomic and 11 qualitatively defined semantic criteria was performed based on preoperative MRI scans. Imaging characteristics were assessed along with clinical and survival data. The analysis included 76 patients (59% women, mean age 59 years). Complete tumor resection was achieved in 65 (86%) cases, and tumor relapse occurred in 17 (22%) cases. Mean follow-up time was 41.6 (range 3–168) months. Cystic component was significantly associated with tumor recurrence (odds ratio (OR) 21.7, 95% confidence interval (CI) 3.8–124.5) and shorter progression-free survival (33.2 vs. 80.7 months, p < 0.001), whereas radiomic characteristics had no predictive value in univariate analysis. However, multivariate analysis demonstrated significant predictive value of high cluster prominence (hazard ratio (HR) 5.89 (1.03–33.73) and cystic component (HR 20.21 (2.46–166.02)) for tumor recurrence. The combination of radiomic and semantic features might be an effective tool for identifying patients with high-risk atypical meningiomas. The presence of a cystic component in these tumors is associated with a high risk of tumor recurrence.

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Section: Discussionmentioning

confidence: 99%

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

Kalasauskas

Kronfeld

Renovanz

et al. 2020

Cancers

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“…Texture analysis has shown promising diagnostic ability in meningioma grading in previous studies (14, 20–22). Additionally, the quantitative evaluation of texture features has been applied into machine learning technology to differentiate high-grade meningiomas from low-grade meningiomas (20, 21). In the current study, we applied multiple classification methods to systematically grade meningiomas.…”

Section: Introductionmentioning

confidence: 91%

The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study

et al. 2019

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Objective: The purpose of the current study is to investigate whether texture analysis-based machine learning algorithms could help devise a non-invasive imaging biomarker for accurate classification of meningiomas using machine learning algorithms.Method: The study cohort was established from the hospital database by reviewing the medical records. Patients were selected if they underwent meningioma resection in the neurosurgery department between January 2015 and December 2018. A total number of 40 texture parameters were extracted from pretreatment postcontrast T1-weighted (T1C) images based on six matrixes. Three feature selection methods were adopted, namely, distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Multiclass classification methods of linear discriminant analysis (LDA) and support vector machine (SVM) algorithms were employed to establish the classification models. The diagnostic performances of models were evaluated with confusion matrix based on which the areas under the curve, accuracy, and Kappa value of models were calculated.Result: Confusion matrix showed that the LDA-based models represented better diagnostic performances than SVM-based models. The highest accuracy among LDA-based models was 75.6%, shown in the combination of Lasso + LDA. The optimal models for SVM-based models was Lasso+SVM, with accuracy of 59.0% in the testing group. One of the SVM-based models, GBDT+SVM, was overfitting, suggesting that this model was not suitable for application.Conclusion: Machine learning algorithms with texture features extracted from T1C images could potentially serve as the assistant imaging biomarkers for presurgically grading meningiomas.

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“…The purpose of the prognostic model is to provide valid outcome predictions for new data patients. Prognostic models have been proposed as an alternative to improve power in long-term TBI outcome prediction [80]- [82] and assist in clinical audit [83], [84]. For this purpose, it is crucial to evaluate both internal and external validities of proposed prognostic models.…”

Section: Predictive Model Validationmentioning

confidence: 99%

Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review

Noor

Ibrahim

2020

IEEE Access

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Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI). This systematic review aims to determine how previous studies have taken into consideration the important modeling issues for quantitative EEG (qEEG) predictors in developing prognostic models. A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG data. Fourteen studies were identified that evaluated ML algorithms using qEEG predictors to predict outcome in patients with moderate to severe TBI. In each model, a maximum of five qEEG predictors were selected to determine the association between these parameters, and favorable or unfavorable predicted outcomes. The most common ML technique used was logistic regression, but the algorithms varied depending on the types and numbers of qEEG predictors selected in each model. The qEEG variability for the relative and absolute band powers were the most common qEEG predictors included in the models (46%) followed by total EEG power of all frequency bands (31%), EEG-reactivity (31%) and coherence (15%). Model performance was often quantified by the area under the receiving-operating characteristic curve (AUROC) rather than by accuracy rate. Various ML models have demonstrated great potential, especially using qEEG predictors, to predict outcome in patients with moderate to severe TBI. INDEX TERMS Electroencephalography, machine learning algorithms, prediction methods, reviews.

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Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging

Cited by 57 publications

References 32 publications

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study

Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review

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