MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery

Speckter, Herwin; Radulović, Marko; Trivodaliev, Kire; Vranes, Velicko; Joaquin, Johanna; Hernández, Wenceslao; Mota, Angel; Bido, Jose; Hernandez, Giancarlo; Rivera, Diones; Suazo, Luis; Valenzuela, Santiago; Stoeter, P.

doi:10.1007/s11060-022-04063-y

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…Compared to subjective clinical characteristics, brain imaging biomarkers may provide more quantitative measures for sus-pected MCI (D. Yang & Hong, 2021). In this study, we developed a other feature types, such as first order, gray level co-occurrence matrix, gray level dependence matrix, gray level run length matrix, and so forth rather than being employed individually (Speckter et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…The employment of wavelet transform allows for the examination of multiple scales, facilitating the enhancement of subtle contrast disparities between lesions and normal tissues. In the realm of medical image diagnostics, log‐sigma, original, and wavelet features are typically utilized in conjunction with other feature types, such as first order, gray level co‐occurrence matrix, gray level dependence matrix, gray level run length matrix, and so forth rather than being employed individually (Speckter et al., 2022 ). Textural features possess widespread utility in the field of imaging and pathology, proving particularly valuable in the realm of diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population

Zheng,

Mu,

Liu

et al. 2023

Brain and Behavior

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ObjectiveWe aim to develop a radiomics model based on 3‐dimensional (3D)‐T1WI images to discriminate amnestic mild cognitive impairment (aMCI) patients from the normal population by measuring changes in frontal white matter.MethodsIn this study, 126 patients with aMCI and 174 normal controls (NC) were recruited from the local community. All subjects underwent routine magnetic resonance imaging examination (including 3D‐T1WI ). Participants were randomly divided into a training set (n = 242, aMCI:102, NC:140) and a testing set (n = 58, aMCI:24, NC:34). Texture features of the frontal lobe were extracted from 3D‐T1WI images. The least absolute shrinkage and selection operator (LASSO) was used to reduce feature dimensions and develop a radiomics signature model. Diagnostic performance was assessed in the training and testing sets using the receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC), sensitivity, and specificity were also calculated. The efficacy of the radiomics model in discriminating aMCI patients from the normal population was assessed by decision curve analysis (DCA).ResultsA total of 108 frontal lobe texture features were extracted from 3D‐T1WI images. LASSO selected 58 radiomic features for the final model, including log‐sigma (n = 18), original (n = 8), and wavelet (n = 32) features. The performance of radiomic features extracted from 3D T1 imaging for distinguishing aMCI patients from controls was: in the training set, AUC was 1.00, and the accuracy, sensitivity, and specificity were 100%, 98%, and 100%, respectively. In the testing set, AUC was 0.82 (95% CI:0.69–0.95), and the accuracy, sensitivity, and specificity were 69%, 92%, and 55%, respectively. The DCA demonstrated that the model had favorable clinical predictive value.ConclusionsTextural features of white matter in the frontal lobe showed potential for distinguishing aMCI from the normal population, which could be a surrogate protocol to aid aMCI screening in clinical setting.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population

Zheng,

Mu,

Liu

et al. 2023

Brain and Behavior

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“…From prior meningioma grading studies, it is known that multilevel feature selection can give better results. Fifteen feature selection methods were selected based on previous related research [13,31,[33][34][35][36], including the filter methods Chi-square (CHSQ), t-test (TSQ), Kruskal-Wallis H-test tests (KWH), variance (VAR), relief (RELF), mutual information (MI), minimum redundancy maximum relevance ensemble (mRMRe) and the embedded methods L1-based logistic regression (L1-LR), elastic net (EN), least absolute shrinkage and selection operator (LASSO), L1-based linear support vector machine (L1-SVM), random forest (RF), extra tree ensemble (ETE), gradient boosting decision tree (GBDT), and xgboost (XGB). In this study, the filter methods were used as the first level of screening to reduce the number of features, and the embedded methods were used as the second level of screening to obtain the final features.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading

Cai,

Wong,

Wong

et al. 2023

Cancers

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Background: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). Methods: This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. Results: The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. Conclusions: The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.

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“…In addition to clinical symptoms and parameters such as age, general health, neurologic de cit, and recurrence after previous treatment, tumor size and localization have served as prognostic indicators. [7][8][9][10][11][12] Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) parameters were also applied to predict meningioma type, consistency, and grading, including texture analysis and machine learning techniques. [13][14][15][16][17][18][19] In a previous analysis of Magnetic Resonance Imaging (MRI) data measured before SRS, several DTI parameter maps, particularly Fraction Anisotropy (FA) values, correlated strongly to treatment outcome.…”

Section: Introductionmentioning

confidence: 99%

Can Apparent Diffusion Coefficient (ADC) maps replace Diffusion Tensor Imaging (DTI) maps to predict the volumetric response of meningiomas to Gamma Knife Radiosurgery?

Speckter

Palque-Santos²,

Mota-Gonzalez³

et al. 2023

J Neurooncol

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Noninvasive methods are desired to predict the treatment response to Stereotactic Radiosurgery (SRS) to improve individual tumor management. In a previous study, we demonstrated that Diffusion Tensor Imaging (DTI)-derived parameter maps signi cantly correlate to SRS response. This study aimed to analyze and compare the predictive value of intratumoral ADC and DTI parameters in patients with meningiomas undergoing radiosurgery. MethodsMR images of 70 patients treated with Gamma Knife SRS for WHO grade I meningiomas were retrospectively reviewed. MR acquisition included pre-and post-treatment DWI and DTI sequences, and subtractions were calculated to assess for radiation-induced changes in the parameter values. ResultsAfter a mean follow-up period (FUP) of 52.7 months, 69 of 70 meningiomas were controlled, with a mean volume reduction of 34.9%. Whereas fractional anisotropy (FA) values of the initial exam showed the highest correlation to tumor volume change at the last FU (CC=-0.607), followed by the differences between rst and second FU values of FA (CC=-0.404) and the rst longitudinal diffusivity (LD) value (CC=-0.375), the correlation coe cients of all ADC values were comparably low. Nevertheless, all these correlations, except for ADC measured at the rst follow-up, reached signi cance. ConclusionFor the rst time, the prognostic value of ADC maps measured in meningiomas before and at rst followup after Gamma Knife SRS, was compared to simultaneously acquired DTI parameter maps. Quantities assessed from ADC maps present signi cant correlations to the volumetric meningioma response but are less effective than correlations with DTI parameters.

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MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery

Cited by 7 publications

References 45 publications

Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population

Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population

Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading

Can Apparent Diffusion Coefficient (ADC) maps replace Diffusion Tensor Imaging (DTI) maps to predict the volumetric response of meningiomas to Gamma Knife Radiosurgery?

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