Related Experiment Video
Updated: Feb 5, 2026
10:17
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
Published on: January 8, 2018
13.7K
Brain MRI Radiomic First-Order Features for Presurgical Prediction of Meningioma Grading.
Camilo Pineda-Ibarra1,2,3, Josep Puig2, Diego Nuñez-Leiva4
1Department of Neuroradiology, Diagnostic Imaging Centre, Hospital Clinic de Barcelona, Barcelona, Spain.
Summary
Radiomics using MRI features can non-invasively predict meningioma grades. Histogram-based radiomic features from T1, FLAIR, and T1CE MRI sequences show promise in distinguishing between Grade 1 and Grade 2 meningiomas.
Area of Science:
- Neurosurgery
- Radiology
- Oncology
Background:
- Meningioma grading is crucial for treatment planning, ranging from observation to aggressive interventions.
- Radiomics offers a potential non-invasive method for tumor characterization, avoiding the need for biopsies.
- Distinguishing between different meningioma grades preoperatively can significantly impact patient management.
Purpose of the Study:
- To evaluate the efficacy of radiomic features (RFs) in differentiating between WHO Grade 1 and Grade 2 meningiomas.
- To assess the potential of multiparametric MRI radiomics as a non-invasive tool for meningioma grading.
- To identify specific radiomic features predictive of meningioma grade.
Main Methods:
- Retrospective collection of preoperative multiparametric MRI data (T1, T2, T2GRE, FLAIR, ADC, T1CE) from 150 meningioma patients.
- Extraction of 75 radiomic features from semimanually segmented tumors using MintLesion Research software.
- Application of the Lasso method for variable selection and 10-fold cross-validation to identify predictive features.
Main Results:
- The study identified key radiomic features differentiating Grade 1 from Grade 2 meningiomas, including histogram coefficient of variation on T1CE, maximum histogram gradient on T1, and quartile coefficient of dispersion on FLAIR.
- Combined radiomic features achieved an area under the curve (AUC) of 0.814 for accurate meningioma grade differentiation.
- Texture features and metrics from T2, T2GRE, and ADC sequences did not significantly discriminate between meningioma grades.
Conclusions:
- First-order histogram-based radiomic features from T1, FLAIR, and T1CE MRI sequences show potential for preoperative meningioma grade prediction.
- These findings suggest radiomics can aid in clinical decision-making and personalized treatment strategies for meningioma patients.
- Further validation through larger, multicenter studies is recommended to confirm these promising results.
Related Concept Videos
01:19Graded Potential
7.1K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
7.1K
02:27Predicting Molecular Geometry
45.9K
VSEPR Theory for Determination of Electron Pair Geometries
45.9K
01:15Types of Aggregate Grading
1.5K
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
1.5K
01:03Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
01:19Sieve Analysis and Grading Curves
982
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
982
01:07End Point Prediction: Gran Plot
1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.2K