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1 |
Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study
مقایسه طبقه بندی کننده های یادگیری ماشین برای تمایز درجه 1 از درجه های بالاتر در مننژیوما: یک مطالعه رادیومتری چند متری-2019 Background and purpose: Advanced imaging analysis for the prediction of tumor biology and modelling of
clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research.
Here we test the hypothesis that a machine learning approach can distinguish grade 1 from higher
gradings in meningioma patients using radiomics features derived from a heterogenous multicenter dataset of
multi-paramedic MRI.
Methods: A total of 138 patients from 5 international centers that underwent MRI prior to surgical resection of
intracranial meningiomas were included. Segmentation was performed manually on co-registered multi-parametric
MR images using apparent diffusion coefficient (ADC) maps, T1-weighted (T1), post-contrast T1-weighted
(T1c), subtraction maps (Sub, T1c – T1), T2-weighted fluid-attenuated inversion recovery (FLAIR) and T2-
weighted (T2) images. Feature selection was performed and using cross-validation to separate training from
testing data, four machine learning classifiers were scored on combinations of MRI modalities: random forest
(RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP).
Results: The best AUC of 0.97 (1.0 and 0.97 for sensitivity and specificity) was observed for the combination of
ADC, ADC of the peritumoral edema, T1, T1c, Sub and FLAIR-derived features using only 16 of the 10,914
possible features and XGBoost.
Conclusions: Machine learning using radiomics features derived from multi-parametric MRI is capable of high
AUC scores with high sensitivity and specificity in classifying meningiomas between low and higher gradings
despite heterogeneous protocols across different centers. Feature selection can be performed effectively even
when extracting a large amount of data for radiomics fingerprinting Keywords: Random forest | Support vector machine | Multilayer perceptron | XGBoost | Machine learning | Meningioma | Grading | Feature selection |
مقاله انگلیسی |
2 |
A deep learning radiomics model for preoperative grading in meningioma
یک مدل رادیومیک یادگیری عمیق برای درجه بندی قبل از عمل در مننژیوما-2019 Objectives: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on
routine post-contrast MRI.
Methods: We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast
MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All
the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep
learning features were extracted by the convolutional neural network. The random forest algorithm was used to
select features with importance values over 0.001, upon which a deep learning signature was built by a linear
discriminant analysis classifier. The performance of our DLR model was assessed by discrimination and calibration
in the independent validation cohort. For comparison, a radiomic model based on hand-crafted features
and a fusion model were built.
Results: The DLR signature comprised 39 deep learning features and showed good discrimination performance in
both the primary and validation cohorts. The area under curve (AUC), sensitivity, and specificity for predicting
meningioma grades were 0.811(95% CI, 0.635–0.986), 0.769, and 0.898 respectively in the validation cohort.
DLR performance was superior over the hand-crafted features. Calibration curves of DLR model showed good
agreements between the prediction probability and the observed outcome of high-grade meningioma.
Conclusions: Using routine MRI data, we developed a DLR model with good performance for noninvasively
individualized prediction of meningioma grades, which achieved a quantization capability superior over the
hand-crafted features. This model has potential to guide and facilitate the clinical decision-making of whether to
observe or to treat patients by providing prognostic information. Keywords: Radiomics | Deep learning | Meningioma | Tumor grading | Magnetic resonance imaging |
مقاله انگلیسی |