عنوان انگلیسی مقاله:
A deep learning radiomics model for preoperative grading in meningioma
ترجمه فارسی عنوان مقاله:
یک مدل رادیومیک یادگیری عمیق برای درجه بندی قبل از عمل در مننژیوما
Sciencedirect - Elsevier - European Journal of Radiology, 116 (2019) 128-134: doi:10:1016/j:ejrad:2019:04:022
Yongbei Zhua,b,c,1, Chuntao Mana,1, Lixin Gongd,1, Di Dongb,e,1, Xinyi Yuf, Shuo Wangb,e, Mengjie Fangb,e, Siwen Wangb,e, Xiangming Fangf,⁎, Xuzhu Cheng,⁎⁎⁎, Jie Tianb,
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