دانلود مقاله انگلیسی رایگان:یک مدل رادیومیک یادگیری عمیق برای درجه بندی قبل از عمل در مننژیوما - 2019
دانلود بهترین مقالات isi همراه با ترجمه فارسی
دانلود مقاله انگلیسی یادگیری عمیق رایگان
  • A deep learning radiomics model for preoperative grading in meningioma A deep learning radiomics model for preoperative grading in meningioma
    A deep learning radiomics model for preoperative grading in meningioma

    سال انتشار:

    2019


    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


    منبع:

    Sciencedirect - Elsevier - European Journal of Radiology, 116 (2019) 128-134: doi:10:1016/j:ejrad:2019:04:022


    نویسنده:

    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,


    چکیده انگلیسی:

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 7
    حجم فایل: 1026 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی