دانلود مقاله انگلیسی رایگان:یادگیری ماشین برای تمایز لیپوماتیک رحمی T2 با وزنی T2 با استفاده از ویژگیهای تصویربرداری رزونانس مغناطیسی چند پارامتری مغناطیسی از سارکوم رحمی - 2019
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  • Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features
    Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features


    ترجمه فارسی عنوان مقاله:

    یادگیری ماشین برای تمایز لیپوماتیک رحمی T2 با وزنی T2 با استفاده از ویژگیهای تصویربرداری رزونانس مغناطیسی چند پارامتری مغناطیسی از سارکوم رحمی


    منبع:

    Sciencedirect - Elsevier - Academic Radiology, 26 (2019) 1390-1399: doi:10:1016/j:acra:2018:11:014


    نویسنده:

    Masataka Nakagawa, MD, PhD, Takeshi Nakaura, MD, PhD, Tomohiro Namimoto, MD, PhD, Yuji Iyama, MD, PhD, Masafumi Kidoh, MD, PhD, Kenichiro Hirata, MD, PhD, Yasunori Nagayama, MD, PhD, Hideaki Yuki, MD, PhD, Seitaro Oda, MD, PhD, Daisuke Utsunomiya, MD, PhD, Yasuyuki Yamashita, MD, PhD


    چکیده انگلیسی:

    Rationale and Objective: Uterine leiomyomas with high signal intensity on T2-weighted imaging (T2WI) can be difficult to distinguish from sarcomas. This study assessed the feasibility of using machine learning to differentiate uterine sarcomas from leiomyomas with high signal intensity on T2WI on multiparametric magnetic resonance imaging. Materials and Methods: This retrospective study included 80 patients (50 with benign leiomyoma and 30 with uterine sarcoma) who underwent pelvic 3 T magnetic resonance imaging examination for the evaluation of uterine myometrial smooth muscle masses with high signal intensity on T2WI. We used six machine learning techniques to develop prediction models based on 12 texture parameters on T1WI and T2WI, apparent diffusion coefficient maps, and contrast-enhanced T1WI, as well as tumor size and age. We calculated the areas under the curve (AUCs) using receiver-operating characteristic analysis for each model by 10-fold cross-validation and compared these to those for two board-certified radiologists. Results: The eXtreme Gradient Boosting model gave the highest AUC (0.93), followed by the random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and logistic regression models. Age was the most important factor for differentiation (leiomyoma 44.9 § 11.1 years; sarcoma 58.9 § 14.7 years; p < 0.001). The AUC for the eXtreme Gradient Boosting was significantly higher than those for both radiologists (0.93 vs 0.80 and 0.68, p = 0.03 and p < 0.001, respectively). Conclusion: Machine learning outperformed experienced radiologists in the differentiation of uterine sarcomas from leiomyomas with high signal intensity on T2WI.
    Key Words: Magnetic resonance imaging | Uterine neoplasm | Leiomyoma | Machine learning | Sarcoma


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 10
    حجم فایل: 2359 کیلوبایت

    قیمت: رایگان


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