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