基于动态增强磁共振成像的影像组学和不同CNN的深度学习对乳腺良恶性病变的诊断价值

doi:10.3969/j.issn.2095-9400.2020.06.009

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Abstract Objective: To evaluate and compare the performance of radiomics and deep learning in the diagnosis of benign and malignant breast lesions based on DCE-MRI. Methods: A total of 152 patients receiving breast MRI for diagnosis were analyzed, including 93 patients with malignant cancers, and 59 patients with benign lesions. Three DCE parametric maps corresponding to early wash-in signal enhancement (SE), maximum signal enhancement, and wash-out slope were generated. Radiomics analysis based on texture and intensity histogram, and deep learning using 5 networks (ResNet50, VGG16, VGG19, Xception, InceptionV3), were performed for differential diagnosis. Results: The accuracy of radiomics was 80%; the smallest bounding box obtained higher diagnostic accuracy than that with tumor only and 1.2 times box which was not significant (both P>0.05), and also higher than 1.5 times box and 2.0 times box (both P<0.01); the accuracy of ResNet50 (93%), Xception (94%), InceptionV3 (93%) was significantly higher than VGG16 (80%), VGG19 (79%) (both P<0.01). Conclusion: CNN achieved better diagnostic performance in the diagnosis of benign and malignant breast lesions. The smaller bounding box containing the tumor with small amount of per-tumor tissue had the higher diagnostic accuracy than that with the tumor only and than larger bounding box.

Key words： breast cancer magnetic resonance imaging radiomics deep learning convolutional neural network

Received: 05 September 2019

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	Articles by authors
	ZHOU Jiejie
	ZHANG Yang
	SU Minying
	HE Xiaxia
	XU Ni’na
	YE Shuxin
	LI Jiance
	WANG Ouchen
	WANG Meihao

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https://xb.wmu.edu.cn/EN/10.3969/j.issn.2095-9400.2020.06.009 OR https://xb.wmu.edu.cn/EN/Y2020/V50/I6/475

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