摘要
乳腺癌及时发现和诊断,并制定合适的治疗方案可以使患者有良好的预后。近年来,深度学习因其强大的特征学习能力而被广泛应用于医学影像学分析领域,尤其在肿瘤相关领域取得诸多进展。与乳腺X线摄影或超声检查相比,MRI的优势是具有高的软组织分辨率、可以多功能及多参数成像并且无电离辐射等。基于MRI深度学习在乳腺病灶检测分割、良恶性鉴别、淋巴结转移预测、分子分型预测以及疗效评估等不同方面的研究进展进行综述。
Breast cancer is the most common cancer in women worldwide,timely detection and diagnosis of breast cancer,and the development of appropriate treatment plans can make patients have a good prognosis.In recent years,deep learning has been widely used in the field of medical image analysis because of its powerful feature learning ability,especially in tumorrelated fields.The advantages of magnetic resonance over mammography or ultrasound are high soft tissue resolution,versatile and multiparametric imaging,and no ionizing radiation.The author reviews the research progress based on MRI deep learning in breast lesion detection and segmentation,benign and malignant discrimination,lymph node metastasis prediction,molecular typing prediction and efficacy evaluation.
作者
赵雪枫
毛宁
谢海柱
ZHAO Xuefeng;MAO Ning;XIE Haizhu(School of Medical Imaging,Binzhou Medical University,Yantai 264003,China;Department of Medical Radiology,Yantai Yuhuangding Hospital,Affiliated Hospital of Qingdao University,Yantai 264001,China)
出处
《医学影像学杂志》
2024年第5期140-142,146,共4页
Journal of Medical Imaging
关键词
乳腺癌
深度学习
肿瘤学
磁共振成像
Breast cancer
Deep learning
Oncology
Magnetic resonance imaging
