Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI

doi:10.1186/s13244-022-01227-2

	Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI
	Liu, Hong 1; Jiao, Menglei 1,6; Yuan, Yuan 2; Ouyang, Hanqiang 3,4,5; Liu, Jianfang 2; Li, Yuan 2; Wang, Chunjie 2; Lang, Ning 2; Qian, Yueliang 1; Jiang, Liang 3,4,5; Yuan, Huishu 2; Wang, Xiangdong 1
	2022-05-10
发表期刊	INSIGHTS INTO IMAGING
ISSN	1869-4101
卷号	13 期号:1 页码:11
摘要	Background The application of deep learning has allowed significant progress in medical imaging. However, few studies have focused on the diagnosis of benign and malignant spinal tumors using medical imaging and age information at the patient level. This study proposes a multi-model weighted fusion framework (WFF) for benign and malignant diagnosis of spinal tumors based on magnetic resonance imaging (MRI) images and age information. Methods The proposed WFF included a tumor detection model, sequence classification model, and age information statistic module based on sagittal MRI sequences obtained from 585 patients with spinal tumors (270 benign, 315 malignant) between January 2006 and December 2019 from the cooperative hospital. The experimental results of the WFF were compared with those of one radiologist (D1) and two spine surgeons (D2 and D3). Results In the case of reference age information, the accuracy (ACC) (0.821) of WFF was higher than three doctors' ACC (D1: 0.686; D2: 0.736; D3: 0.636). Without age information, the ACC (0.800) of the WFF was also higher than that of the three doctors (D1: 0.750; D2: 0.664; D3:0.614). Conclusions The proposed WFF is effective in the diagnosis of benign and malignant spinal tumors with complex histological types on MRI.
关键词	Spine tumor Benign Malignant Deep learning MRI
DOI	10.1186/s13244-022-01227-2
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation[Z190020] ; National Natural Science Foundation of China[81871326] ; National Natural Science Foundation of China[81971578] ; Capital's Funds for Health Improvement and Research[2020-440916] ; Clinical Medicine Plus X-Young Scholars Project, Peking University ; Fundamental Research Funds for the Central Universities[PKU2021LCXQ005]
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000793164700001
出版者	SPRINGER
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19537
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Hong; Jiang, Liang; Yuan, Huishu; Wang, Xiangdong
作者单位	1.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China 2.Peking Univ Third Hosp, Dept Radiol, 49 North Garden Rd, Beijing 100191, Peoples R China 3.Peking Univ Third Hosp, Dept Orthopaed, 49 North Garden Rd, Beijing 100191, Peoples R China 4.Engn Res Ctr Bone & Joint Precis Med, Beijing 100191, Peoples R China 5.Beijing Key Lab Spinal Dis Res, Beijing 100191, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100086, Peoples R China
推荐引用方式 GB/T 7714	Liu, Hong,Jiao, Menglei,Yuan, Yuan,et al. Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI[J]. INSIGHTS INTO IMAGING,2022,13(1):11.
APA	Liu, Hong.,Jiao, Menglei.,Yuan, Yuan.,Ouyang, Hanqiang.,Liu, Jianfang.,...&Wang, Xiangdong.(2022).Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI.INSIGHTS INTO IMAGING,13(1),11.
MLA	Liu, Hong,et al."Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI".INSIGHTS INTO IMAGING 13.1(2022):11.