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

文献类型：期刊论文

作者Liu, Hong1; Jiao, Menglei1,6; Yuan, Yuan2; Ouyang, Hanqiang3,4,5; Liu, Jianfang2; Li, Yuan2; Wang, Chunjie2; Lang, Ning2; Qian, Yueliang1; Jiang, Liang3,4,5
刊名INSIGHTS INTO IMAGING
出版日期2022-05-10
卷号13期号:1页码:11
ISSN号1869-4101
关键词Spine tumor Benign Malignant Deep learning MRI
DOI10.1186/s13244-022-01227-2
英文摘要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.
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
语种英语
出版者SPRINGER
WOS记录号WOS:000793164700001
源URL[http://119.78.100.204/handle/2XEOYT63/19536]  
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.

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来源：计算技术研究所

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