Patient-level grading prediction of prostate cancer from mp-MRI via GMINet

doi:10.1016/j.compbiomed.2022.106168

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Patient-level grading prediction of prostate cancer from mp-MRI via GMINet
	Shao, Lizhi 4; Liu, Zhenyu 1,4; Liu, Jiangang 2,3; Yan, Ye 5; Sun, Kai 4; Liu, Xiangyu 4; Lu, Jian 5; Tian, Jie 2,3,4
刊名	COMPUTERS IN BIOLOGY AND MEDICINE
	2022-11-01
卷号	150 页码:10
关键词	mp-MRI Prostate cancer Grade group Patient-level prediction Deep learning
ISSN号	0010-4825
DOI	10.1016/j.compbiomed.2022.106168
通讯作者	Lu, Jian(lujian@bjmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Magnetic resonance imaging (MRI) is considered the best imaging modality for non-invasive observation of prostate cancer. However, the existing quantitative analysis methods still have challenges in patient-level pre-diction, including accuracy, interpretability, context understanding, tumor delineation dependence, and multiple sequence fusion. Therefore, we propose a topological graph-guided multi-instance network (GMINet) to catch global contextual information of multi-parametric MRI for patient-level prediction. We integrate visual infor-mation from multi-slice MRI with slice-to-slice correlations for a more complete context. A novel strategy of attention folwing is proposed to fuse different MRI-based network branches for mp-MRI. Our method achieves state-of-the-art performance for Prostate cancer on a multi-center dataset (N = 478) and a public dataset (N = 204). The five-classification accuracy of Grade Group is 81.1 +/- 1.8% (multi-center dataset) from the test set of five-fold cross-validation, and the area under curve of detecting clinically significant prostate cancer is 0.801 +/- 0.018 (public dataset) from the test set of five-fold cross-validation respectively. The model also achieves tumor detection based on attention analysis, which improves the interpretability of the model. The novel method is hopeful to further improve the accurate prediction ability of MRI in the diagnosis and treatment of prostate cancer.
资助项目	National Key Research and Development Program of China ; Na-tional Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Associa-tion CAS ; Key Research and Development Project of Jiangsu Province ; [2017YFA0205200] ; [81922040] ; [92059103] ; [81930053] ; [62027901] ; [81227901] ; [Z200027] ; [2019136] ; [BE2018749]
WOS关键词	RADICAL PROSTATECTOMY ; SYSTEM ; CARCINOMA ; BIOPSY
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000875408800007
资助机构	National Key Research and Development Program of China ; Na-tional Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Associa-tion CAS ; Key Research and Development Project of Jiangsu Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50486]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Lu, Jian; Tian, Jie
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China 3.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ China, Beijing 100191, Peoples R China 4.CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China 5.Peking Univ Third Hosp, Dept Urol, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Shao, Lizhi,Liu, Zhenyu,Liu, Jiangang,et al. Patient-level grading prediction of prostate cancer from mp-MRI via GMINet[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,150:10.
APA	Shao, Lizhi.,Liu, Zhenyu.,Liu, Jiangang.,Yan, Ye.,Sun, Kai.,...&Tian, Jie.(2022).Patient-level grading prediction of prostate cancer from mp-MRI via GMINet.COMPUTERS IN BIOLOGY AND MEDICINE,150,10.
MLA	Shao, Lizhi,et al."Patient-level grading prediction of prostate cancer from mp-MRI via GMINet".COMPUTERS IN BIOLOGY AND MEDICINE 150(2022):10.