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

doi:10.1016/j.compbiomed.2022.106168

	Patient-level grading prediction of prostate cancer from mp-MRI via GMINet
	Shao, Lizhi 1; Liu, Zhenyu1,2 ; Liu, Jiangang 3,4; Yan, Ye 5; Sun, Kai 1; Liu, Xiangyu 1; Lu, Jian 5; Tian, Jie 1,3,4
发表期刊	COMPUTERS IN BIOLOGY AND MEDICINE
ISSN	0010-4825
	2022-11-01
卷号	150 页码:10
通讯作者	Lu, Jian(lujian@bjmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
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.
关键词	mp-MRI Prostate cancer Grade group Patient-level prediction Deep learning
DOI	10.1016/j.compbiomed.2022.106168
关键词[WOS]	RADICAL PROSTATECTOMY ; SYSTEM ; CARCINOMA ; BIOPSY
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS类目	Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS记录号	WOS:000875408800007
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50487
专题	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.CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China 4.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ China, Beijing 100191, 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.