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

文献类型：期刊论文

作者Shao, Lizhi5; Liu, Zhenyu4,5; Liu, Jiangang1,2; Yan, Ye3; Sun, Kai5; Liu, Xiangyu5; Lu, Jian3; Tian, Jie1,2,5
刊名COMPUTERS IN BIOLOGY AND MEDICINE
出版日期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
DOI10.1016/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.
WOS关键词RADICAL PROSTATECTOMY ; SYSTEM ; CARCINOMA ; BIOPSY
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研究方向Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000875408800007
出版者PERGAMON-ELSEVIER SCIENCE LTD
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
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.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ China, Beijing 100191, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
3.Peking Univ Third Hosp, Dept Urol, Beijing 100191, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, 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.

入库方式： OAI收割

来源：自动化研究所

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