Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| Patient-level grading prediction of prostate cancer from mp-MRI via GMINet | |
Shao, Lizhi1; Liu, Zhenyu1,2 ; Liu, Jiangang3,4; Yan, Ye5; Sun, Kai1; Liu, Xiangyu1; Lu, Jian5; Tian, Jie1,3,4
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| Source Publication | COMPUTERS IN BIOLOGY AND MEDICINE
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| ISSN | 0010-4825 |
| 2022-11-01 | |
| Volume | 150Pages:10 |
| Corresponding Author | Lu, Jian(lujian@bjmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) |
| Abstract | 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. |
| Keyword | mp-MRI Prostate cancer Grade group Patient-level prediction Deep learning |
| DOI | 10.1016/j.compbiomed.2022.106168 |
| WOS Keyword | RADICAL PROSTATECTOMY ; SYSTEM ; CARCINOMA ; BIOPSY |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 Research Area | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
| WOS Subject | Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology |
| WOS ID | WOS:000875408800007 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50487 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Lu, Jian; Tian, Jie |
| Affiliation | 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 |
| Recommended Citation 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. |
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