CASIA OpenIR  > 中国科学院分子影像重点实验室
Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists
Shao, Lizhi1,2; Liu, Zhenyu2,3; Yan, Ye4; Liu, Jiangang5,6; Ye, Xiongjun7; Xia, Haizhui4; Zhu, Xuehua4; Zhang, Yuting4; Zhang, Zhiying4; Chen, Huiying8; He, Wei2,3; Liu, Cheng1,2; Lu, Min4,9; Huang, Yi4; Sun, Kai2; Zhou, Xuezhi2; Yang, Guanyu1; Lu, Jian4; Tian, Jie6,10,11
Source PublicationIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
ISSN0018-9294
2021-12-01
Volume68Issue:12Pages:3690-3700
Corresponding AuthorYang, Guanyu(yang.list@seu.edu.cn) ; Lu, Jian(lujian@bjmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractThe grade groups (GGs) of Gleason scores (Gs) is the most critical indicator in the clinical diagnosis and treatment system of prostate cancer. End-to-end method for stratifying the patient-level pathological appearance of prostate cancer (PCa) in magnetic resonance (MRI) are of high demand for clinical decision. Existing methods typically employ a statistical method for integrating slice-level results to a patient-level result, which ignores the asymmetric use of ground truth (GT) and overall optimization. Therefore, more domain knowledge (e.g., diagnostic logic of radiologists) needs to be incorporated into the design of the framework. The patient-level GT is necessary to be logically assigned to each slice of a MRI to achieve joint optimization between slice-level analysis and patient-level decision-making. In this paper, we propose a framework (PCa-GGNet-v2) that learns from radiologists to capture signs in a separate two-dimensional (2-D) space of MRI and further associate them for the overall decision, where all steps are optimized jointly in an end-to-end trainable way. In the training phase, patient-level prediction is transferred from weak supervision to supervision with GT. An association route records the attentional slice for reweighting loss of MRI slices and interpretability. We evaluate our method in an in-house multi-center dataset (N = 570) and PROSTATEx (N = 204), which yields five-classification accuracy over 80% and AUC of 0.804 at patient-level respectively. Our method reveals the state-of-the-art performance for patient-level multi-classification task to personalized medicine.
KeywordMagnetic resonance imaging Principal component analysis Pathology Lesions Optimization Task analysis Predictive models Gleason score prostate cancer patient-level prediction joint optimization MRI reinforcement learning
DOI10.1109/TBME.2021.3082176
WOS KeywordCANCER ; RADIOMICS ; SYSTEM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China from the Beijing Natural Science Foundation[81922040] ; National Natural Science Foundation of China from the Beijing Natural Science Foundation[61871004] ; National Natural Science Foundation of China from the Beijing Natural Science Foundation[81930053] ; National Natural Science Foundation of China from the Beijing Natural Science Foundation[81227901] ; National Natural Science Foundation of China from the Beijing Natural Science Foundation[81527805] ; National Natural Science Foundation of China from the Beijing Natural Science Foundation[31571001] ; National Natural Science Foundation of China from the Beijing Natural Science Foundation[61828101] ; National Key Research and Development Program of China[2018YFC0115900] ; National Key R&D Program of China[2017YFA0205200] ; Chinese Academy of Sciences[XDB32030200] ; Chinese Academy of Sciences[XDB01030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Youth Innovation Promotion Association CAS[2019136] ; Key Research and Development Project of Jiangsu Province[BE2018749] ; Southeast UniversityNanjing Medical University[2242019K3DN08] ; Beijing Natural Science Foundation[Z200027] ; Beijing Natural Science Foundation[7182109]
Funding OrganizationNational Natural Science Foundation of China from the Beijing Natural Science Foundation ; National Key Research and Development Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Key Research and Development Project of Jiangsu Province ; Southeast UniversityNanjing Medical University ; Beijing Natural Science Foundation
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000720518600027
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46507
Collection中国科学院分子影像重点实验室
Corresponding AuthorYang, Guanyu; Lu, Jian; Tian, Jie
Affiliation1.Southeast Univ, Sch Comp Sci & Engn, Nanjing 210096, Peoples R China
2.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Peking Univ Third Hosp, Dept Urol, Beijing 100191, Peoples R China
5.Bei hang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
6.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ Chin, Beijing, Peoples R China
7.Peking Univ Peoples Hosp, Urol & Lithotripsy Ctr, Beijing, Peoples R China
8.Peking Univ Third Hosp, Dept Radiol, Beijing, Peoples R China
9.Peking Univ Third Hosp, Dept Pathol, Beijing, Peoples R China
10.Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst,I, Beijing 100190, Peoples R China
11.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Shao, Lizhi,Liu, Zhenyu,Yan, Ye,et al. Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2021,68(12):3690-3700.
APA Shao, Lizhi.,Liu, Zhenyu.,Yan, Ye.,Liu, Jiangang.,Ye, Xiongjun.,...&Tian, Jie.(2021).Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,68(12),3690-3700.
MLA Shao, Lizhi,et al."Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 68.12(2021):3690-3700.
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