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Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists

文献类型：期刊论文

作者Shao, Lizhi1,4; Liu, Zhenyu1,6; Yan, Ye7; Liu, Jiangang2,5; Ye, Xiongjun3; Xia, Haizhui7; Zhu, Xuehua7; Zhang, Yuting7; Zhang, Zhiying7; Chen, Huiying11
刊名IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
出版日期2021-12-01
卷号68期号:12页码:3690-3700
ISSN号0018-9294
关键词Magnetic 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
通讯作者Yang, Guanyu(yang.list@seu.edu.cn) ; Lu, Jian(lujian@bjmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要The 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.
WOS关键词CANCER ; RADIOMICS ; SYSTEM
资助项目National 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]
WOS研究方向Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000720518600027
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/46506]  
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Yang, Guanyu; Lu, Jian; Tian, Jie
作者单位1.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
2.Bei hang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
3.Peking Univ Peoples Hosp, Urol & Lithotripsy Ctr, Beijing, Peoples R China
4.Southeast Univ, Sch Comp Sci & Engn, Nanjing 210096, Peoples R China
5.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ Chin, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
7.Peking Univ Third Hosp, Dept Urol, Beijing 100191, Peoples R China
8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
9.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
10.Peking Univ Third Hosp, Dept Pathol, Beijing, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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