CASIA OpenIR  > 中国科学院分子影像重点实验室
Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy
Shao, Lizhi1,2; Yan, Ye3; Liu, Zhenyu2,9,12; Ye, Xiongjun4; Xia, Haizhui3; Zhu, Xuehua3; Zhang, Yuting3; Zhang, Zhiying3; Chen, Huiying5; He, Wei5; Liu, Cheng3; Lu, Min6; Huang, Yi3; Ma, Lulin3; Sun, Kai2,8; Zhou, Xuezhi2,8; Yang, Guanyu1,7; Lu, Jian3; Tian, Jie2,8,10,11
Source PublicationTHERANOSTICS
ISSN1838-7640
2020
Volume10Issue:22Pages:10200-10212
Corresponding AuthorYang, Guanyu(yang.list@seu.edu.cn) ; Lu, Jian(lujian@bjmu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractRationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over-and under-treatment. Methods: In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images (T2-weighted sequences with fat suppression, T2WI-FS) containing 5405 slices of prostate tissue, and 2,753 tumor annotations (only T2WI-FS were annotated using RP pathological sections as ground truth) were analyzed for the prediction of patient-level RP GGs. We present a prostate cancer (PCa) framework, PCa-GGNet, that mimics radiologist behavior based on deep reinforcement learning (DRL). We developed and validated it using a multi-center format. Results: Accuracy (ACC) of our model outweighed NB results (0.815 [95% confidence interval (CI): 0.773-0.857] vs. 0.437 [95% CI: 0.335-0.539]). The PCa-GGNet scored higher (kappa value: 0.761) than NB (kappa value: 0.289). Our model significantly reduced the upgrading rate by 27.9% (P < 0.001) and downgrading rate by 6.4% (P = 0.029). Conclusions: DRL using MRI can be applied to the prediction of patient-level RP GGs to reduce upgrading and downgrading from biopsy, potentially improving the clinical benefits of prostate cancer oncologic controls.
Keywordprostate cancer Gleason grade groups deep reinforcement learning prostate cancer grading magnetic resonance imaging
DOI10.7150/thno.48706
WOS KeywordGLEASON SCORE ; TARGETED BIOPSY ; CANCER ; CLASSIFICATION ; OPPORTUNITIES ; CONCORDANCE ; VALIDATION ; DIAGNOSIS ; FEATURES ; SYSTEM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[61871004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[31571001] ; National Natural Science Foundation of China[61828101] ; National Key Research and Development Program of China[2018YFC0115900] ; Beijing Natural Science Foundation[7182109] ; 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 University-Nanjing Medical University Cooperative Research Project[2242019K3DN08]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China ; Beijing Natural Science Foundation ; 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 University-Nanjing Medical University Cooperative Research Project
WOS Research AreaResearch & Experimental Medicine
WOS SubjectMedicine, Research & Experimental
WOS IDWOS:000596762400001
PublisherIVYSPRING INT PUBL
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42732
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 211189, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing 100190, Peoples R China
3.Peking Univ Third Hosp, Dept Urol, Beijing, Peoples R China
4.Peking Univ Peoples Hosp, Urol & Lithotripsy Ctr, Beijing, Peoples R China
5.Peking Univ Third Hosp, Dept Radiol, Beijing, Peoples R China
6.Peking Univ Third Hosp, Dept Pathol, Beijing, Peoples R China
7.Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, LIST, Nanjing, Peoples R China
8.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China
9.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
10.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
11.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
12.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shao, Lizhi,Yan, Ye,Liu, Zhenyu,et al. Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy[J]. THERANOSTICS,2020,10(22):10200-10212.
APA Shao, Lizhi.,Yan, Ye.,Liu, Zhenyu.,Ye, Xiongjun.,Xia, Haizhui.,...&Tian, Jie.(2020).Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.THERANOSTICS,10(22),10200-10212.
MLA Shao, Lizhi,et al."Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy".THERANOSTICS 10.22(2020):10200-10212.
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