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Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

文献类型：期刊论文

作者Shao, Lizhi4,6; Yan, Ye7; Liu, Zhenyu1,3,6; Ye, Xiongjun8; Xia, Haizhui7; Zhu, Xuehua7; Zhang, Yuting7; Zhang, Zhiying7; Chen, Huiying9; He, Wei9
刊名THERANOSTICS
出版日期2020
卷号10期号:22页码:10200-10212
ISSN号1838-7640
关键词prostate cancer Gleason grade groups deep reinforcement learning prostate cancer grading magnetic resonance imaging
DOI10.7150/thno.48706
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Rationale: 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.
WOS关键词GLEASON SCORE ; TARGETED BIOPSY ; CANCER ; CLASSIFICATION ; OPPORTUNITIES ; CONCORDANCE ; VALIDATION ; DIAGNOSIS ; FEATURES ; SYSTEM
资助项目National 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]
WOS研究方向Research & Experimental Medicine
语种英语
出版者IVYSPRING INT PUBL
WOS记录号WOS:000596762400001
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/42731]  
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.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
4.Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China
5.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing 100190, Peoples R China
7.Peking Univ Third Hosp, Dept Urol, Beijing, Peoples R China
8.Peking Univ Peoples Hosp, Urol & Lithotripsy Ctr, Beijing, Peoples R China
9.Peking Univ Third Hosp, Dept Radiol, Beijing, 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,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.

入库方式： OAI收割

来源：自动化研究所

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