CASIA OpenIR  > 中国科学院分子影像重点实验室
The potential of prostate gland radiomic features in identifying the Gleason score
Gong, Lixin1,2; Xu, Min3; Fang, Mengjie2,4; He, Bingxi2,5; Li, Hailin2,5; Fang, Xiangming3; Dong, Di2,4; Tian, Jie1,2,5,6
Source PublicationCOMPUTERS IN BIOLOGY AND MEDICINE
ISSN0010-4825
2022-05-01
Volume144Pages:11
Corresponding AuthorFang, Xiangming(xiangming_fang@njmu.edu.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractBackground: Gleason score (GS) is one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and > 7. Methods: We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them. Results: Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation. Conclusion: 2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.
KeywordMRI Radiomics Prostate cancer Gleason score Gland segmentation
DOI10.1016/j.compbiomed.2022.105318
WOS KeywordCLINICALLY SIGNIFICANT ; MULTIPARAMETRIC MRI ; CANCER ; ANTIGEN ; GUIDELINES ; DIAGNOSIS ; SELECTION ; PATTERNS ; BIOPSY ; SYSTEM
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81930053] ; National Key R&D Program of China[2017YFA 0205200] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[Z20J00105] ; Chinese Academy of Sciences[GJJSTD2017 0004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; Youth Innovation Promotion Association CAS
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS SubjectBiology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS IDWOS:000806843400008
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49612
Collection中国科学院分子影像重点实验室
Corresponding AuthorFang, Xiangming; Dong, Di; Tian, Jie
Affiliation1.Northeastern Univ, Coll Med, Shenyang 110016, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Nanjing Med Univ, Wuxi Peoples Hosp, Imaging Ctr, Wuxi 214023, Jiangsu, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
6.Jinan Univ, Zhuhai Precis Med Ctr, Zhuhai Peoples Hosp, Zhuhai 519000, 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
Gong, Lixin,Xu, Min,Fang, Mengjie,et al. The potential of prostate gland radiomic features in identifying the Gleason score[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,144:11.
APA Gong, Lixin.,Xu, Min.,Fang, Mengjie.,He, Bingxi.,Li, Hailin.,...&Tian, Jie.(2022).The potential of prostate gland radiomic features in identifying the Gleason score.COMPUTERS IN BIOLOGY AND MEDICINE,144,11.
MLA Gong, Lixin,et al."The potential of prostate gland radiomic features in identifying the Gleason score".COMPUTERS IN BIOLOGY AND MEDICINE 144(2022):11.
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