CASIA OpenIR
Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions
Xu, Min1,2; Fang, Mengjie2,3; Zou, Jian5; Yang, Shudong6; Yu, Dongdong2,3; Zhong, Lianzhen2,3; Hu, Chaoen2,3; Zang, Yali2,3; Di, Dong2,3,4; Tian, Jie2,3,4; Fang, Xiangming1
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-05-01
Volume114Pages:38-44
Corresponding AuthorDi, Dong(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Fang, Xiangming(xiangming_fang@njmu.edu.cn)
AbstractPurpose: To investigate the efficiency of radiomics signature in discriminating between benign and malignant prostate lesions with similar biparametric magnetic resonance imaging (bp-MRI) findings. Experimental design: Our study consisted of 331 patients underwent bp-MRI before pathological examination from January 2013 to November 2016. Radiomics features were extracted from peripheral zone (PZ), transition zone (TZ), and lesion areas segmented on images obtained by T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and its derivative apparent-diffusion coefficient (ADC) imaging. The individual prediction model, built using the clinical data and biparametric MRI features (Bp signature), was prepared using data of 232 patients and validated using data of 99 patients. The predictive performance was calculated and demonstrated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The Bp signature, based on the six selected radiomics features of bp-MRI, showed better discrimination in the validation cohort (area under the curve [AUC], 0.92) than on each subcategory images (AUC, 0.81 on T2WI; AUC, 0.77 on DWI; AUC, 0.89 on ADC). The differential diagnostic efficiency was poorer with the clinical model (AUC, 0.73), built using the selected independent clinical risk factors with statistical significance (P < 0.05), than with the Bp signature. Discrimination efficiency improved when including the Bp signature and clinical factors [i.e., the individual prediction model (AUC, 0.93)]. Conclusion: The Bp signature, based on bp-MRI, performed better than each single imaging modality. The individual prediction model including the radiomics signatures and clinical factors showed better preoperative diagnostic performance, which could contribute to clinical individualized treatment.
KeywordProstate cancer Radiomics Biparametric magnetic resonance imaging Medical imaging
DOI10.1016/j.ejrad.2019.02.032
WOS KeywordACTIVE SURVEILLANCE ; HISTOGRAM ANALYSIS ; CANCER STATISTICS ; TUMOR-DETECTION ; RESONANCE ; PREDICTION ; BIOPSY ; ELIGIBILITY ; MORTALITY ; DIAGNOSIS
Indexed BySCI
Language英语
Funding Projectspecial program for science and technology development from the Ministry of science and technology, China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; National Natural Science Foundation of China
Funding Organizationspecial program for science and technology development from the Ministry of science and technology, China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; National Natural Science Foundation of China
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000464983700006
PublisherELSEVIER IRELAND LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24913
Collection中国科学院自动化研究所
Corresponding AuthorDi, Dong; Tian, Jie; Fang, Xiangming
Affiliation1.Nanjing Med Univ, Imaging Ctr, Wuxi Peoples Hosp, 299 Qingyang Rd, Wuxi 214023, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
4.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Nanjing Med Univ, Ctr Clin Res, Wuxi Peoples Hosp, 299 Qingyang Rd, Wuxi 214023, Jiangsu, Peoples R China
6.Nanjing Med Univ, Wuxi Peoples Hosp, Dept Pathol, 299 Qingyang Rd, Wuxi 214023, Jiangsu, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Min,Fang, Mengjie,Zou, Jian,et al. Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,114:38-44.
APA Xu, Min.,Fang, Mengjie.,Zou, Jian.,Yang, Shudong.,Yu, Dongdong.,...&Fang, Xiangming.(2019).Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions.EUROPEAN JOURNAL OF RADIOLOGY,114,38-44.
MLA Xu, Min,et al."Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions".EUROPEAN JOURNAL OF RADIOLOGY 114(2019):38-44.
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