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Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies

文献类型：期刊论文

作者Qi, Yafei3; Zhang, Shuaitong4,5; Wei, Jingwei4,5; Zhang, Gumuyang3; Lei, Jing3; Yan, Weigang6; Xiao, Yu7; Yan, Shuang3; Xue, Huadan3; Feng, Feng3
刊名JOURNAL OF MAGNETIC RESONANCE IMAGING
出版日期2019-12-06
页码10
关键词magnetic resonance imaging radiomics prostate cancer prostate-specific antigen biopsy
ISSN号1053-1807
DOI10.1002/jmri.27008
通讯作者Sun, Hao(sunhao_robert@126.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Jin, Zhengyu(jinzy@pumch.cn)
英文摘要Background Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging. Purpose To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. Study Type Retrospective. Subjects In all, 199 patients with PSA levels of 4-10 ng/mL. Field Strength/Sequence 3T, T-2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. Assessment Lesion regions of interest (ROIs) from T-2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors. Statistical Tests For continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant. Results The combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%. Data Conclusion The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019.
WOS关键词HEALTH INDEX ; ASIAN MEN ; VALIDATION ; TRANSITION ; GUIDELINES ; FEATURES ; SYSTEM ; URINE ; GRADE
资助项目National Natural Science Foundation of China[91859119] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81771924] ; National Key Research and Development Program of China[2017YFC1308700] ; National Key Research and Development Program of China[2017YFA0205200] ; National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences[2018PT32003] ; National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences[2019PT320008] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; Natural Science Foundation of Beijing Municipality[7192176] ; Central University Basic Scientific Research Business Expenses Special Funds[3332018022]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000500863800001
出版者WILEY
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Natural Science Foundation of Beijing Municipality ; Central University Basic Scientific Research Business Expenses Special Funds
源URL[http://ir.ia.ac.cn/handle/173211/29351]  
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Sun, Hao; Tian, Jie; Jin, Zhengyu
作者单位1.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
2.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
3.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Radiol, 1 Shuaifuyuan, Beijing 100730, Peoples R China
4.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Chinese Acad Med Sci, Dept Urol, Peking Union Med Coll Hosp, Beijing, Peoples R China
7.Chinese Acad Med Sci, Dept Pathol, Peking Union Med Coll Hosp, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Qi, Yafei,Zhang, Shuaitong,Wei, Jingwei,et al. Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2019:10.
APA Qi, Yafei.,Zhang, Shuaitong.,Wei, Jingwei.,Zhang, Gumuyang.,Lei, Jing.,...&Jin, Zhengyu.(2019).Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies.JOURNAL OF MAGNETIC RESONANCE IMAGING,10.
MLA Qi, Yafei,et al."Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies".JOURNAL OF MAGNETIC RESONANCE IMAGING (2019):10.

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来源：自动化研究所

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