MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
文献类型：期刊论文
| 作者 | Zhu, Xuehua4; Shao, Lizhi6; Liu, Zhenyu1,6 ; Liu, Zenan4; He, Jide4; Liu, Jiangang2,5; Ping, Hao3; Lu, Jian4
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| 刊名 | JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B
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| 出版日期 | 2023-08-01 |
| 卷号 | 24期号:8页码:663-681 |
| ISSN号 | 1673-1581 |
| 关键词 | Magnetic resonance imaging (MRI) Radiomics Prostate cancer Predictive model |
| DOI | 10.1631/jzus.B2200619 |
| 通讯作者 | Ping, Hao(pinghaotrh@ccmu.edu.cn) ; Lu, Jian(lujian@bjmu.edu.cn) |
| 英文摘要 | Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics. |
| WOS关键词 | RISK-ASSESSMENT SCORE ; RADICAL PROSTATECTOMY ; BIOCHEMICAL RECURRENCE ; CLINICALLY SIGNIFICANT ; PREOPERATIVE NOMOGRAM ; MULTIPARAMETRIC MRI ; EXTERNAL VALIDATION ; DISEASE RECURRENCE ; PATHOLOGICAL STAGE ; PREDICTION |
| 资助项目 | Beijing Natural Science Foundation[Z200027] ; Beijing Natural Science Foundation[L212051] ; Cohort Construction Project of Peking University Third Hospital[BYSYDL2021012] ; Medicine-X Project of Peking University Health Science Center[BMU2022MX014] ; National Natural Science Foundation of China[61871004] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Research & Experimental Medicine |
| 语种 | 英语 |
| 出版者 | ZHEJIANG UNIV PRESS |
| WOS记录号 | WOS:001049245800001 |
| 资助机构 | Beijing Natural Science Foundation ; Cohort Construction Project of Peking University Third Hospital ; Medicine-X Project of Peking University Health Science Center ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54044]  |
| 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 | Ping, Hao; Lu, Jian |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China 2.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China 3.Capital Med Univ, Beijing Tongren Hosp, Dept Urol, Beijing 100730, Peoples R China 4.Peking Univ Third Hosp, Dept Urol, Beijing 100191, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Xuehua,Shao, Lizhi,Liu, Zhenyu,et al. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer[J]. JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B,2023,24(8):663-681. |
| APA | Zhu, Xuehua.,Shao, Lizhi.,Liu, Zhenyu.,Liu, Zenan.,He, Jide.,...&Lu, Jian.(2023).MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B,24(8),663-681. |
| MLA | Zhu, Xuehua,et al."MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer".JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B 24.8(2023):663-681. |
入库方式： OAI收割
来源：自动化研究所
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