Share：
Share this content in WeChat
X
[Chinese][PDF]116289
• Review •
Advances in clinical application of radiomics in prostate cancer
ZHANG Han  HUANG Cheng  WANG Bin 

Cite this article as: Zhang H, Huang C, Wang B. Advances in clinical application of radiomics in prostate cancer. Chin J Magn Reson Imaging, 2020, 11(11): 1063-1066. DOI:10.12015/issn.1674-8034.2020.11.025.


[Abstract] Radiomics extract digital features from medical images using computer algorithm. Combining with machine learning method, prediction model was constructed and then assistant diagnosis and treatment. Currently, radiomics is supplied to the diagnosis and treatment of prostate cancer. In this article, I will make a review of the advances in clinical application of radiomics in prostate cancer.
[Keywords] radiomics；prostatic neoplasms；clinical application；diagnosis；treatment

ZHANG Han Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264100, China

HUANG Cheng Department of Radiology, Zhifu Branch of Yantai Yuhuangding Hospital (Yantai Zhifu Hospital), Yantai 264000, China

WANG Bin* Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264100, China

*Correspondence to: Wang B, E-mail: binwang001@aliyun.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by Natural Science Foundation of Shandong Province of China No. ZR2016HL40 Shandong Key R & D Plan No. 2017GSF18121
Received  2020-04-02
Accepted  2020-07-25
DOI: 10.12015/issn.1674-8034.2020.11.025
Cite this article as: Zhang H, Huang C, Wang B. Advances in clinical application of radiomics in prostate cancer. Chin J Magn Reson Imaging, 2020, 11(11): 1063-1066. DOI:10.12015/issn.1674-8034.2020.11.025.

[1]
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492
[2]
章绪辉，梁文，李欣明，等. DCE-MRI定量参数对早期前列腺癌的应用研究．磁共振成像, 2018, 9(8): 607-611. DOI: 10.12015/issn.1674-8034.2018.08.009
[3]
王滨．前列腺的影像学检查应进一步重视．医学影像学杂志, 2007, 17(10): 1011-1012. DOI: 10.3969/j.issn.1006-9011.2007.10.001
[4]
王倩，房俊芳，王滨．磁共振扩散张量成像诊断前列腺癌的理论基础．磁共振成像, 2015, 6(1):76-80. DOI: 10.3969/j.issn.1674-8034.2015.01.20
[5]
Barentsz JO, Richenberg J, Clements R, et al. ESUR prostate MR guidelines 2012. Eur Radiol, 2012, 22(4): 746-757. DOI: 10.1007/s00330-011-2377-y
[6]
Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS prostate imaging-reporting and data system: 2015, version 2. Eur Urol, 2016, 69(1): 16-40. DOI: 10.1016/j.eururo.2015.08.052
[7]
Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol, 2019, 76(3): 340-351. DOI: 10.1016/j.eururo.2019.02.033
[8]
彭峰河，沈秀芝，蔡建新，等．基于PI-RADS V2多参数MRI对移行带前列腺癌的诊断价值．磁共振成像, 2018, 9(12): 922-927. DOI: 10.12015/issn.1674-8034.2018.12.008
[9]
李鹏，黄云海，张进，等. PI-RADS V2在3.0 T MR前列腺中央腺体癌诊断中的应用价值．磁共振成像, 2018, 9(10): 773-777. DOI: 10.12015/issn.1674-8034.2018.10.011
[10]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036
[11]
Kumar V, Gu YH, Basu B, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248. DOI: 10.1016/j.mri.2012.06.010
[12]
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169
[13]
Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev, 2016, 1(2): 207-226. DOI: 10.1080/23808993.2016.1164013
[14]
Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol, 2016, 61(13): R150-166. DOI: 10.1088/0031-9155/61/13/r150
[15]
吴佩琪，刘再毅，何兰，等．影像组学与大数据结合的研究现状．中华放射学杂志, 2017, 51(7): 554-558. DOI: 10.3760/cma.j.issn.1005-1201.2017.07.023
[16]
Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164. DOI: 10.1200/jco.2015.65.9128
[17]
Min XD, Li M, Dong D, et al. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method. Eur J Radiol, 2019, 115: 16-21. DOI: 10.1016/j.ejrad.2019.03.010
[18]
Hectors SJ, Cherny M, Yadav KK, et al. Radiomics features measured with multiparametric magnetic resonance imaging predict prostate cancer aggressiveness. J Urol, 2019, 202(3): 498-505. DOI: 10.1097/ju.0000000000000272
[19]
Xu M, Fang MJ, Zou J, et al. Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions. Eur J Radiol, 2019, 114: 38-44. DOI: 10.1016/j.ejrad.2019.02.032
[20]
Chen T, Li MJ, Gu YF, et al. Prostate cancer differentiation and aggressiveness: assessment with a radiomic-based model vs. PI-RADS v2. J Magn Reson Imaging, 2019, 49(3): 875-884. DOI: 10.1002/jmri.26243
[21]
Bonekamp D, Kohl S, Wiesenfarth M, et al. Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology, 2018, 289(1): 128-137. DOI: 10.1148/radiol.2018173064
[22]
黄乐生，黎晋宇，李深福，等．扩散加权成像与超声对血清PSA<10.0 μg/L前列腺癌的诊断意义．海南医学, 2016, 27(5): 741-744. DOI: 10.3969/j.issn.1003-6350.2016.05.019
[23]
Qi YF, Zhang ST, Wei JW, et al. Multiparametric MRI-based radiomics for prostate cancer screening with PSA in 4-10 ng/mL to reduce unnecessary biopsies. J Magn Reson Imaging, 2020, 51(6): 1890-1899. DOI: 10.1002/jmri.27008
[24]
Humphrey PA. Gleason grading and prognostic factors in carcinoma of the prostate. Mod Pathol, 2004, 17(3): 292-306. DOI: 10.1038/modpathol.3800054
[25]
Mohler JL, Antonarakis ES, Armstrong AJ, et al. Prostate cancer, version 2.2019, NCCN clinical practice guidelines in oncology. J Nat Compr Canc Netw, 2019, 17(5): 479-505. DOI: 10.6004/jnccn.2019.0023
[26]
Mehralivand S, Shih JH, Rais-Bahrami S, et al. A magnetic resonance imaging-based prediction model for prostate biopsy risk stratification. JAMA oncol, 2018, 4(5): 678-685. DOI: 10.1001/jamaoncol.2017.5667
[27]
Hausmann D, Aksöz N, von Hardenberg J, et al. Prostate cancer detection among readers with different degree of experience using ultra-high b-value diffusion-weighted Imaging: is a non-contrast protocol sufficient to detect significant cancer? Eur Radiol, 2018, 28(2): 869-876. DOI: 10.1007/s00330-017-5004-8
[28]
Liu B, Cheng J, Guo DJ, et al. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI. Clin Radiol, 2019, 74(11): 896.e1-896.e8. DOI: 10.1016/j.crad.2019.07.011
[29]
Toivonen J, Perez TM, Movahedi P, et al. Radiomics and machine learning of multisequence multiparametric prostate MRI: towards improved non-invasive prostate cancer characterization. PLoS One, 2019, 14(7): e0217702. DOI: 10.1371/journal.pone.0217702
[30]
Lee SL, Ravi A, Morton G, et al. Changes in ADC and T2-weighted MRI-derived radiomic features in patients treated with focal salvage HDR prostate brachytherapy for local recurrence after previous external-beam radiotherapy. Brachytherapy, 2019, 18(5): 567-573. DOI: 10.1016/j.brachy.2019.04.006
[31]
Abdollahi H, Mofid B, Shiri I, et al. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer. Radiol Med, 2019, 124(6): 555-567. DOI: 10.1007/s11547-018-0966-4
[32]
Shiradkar R, Podder TK, Algohary A, et al. Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI. Radiat Oncol, 2016, 11(1): 148. DOI: 10.1186/s13014-016-0718-3
[33]
Abdollahi H, Mahdavi SR, Shiri I, et al. Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy. J Cancer Res Ther, 2019, 15(Suppl): S11-S19. DOI: 10.4103/jcrt.JCRT_172_18
[34]
Gnep K, Fargeas A, Gutiérrez-Carvajal RE, et al. Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging, 2017, 45(1): 103-117. DOI: 10.1002/jmri.25335
[35]
Shiradkar R, Ghose S, Jambor I, et al. Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging, 2018, 48(6): 1626-1636. DOI: 10.1002/jmri.26178
[36]
Fischer S, Tahoun M, Klaan B, et al. A radiogenomic approach for decoding molecular mechanisms underlying tumor progression in prostate cancer. Cancers (Basel), 2019, 11(9): 1293. DOI: 10.3390/cancers11091293
[37]
Sun Y, Williams S, Byrne D, et al. Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer: a pilot study. Br J Radiol, 2019, 92(1104): 20190373. DOI: 10.1259/bjr.20190373

PREV Progress in the application of multimodal imaging technique in hepatic cystic hydatidosis
NEXT Detection, stratification and differentiation of prostate cancer by intravoxel incoherent motion imaging
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn