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Jian LU


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Journal of Zhejiang University SCIENCE B 2023 Vol.24 No.8 P.663-681


MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer

Author(s):  Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, Zenan LIU, Jide HE, Jiangang LIU, Hao PING, Jian LU

Affiliation(s):  Department of Urology, Peking University Third Hospital,Beijing100191,China; more

Corresponding email(s):   lujian@bjmu.edu.cn, pinghaotrh@ccmu.edu.cn

Key Words:  Magnetic resonance imaging (MRI), Radiomics, Prostate cancer, Predictive model

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Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, Zenan LIU, Jide HE, Jiangang LIU, Hao PING, Jian LU. 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.

@article{title="MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer",
author="Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, Zenan LIU, Jide HE, Jiangang LIU, Hao PING, Jian LU",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
%A Xuehua ZHU
%A Lizhi SHAO
%A Zhenyu LIU
%A Zenan LIU
%A Jide HE
%A Jiangang LIU
%A Jian LU
%J Journal of Zhejiang University SCIENCE B
%V 24
%N 8
%P 663-681
%@ 1673-1581
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2200619

T1 - MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
A1 - Xuehua ZHU
A1 - Lizhi SHAO
A1 - Zhenyu LIU
A1 - Zenan LIU
A1 - Jide HE
A1 - Jiangang LIU
A1 - Hao PING
A1 - Jian LU
J0 - Journal of Zhejiang University Science B
VL - 24
IS - 8
SP - 663
EP - 681
%@ 1673-1581
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2200619

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]AbdollahiH,MahdaviSR,MofidB,et al.,2018.Rectal wall MRI radiomics in prostate cancer patients: prediction of and correlation with early rectal toxicity.Int J Radiat Biol,94(9):829-837.

[2]AcharyaUR,HagiwaraY,SudarshanVK,et al.,2018.Towards precision medicine: from quantitative imaging to radiomics.J Zhejiang Univ-Sci B (Biomed & Biotechnol),19(1):6-24.

[3]Bagher-EbadianH,JanicB,LiuC,et al.,2019.Detection of dominant intra-prostatic lesions in patients with prostate cancer using an artificial neural network and MR multi-modal radiomics analysis.Front Oncol,9:1313.

[4]BaiHL,XiaW,JiXF,et al.,2021.Multiparametric magnetic resonance imaging-based peritumoral radiomics for preoperative prediction of the presence of extracapsular extension with prostate cancer.J Magn Reson Imaging,54(4):1222-1230.

[5]BerryB,ParryMG,SujenthiranA,et al.,2020.Comparison of complications after transrectal and transperineal prostate biopsy: a national population-based study.BJU Int,126(1):97-103.

[6]BiWL,HosnyA,SchabathMB,et al.,2019.Artificial intelligence in cancer imaging: clinical challenges and applications.CA Cancer J Clin,69(2):127-157.

[7]BlekerJ,KweeTC,DierckxRAJO,et al.,2020.Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer.Eur Radiol,30(3):1313-1324.

[8]BoehmK,LarcherA,BeyerB,et al.,2016.Identifying the most informative prediction tool for cancer-specific mortality after radical prostatectomy: comparative analysis of three commonly used preoperative prediction models.Eur Urol,69(6):1038-1043.

[9]BoevéLMS,HulshofMCCM,VisAN,et al.,2019.Effect on survival of androgen deprivation therapy alone compared to androgen deprivation therapy combined with concurrent radiation therapy to the prostate in patients with primary bone metastatic prostate cancer in a prospective randomised clinical trial: data from the HORRAD trial.Eur Urol,75(3):410-418.

[10]BourbonneV,VallièresM,LuciaF,et al.,2019.MRI-derived radiomics to guide post-operative management for high-risk prostate cancer.Front Oncol,9:807.

[11]BourbonneV,FournierG,VallièresM,et al.,2020.External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer.Cancers,12(4):814.

[12]BourbonneV,JaouenV,NguyenTA,et al.,2021.Development of a radiomic-based model predicting lymph node involvement in prostate cancer patients.Cancers,13(22):5672.

[13]BrajtbordJS,LeapmanMS,CooperbergMR,2017.The CAPRA score at 10 years: contemporary perspectives and analysis of supporting studies.Eur Urol,71(5):‍705-709.

[14]BrancatoV,AielloM,BassoL,et al.,2021.Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions.Sci Rep,11:643.

[15]BruneseL,MercaldoF,ReginelliA,et al.,2020.Radiomics for gleason score detection through deep learning.Sensors,20(18):5411.

[16]CampbellJM,RaymondE,O'CallaghanME,et al.,2017a.Optimum tools for predicting clinical outcomes in prostate cancer patients undergoing radical prostatectomy: a systematic review of prognostic accuracy and validity.Clin Genitourin Cancer,15(5):e827-e834.

[17]CampbellJM,O'CallaghanME,RaymondE,et al.,2017b.Tools for predicting clinical and patient-reported outcomes in prostate cancer patients undergoing androgen deprivation therapy: a systematic review of prognostic accuracy and validity.Clin Genitourin Cancer,15(6):‍629-634.e8.

[18]CastilloTJM,StarmansMPA,ArifM,et al.,2021.A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: high grade vs. low grade.Diagnostics,11(2):369.

[19]ChaddadA,KucharczykMJ,NiaziT,2018.Multimodal radiomic features for the predicting gleason score of prostate cancer.Cancers,10(8):249.

[20]ChenT,LiMJ,GuYF,et al.,2019.Prostate cancer differentiation and aggressiveness: assessment with a radiomic-based model vs. PI-RADS v2.J Magn Reson Imaging,49(3):875-884.

[21]ChenT,ZhangZY,TanSX,et al.,2022.MRI based radiomics compared with the PI-RADS v2.1 in the prediction of clinically significant prostate cancer: biparametric vs multiparametric MRI.Front Oncol,11:792456.

[22]CooperbergMR,PastaDJ,ElkinEP,et al.,2005.The University of California, San Francisco Cancer of the Prostate Risk Assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy.J Urol,173(6):1938-1942.

[23]CooperbergMR,HinotsuS,NamikiM,et al.,2009.Risk assessment among prostate cancer patients receiving primary androgen deprivation therapy.J Clin Oncol,27(26):4306-4313.

[24]CostaDN,2021.Multiparametric MRI of the prostate: beyond cancer detection and staging.Radiology,299(3):624-625.

[25]CuocoloR,StanzioneA,FalettiR,et al.,2021.MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study.Eur Radiol,31(10):7575-7583.

[26]D'AmicoAV,WhittingtonR,MalkowiczSB,et al.,1998.Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer.JAMA,280(11):969-974.

[27]FehrD,VeeraraghavanH,WibmerA,et al.,2015.Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.Proc Natl Acad Sci USA,112(46):E6265-E6273.

[28]FerroM,de CobelliO,VartolomeiMD,et al.,2021.Prostate cancer radiogenomics—from imaging to molecular characterization.Int J Mol Sci,22(18):9971.

[29]FrenchWW,WallenEM,2020.Advances in the diagnostic options for prostate cancer.Postgrad Med,132(S4):52-62.

[30]GnepK,FargeasA,Gutiérrez-CarvajalRE,et al.,2017.Haralick textural features onT2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.J Magn Reson Imaging,45(1):103-117.

[31]GongLX,XuM,FangMJ,et al.,2020.Noninvasive prediction of high-grade prostate cancer via biparametric MRI radiomics.J Magn Reson Imaging,52(4):1102-1109.

[32]GongLX,XuM,FangMJ,et al.,2022.The potential of prostate gland radiomic features in identifying the Gleason score.Comput Biol Med,144:105318.

[33]HanC,MaS,LiuX,et al.,2021.Radiomics models based on apparent diffusion coefficient maps for the prediction of high-grade prostate cancer at radical prostatectomy: comparison with preoperative biopsy.J Magn Reson Imaging,54(6):1892-1901.

[34]HectorsSJ,ChenC,ChenJ,et al.,2021.Magnetic resonance imaging radiomics-based machine learning prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions.J Magn Reson Imaging,54(5):1466-1473.

[35]HinevAI,AnakievskiD,KolevN,et al.,2011.Validation of pre- and postoperative nomograms used to predict the pathological stage and prostate cancer recurrence after radical prostatectomy: a multi-institutional study.J BUON,16(2):316-322.

[36]HouY,BaoML,WuCJ,et al.,2020.A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions.Abdom Radiol,45(12):4223-4234.

[37]HouY,BaoJ,SongY,et al.,2021.Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer.eBioMedicine,68:103395.

[38]HuL,ZhouDW,FuCX,et al.,2021.Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study.Eur Radiol,31(3):1760-1769.

[39]HuMB,YangT,HuJM,et al.,2018.Prognostic factors in Chinese patients with prostate cancer receiving primary androgen deprivation therapy: validation of Japan Cancer of the Prostate Risk Assessment (J-CAPRA) score and impacts of pre-existing obesity and diabetes mellitus.Int J Clin Oncol,23(3):591-598.

[40]HuetingTA,CornelEB,SomfordDM,et al.,2018.External validation of models predicting the probability of lymph node involvement in prostate cancer patients.Eur Urol Oncol,1(5):411-417.

[41]IsbarnH,KarakiewiczPI,WalzJ,et al.,2010.External validation of a preoperative nomogram for prediction of the risk of recurrence after radical prostatectomy.Int J Radiat Oncol Biol Phys,77(3):788-792.

[42]JohnsonLM,TurkbeyB,FiggWD,et al.,2014.Multiparametric MRI in prostate cancer management.Nat Rev Clin Oncol,11(6):346-353.

[43]KalantarR,LinG,WinfieldJM,et al.,2021.Automatic segmentation of pelvic cancers using deep learning: state-of-the-art approaches and challenges.Diagnostics,11(11):1964.

[44]KattanMW,EasthamJA,StapletonAMF,et al.,1998.A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer.J Natl Cancer Inst,90(10):766-771.

[45]KrishnaS,LimCS,McInnesMDF,et al.,2018.Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer.J Magn Reson Imaging,47(1):176-185.

[46]KucharczykM,2021.Can MRI of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour (MRI-PREDICT). ClinicalTrials.gov.https://clinicaltrials.gov/ct2/show/record/NCT05024162

[47]LambinP,LeijenaarRTH,DeistTM,et al.,2017.Radiomics: the bridge between medical imaging and personalized medicine.Nat Rev Clin Oncol,14(12):749-762.

[48]LeeCH,TanTW,TanCH,2021.Multiparametric MRI in active surveillance of prostate cancer: an overview and a practical approach.Korean J Radiol,22(7):1087-1099.

[49]LiL,ShiradkarR,LeoP,et al.,2021.A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI.eBioMedicine,63:103163.

[50]LiMJ,ChenT,ZhaoWL,et al.,2020.Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.Quant Imaging Med Surg,10(2):368-379.

[51]LiTP,SunLN,LiQH,et al.,2022.Development and validation of a radiomics nomogram for predicting clinically significant prostate cancer in PI-RADS 3 lesions.Front Oncol,11:825429.

[52]LimkinEJ,SunR,DercleL,et al.,2017.Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.Ann Oncol,28(6):‍1191-1206.

[53]LitwinMS,TanHJ,2017.The diagnosis and treatment of prostate cancer: a review.JAMA,317(24):2532-2542.

[54]LiuLF,YiXP,LuC,et al.,2020.Applications of radiomics in genitourinary tumors.Am J Cancer Res,10(8):‍2293-2308.

[55]LosnegårdA,ReisæterLAR,HalvorsenOJ,et al.,2020.Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients.Acta Radiol,61(11):1570-1579.

[56]LughezzaniG,BudäusL,IsbarnH,et al.,2010.Head-to-head comparison of the three most commonly used preoperative models for prediction of biochemical recurrence after radical prostatectomy.Eur Urol,57(4):‍562-568.

[57]MaS,XieHH,WangHH,et al.,2019.MRI-based radiomics signature for the preoperative prediction of extracapsular extension of prostate cancer.J Magn Reson Imaging,50(6):1914-1925.

[58]MayerhoeferME,MaterkaA,LangsG,et al.,2020.Introduction to radiomics.J Nucl Med,61(4):488-495.

[59]MeijerD,van LeeuwenPJ,RobertsMJ,et al.,2021.External validation and addition of prostate-specific membrane antigen positron emission tomography to the most frequently used nomograms for the prediction of pelvic lymph-node metastases: an international multicenter study.Eur Urol,80(2):234-242.

[60]MerrielSWD,PocockL,GilbertE,et al.,2022.Systematic review and meta-analysis of the diagnostic accuracy of prostate-specific antigen (PSA) for the detection of prostate cancer in symptomatic patients.BMC Med,20:54.

[61]MidiriF,VernuccioF,PurpuraP,et al.,2021.Multiparametric MRI and radiomics in prostate cancer: a review of the current literature.Diagnostics,11(10):1829.

[62]MinXD,LiM,DongD,et al.,2019.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,115:16-21.

[63]MoreiraDM,JayachandranJ,PrestiJCJr,et al.,2009.Validation of a nomogram to predict disease progression following salvage radiotherapy after radical prostatectomy: results from the SEARCH database.BJU Int,104(10):1452-1456.

[64]NesbittAL,KapoorJ,PiesseC,et al.,2019.Prediction of pathological stage at radical prostatectomy: do commonly used prostate cancer nomograms apply to men from Far North Queensland?ANZ J Surg,89(1-2):111-114.

[65]OndracekRP,KattanMW,MurekeyisoniC,et al.,2016.Validation of the Kattan nomogram for prostate cancer recurrence after radical prostatectomy.J Natl Compr Cancer Netw,14(11):1395-1401.

[66]ParraNA,LuH,ChoiJ,et al.,2019.Habitats in DCE-MRI to predict clinically significant prostate cancers.Tomography,5(1):68-76.

[67]PuechP,Sufana-IancuA,RenardB,et al.,2013.Prostate MRI: can we do without DCE sequences in 2013?Diagn Interv Imaging,94(12):1299-1311.

[68]PunnenS,FreedlandSJ,PrestiJCJr,et al.,2014.Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy.Eur Urol,65(6):1171-1177.

[69]QiYF,ZhangST,WeiJW,et al.,2020.Multiparametric MRI-based radiomics for prostate cancer screening with PSA in 4‍–‍10 ng/mL to reduce unnecessary biopsies. J Magn Reson Imaging,51(6):1890-1899.

[70]RoupretM,HupertanV,ComperatE,et al.,2009.Cross-cultural validation of a prognostic tool: example of the limitations of the Kattan preoperative nomogram as a predictor of prostate cancer recurrence after radical prostatectomy.J Urol,181(Suppl4):718.

[71]ShaoLZ,YanY,LiuZY,et al.,2020.Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.Theranostics,10(22):10200-10212.

[72]ShiotaM,YokomizoA,TakeuchiA,et al.,2015.The oncological outcome and validation of Japan Cancer of the Prostate Risk Assessment score among men treated with primary androgen-deprivation therapy.J Cancer Res Clin Oncol,141(3):495-503.

[73]ShiradkarR,GhoseS,JamborI,et al.,2018.Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings.J Magn Reson Imaging,48(6):1626-1636.

[74]SiddiquiMM,Rais-BahramiS,TurkbeyB,et al.,2015.Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.JAMA,313(4):390-397.

[75]SiegelRL,MillerKD,FuchsHE,et al.,2022.Cancer statistics, 2022.CA Cancer J Clin,72(1):7-33.

[76]SoeterikTFW,HuetingTA,IsraelB,et al.,2021.External validation of the Memorial Sloan Kettering Cancer Centre and Briganti nomograms for the prediction of lymph node involvement of prostate cancer using clinical stage assessed by magnetic resonance imaging.BJU Int,128(2):236-243.

[77]SpohnSKB,BettermannAS,BambergF,et al.,2021.Radiomics in prostate cancer imaging for a personalized treatment approach ‍–‍ current aspects of methodology and a systematic review on validated studies. Theranostics,11(16):8027-8042.

[78]StanzioneA,CuocoloR,CocozzaS,et al.,2019.Detection of extraprostatic extension of cancer on biparametric MRI combining texture analysis and machine learning: preliminary results.Acad Radiol,26(10):1338-1344.

[79]StanzioneA,GambardellaM,CuocoloR,et al.,2020.Prostate MRI radiomics: a systematic review and radiomic quality score assessment.Eur J Radiol,129:109095.

[80]StephensonAJ,ScardinoPT,EasthamJA,et al.,2006.Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy.J Natl Cancer Inst,98(10):715-717.

[81]SunY,ReynoldsHM,ParameswaranB,et al.,2019.Multiparametric MRI and radiomics in prostate cancer: a review.Australas Phys Eng Sci Med,42(1):3-25.

[82]TamblynDJ,ChopraS,YuCH,et al.,2011.Comparative analysis of three risk assessment tools in Australian patients with prostate cancer.BJU Int,108(S2):51-56.

[83]ToMNN,VuDQ,TurkbeyB,et al.,2018.Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.Int J Comput Assist Radiol Surg,13(11):1687-1696.

[84]TurkbeyB,RosenkrantzAB,HaiderMA,et al.,2019.Prostate Imaging Reporting and Data System version 2.1: 2019 update of Prostate Imaging Reporting and Data System version 2.Eur Urol,76(3):340-351.

[85]WallisCJD,SaskinR,ChooR,et al.,2016.Surgery versus radiotherapy for clinically-localized prostate cancer: a systematic review and meta-analysis.Eur Urol,70(1):21-30.

[86]WangB,LeiY,TianSB,et al.,2019.Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.Med Phys,46(4):1707-1718.

[87]WangJ,WuCJ,BaoML,et al.,2017.Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.Eur Radiol,27(10):4082-4090.

[88]WangYR,YuB,ZhongF,et al.,2019.MRI-based texture analysis of the primary tumor for pre-treatment prediction of bone metastases in prostate cancer.Magn Reson Imaging,60:76-84.

[89]WoźnickiP,WesthoffN,HuberT,et al.,2020.Multiparametric MRI for prostate cancer characterization: combined use of radiomics model with PI-RADS and clinical parameters.Cancers,12(7):1767.

[90]WuLM,XuJR,YeYQ,et al.,2012.The clinical value of diffusion-weighted imaging in combination with T2-weighted imaging in diagnosing prostate carcinoma: a systematic review and meta-analysis.Am J Roentgenol,199(1):103-110.

[91]WuS,JiaoYN,ZhangYF,et al.,2019.Imaging-based individualized response prediction of carbon ion radiotherapy for prostate cancer patients.Cancer Manage Res,11:9121-9131.

[92]XylinasE,DachéA,RouprêtM,2010.Is radical prostatectomy a viable therapeutic option in clinically locally advanced (cT3) prostate cancer?BJU Int,106(11):1596-1600.

[93]YanY,ShaoLZ,LiuZY,et al.,2021.Deep learning with quantitative features of magnetic resonance images to predict biochemical recurrence of radical prostatectomy: a multi-center study.Cancers,13(12):3098.

[94]YonedaK,UtsumiT,SomotoT,et al.,2018.External validation of two web-based postoperative nomograms predicting the probability of early biochemical recurrence after radical prostatectomy: a retrospective cohort study.Jpn J Clin Oncol,48(2):195-199.

[95]ZelicR,GarmoH,ZugnaD,et al.,2020.Predicting prostate cancer death with different pretreatment risk stratification tools: a head-to-head comparison in a nationwide cohort study.Eur Urol,77(2):180-188.

[96]ZhangH,LiXL,ZhangYX,et al.,2021.Diagnostic nomogram based on intralesional and perilesional radiomics features and clinical factors of clinically significant prostate cancer.J Magn Reson Imaging,53(5):1550-1558.

[97]ZhangWJ,MaoN,WangYS,et al.,2020.A radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients.Eur J Radiol,128:109020.

[98]ZhangYS,ChenW,YueXJ,et al.,2020.Development of a novel, multi-parametric, MRI-based radiomic nomogram for differentiating between clinically significant and insignificant prostate cancer.Front Oncol,10:888.

[99]ZhongQZ,LongLH,LiuA,et al.,2020.Radiomics of multiparametric MRI to predict biochemical recurrence of localized prostate cancer after radiation therapy.Front Oncol,10:731.

[100]ZwanenburgA,VallièresM,AbdalahMA,et al.,2020.The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.Radiology,295(2):328-338.

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