MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis

Tollefson MK, Karnes RJ, Rangel LJ, Bergstralh EJ, Boorjian SA (2013) The impact of clinical stage on prostate cancer survival following radical prostatectomy. J Urol 189:1707–1712. https://doi.org/10.1016/j.juro.2012.11.065

Article  PubMed  Google Scholar 

Mikel Hubanks J, Boorjian SA, Frank I, Gettman MT, Houston Thompson R, Rangel LJ et al (2014) The presence of extracapsular extension is associated with an increased risk of death from prostate cancer after radical prostatectomy for patients with seminal vesicle invasion and negative lymph nodes. Urol Oncol 32(26):e1-7. https://doi.org/10.1016/j.urolonc.2012.09.002

Article  Google Scholar 

Ohori M, Kattan MW, Koh H, Maru N, Slawin KM, Shariat S et al (2004) Predicting the presence and side of extracapsular extension: a nomogram for staging prostate cancer. J Urol 171:1844–1849. https://doi.org/10.1097/01.ju.0000121693.05077.3d

Article  PubMed  Google Scholar 

Eifler JB, Feng Z, Lin BM, Partin MT, Humphreys EB, Han M et al (2013) An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int 111:22–29. https://doi.org/10.1111/j.1464-410X.2012.11324.x

Article  PubMed  Google Scholar 

Rayn KN, Bloom JB, Gold SA, Hale GR, Baiocco JA, Mehralivand S et al (2018) Added value of multiparametric magnetic resonance imaging to clinical nomograms in predicting adverse pathology in prostate cancer. J Urol. https://doi.org/10.1016/j.juro.2018.05.094

Article  PubMed  PubMed Central  Google Scholar 

Turkbey B, Brown AM, Sankineni S, Wood BJ, Pinto PA, Choyke PL (2016) Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer. CA Cancer J Clin 66:326–336. https://doi.org/10.3322/caac.21333

Article  PubMed  Google Scholar 

Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G et al (2012) ESUR prostate MR guidelines 2012. Eur Radiol 22:746–757. https://doi.org/10.1007/s00330-011-2377-y

Article  PubMed  PubMed Central  Google Scholar 

Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ et al (2016) PI-RADS prostate imaging–reporting and data system: 2015, version 2. Eur Urol 69:16–40

Article  PubMed  Google Scholar 

Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Reporting PI, Version DS et al (2019) Update of prostate imaging reporting and data system version 2. Eur Urol 2019(76):340–351. https://doi.org/10.1016/j.eururo.2019.02.033

Article  Google Scholar 

Li W, Dong A, Hong G, Shang W, Shen X (2021) Diagnostic performance of ESUR scoring system for extraprostatic prostate cancer extension: a meta-analysis. Eur J Radiol 143:109896. https://doi.org/10.1016/j.ejrad.2021.109896

Article  PubMed  Google Scholar 

Li W, Shang W, Feng L, Sun Y, Tian J, Yiman W, Dong A (2022) Diagnostic performance of extraprostatic extension grading system for detection of extraprostatic extension in prostate cancer: a diagnostic systematic review and meta-analysis. Front Oncol. https://doi.org/10.3389/fonc.2021.792120

Article  PubMed  PubMed Central  Google Scholar 

Mehralivand S, Shih JH, Harmon S, Smith C, Bloom J, Czarniecki M et al (2019) A Grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI. Radiology 290:709–719. https://doi.org/10.1148/radiol.2018181278

Article  PubMed  Google Scholar 

Calimano-Ramirez LF, Virarkar MK, Hernandez M, Ozdemir S, Kumar S, Gopireddy DR et al (2023) MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdom Radiol 48:2379–2400. https://doi.org/10.1007/s00261-023-03924-y

Article  Google Scholar 

Chiacchio G, Castellani D, Nedbal C, De Stefano V, Brocca C, Tramanzoli P, Galosi AB, Donalisio R, da Silva J, Teoh Y-C, Tiong HY, Naik N, Somani BK, Merseburger AS, Gauhar V (2023) Radiomics vs radiologist in prostate cancer. Results from a systematic review. World J Urol 41(3):709–724. https://doi.org/10.1007/s00345-023-04305-2

Article  PubMed  Google Scholar 

Cutaia G, La Tona G, Comelli A, Vernuccio F, Agnello F, Gagliardo C et al (2021) Radiomics and prostate MRI: current role and future applications. J Imaging 7:34. https://doi.org/10.3390/jimaging7020034

Article  PubMed  PubMed Central  Google Scholar 

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Epidemiol Biostat Public Health 6:e1-34

Google Scholar 

Whiting PF (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529. https://doi.org/10.7326/0003-4819-155-8-201110180-00009

Article  PubMed  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141

Article  PubMed  Google Scholar 

Rutter CM, Gatsonis CA (2001) A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 20:2865–2884

Article  CAS  PubMed  Google Scholar 

Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD et al (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343:889–893

Article  Google Scholar 

Bai H, Xia W, Ji X, He D, Zhao X, Bao J, Zhou J, Wei X, Huang Y, Li Q, Gao X (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. https://doi.org/10.1002/jmri.27678

Article  PubMed  Google Scholar 

Cuocolo R, Stanzione A, Faletti R, Gatti M, Calleris G, Fornari A et al (2021) MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol. https://doi.org/10.1007/s00330-021-07856-3

Article  PubMed  PubMed Central  Google Scholar 

Damascelli A, Gallivanone F, Cristel G, Cava C, Interlenghi M, Esposito A et al (2021) Advanced imaging analysis in prostate MRI: building a radiomic signature to predict tumor aggressiveness. Diagn Basel Switz 11:594. https://doi.org/10.3390/diagnostics11040594

Article  Google Scholar 

Fan X, Xie N, Chen J, Li T, Cao R, Yu H et al (2022) Multiparametric MRI and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer. Front Oncol 12:839621. https://doi.org/10.3389/fonc.2022.839621

Article  PubMed  PubMed Central  Google Scholar 

He D, Wang X, Fu C, Wei X, Bao J, Ji X et al (2021) MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging Off Publ Int Cancer Imaging Soc 21:46. https://doi.org/10.1186/s40644-021-00414-6

Article  Google Scholar 

Losnegård A, Reisæter LAR, Halvorsen OJ, Jurek J, Assmus J, Arnes JB et al (2020) Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients. Acta Radiol 61:1570–1579. https://doi.org/10.1177/0284185120905066

Article  PubMed  Google Scholar 

Ma S, Xie H, Wang H, Han C, Yang J, Lin Z et al (2019) MRI-based radiomics signature for the preoperative prediction of extracapsular extension of prostate cancer. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26777

Article  PubMed  PubMed Central  Google Scholar 

Ma S, Xie H, Wang H, Yang J, Han C, Wang X et al (2020) Preoperative prediction of extracapsular extension: radiomics signature based on magnetic resonance imaging to stage prostate cancer. Mol Imaging Biol 22:711–721. https://doi.org/10.1007/s11307-019-01405-7

Article  CAS  PubMed  Google Scholar 

Stanzione A, Cuocolo R, Cocozza S, Romeo V, Persico F, Fusco F et al (2019) Detection of extraprostatic extension of cancer on biparametric MRI combining texture analysis and machine learning: preliminary results. Acad Radiol 26:1338–1344. https://doi.org/10.1016/j.acra.2018.12.025

Article  PubMed  Google Scholar 

Xu L, Zhang G, Zhao L, Mao L, Li X, Yan W et al (2020) Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer. Front Oncol 10:940. https://doi.org/10.3389/fonc.2020.00940

Article  PubMed  PubMed Central  Google Scholar 

de Rooij M, Hamoen EHJ, Witjes JA, Barentsz JO, Rovers MM (2016) Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta-analysis. Eur Urol 70:233–245. https://doi.org/10.1016/j.eururo.2015.07.029

Article  PubMed  Google Scholar 

Kao Y-S, Lin K-T (2022) A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity. Radiol Med (Torino) 127:754–762. https://doi.org/10.1007/s11547-022-01510-8

Article  PubMed  Google Scholar 

Kozikowski M, Suarez-Ibarrola R, Osiecki R, Bilski K, Gratzke C, Shariat SF et al (2022) Role of radiomics in the prediction of muscle-invasive bladder cancer: a systematic review and meta-analysis. Eur Urol Focus 8:728–738. https://doi.org/10.1016/j.euf.2021.05.005

Article  PubMed  Google Scholar 

Li Y, Liu Y, Liang Y, Wei R, Zhang W, Yao W et al (2022) Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 32:8039–8051. https://doi.org/10.1007/s00330-022-08828-x

Article  PubMed  Google Scholar 

Nketiah G, Elschot M, Kim E, Teruel JR, Scheenen TW, Bathen TF et al (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 27:3050–3059. https://doi.org/10.1007/s00330-016-4663-1

Article  PubMed  Google Scholar 

Spohn SKB, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH 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 11:8027–8042. https://doi.org/10.7150/thno.61207

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