Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108.

Article  PubMed  Google Scholar 

Talab SS, Preston MA, Elmi A, Tabatabaei S. Prostate cancer imaging. Radiol Clin North Am. 2012;50(6):1015–41.

Article  PubMed  Google Scholar 

Johnson LM, Turkbey B, Figg WD, Choyke PL. Multiparametric MRI in prostate cancer management. Nat Rev Clin Oncol. 2014;11(6):346–53.

Article  PubMed  PubMed Central  Google Scholar 

Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al. Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol. 2016;69:41–9.

Article  PubMed  Google Scholar 

Milonas D, Venclovas Z, Muilwijk T, Jievaltas M, Joniau S. External validation of Memorial Sloan Kettering Cancer Center nomogram and prediction of optimal candidate for lymph node dissection in clinically localized prostate cancer. Cent Eur J Urol. 2020;73(1):19–25.

Google Scholar 

Gandaglia G, Ploussard G, Valerio M, Mattei A, Fiori C, Fossati N, et al. A novel nomogram to identify candidates for extended pelvic lymph node dissection among patients with clinically localized prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies. Eur Urol. 2019;75(3):506–14.

Article  PubMed  Google Scholar 

Mehralivand S, Shih JH, Harmon S, Smith C, Bloom J, Czarniecki M, et al. A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI. Radiology. 2019;290(3):709–19.

Article  PubMed  Google Scholar 

Shieh AC, Guler E, Ojili V, Paspulati RM, Elliott R, Ramaiya NH, et al. Extraprostatic extension in prostate cancer: primer for radiologists. Abdom Radiol. 2020;45(12):4040–51.

Article  Google Scholar 

Costa DN, Passoni NM, Leyendecker JR, de Leon AD, Lotan Y, Roehrborn CG, et al. Diagnostic utility of a likert scale versus qualitative descriptors and length of capsular contact for determining extraprostatic tumor extension at multiparametric prostate MRI. AJR Am J Roentgenol. 2018;210(5):1066–72.

Article  PubMed  Google Scholar 

Park KJ, Kim M, hyun, Kim JK. Extraprostatic tumor extension: comparison of preoperative multiparametric MRI criteria and histopathologic correlation after radical prostatectomy. Radiology. 2020;296(1):87–95.

Article  PubMed  Google Scholar 

Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. The Lancet. 2017;389(10071):815–22.

Article  Google Scholar 

Choyke PL. Quantitative MRI or machine learning for prostate MRI: which should you use? Radiology. 2018;289:138–9.

Article  PubMed  Google Scholar 

Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16(7):391–403.

Article  PubMed  Google Scholar 

Li L, Shiradkar R, Leo P, Algohary A, Fu P, Tirumani SH, et al. A novel imaging based nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI. EBioMedicine. 2021;63:103163.

Article  CAS  PubMed  Google Scholar 

Losnegård A, Reisæter LAR, Halvorsen OJ, Jurek J, Assmus J, Arnes JB, et al. Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients. Acta Radiol. 2020;61(11):1570–9.

Article  PubMed  Google Scholar 

Xu L, Zhang G, Zhao L, Mao L, Li X, Yan W, et al. Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer. Front Oncol. 2020;10:1–9.

Google Scholar 

Ma S, Xie H, Wang H, Yang J, Han C, Wang X, et al. Preoperative prediction of extracapsular extension: radiomics signature based on magnetic resonance imaging to stage prostate cancer. Mol Imaging Biol. 2020;22(3):711–21.

Article  CAS  PubMed  Google Scholar 

Stanzione A, Cuocolo R, Cocozza S, Romeo V, Persico F, Fusco F, et al. Detection of extraprostatic extension of cancer on biparametric MRI combining texture analysis and machine learning: preliminary results. Acad Radiol. 2019;26(10):1338–44.

Article  PubMed  Google Scholar 

Wang J, Wu CJ, Bao ML, Zhang J, Shi H, Bin, Zhang YD. Using support vector machine analysis to assess PartinMR: a new prediction model for organ-confined prostate cancer. J Magn Reson Imaging. 2018;48(2):499–506.

Article  PubMed  Google Scholar 

Min X, Li M, Dong D, Feng Z, Zhang P, Ke Z, 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.

Article  PubMed  Google Scholar 

Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8(1):53.

Article  PubMed  PubMed Central  Google Scholar 

Guerra A, Alves FC, Maes K, Joniau S, Cassis J, Maio R, et al. Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features. Cancer Imaging. 2022;22(1):74.

Article  PubMed  PubMed Central  Google Scholar 

Marcus DS, Olsen TR, Ramaratnam M, Buckner RL. The extensible neuroimaging archive toolkit. Neuroinformatics. 2007;5(1):11–33.

Article  PubMed  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

Article  PubMed  PubMed Central  Google Scholar 

Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, et al. PI-RADS prostate imaging-reporting and data system: 2015, version 2. Eur Urol. 2016;69(1):16–40.

Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.

Article  PubMed  PubMed Central  Google Scholar 

Doran SJ, Kumar S, Orton M, D’Arcy J, Kwaks F, O’Flynn E, et al. Real-world radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues. Cancer Imaging. 2021;21(1):37.

Article  PubMed  PubMed Central  Google Scholar 

Lundberg S, Lee SI. A unified approach to interpreting model predictions. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA; 2017. p. 1–10.

Bai H, Xia W, Ji X, He D, Zhao X, Bao J, et al. Multiparametric magnetic resonance imaging-based peritumoral radiomics for preoperative prediction of the presence of extracapsular extension with prostate cancer. J Magn Reson Imaging. 2021;54(4):1222–30.

Article  PubMed  Google Scholar 

He D, Wang X, Fu C, Wei X, Bao J, Ji X, et al. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging. 2021;21(1):46.

Article  PubMed  PubMed Central  Google Scholar 

Fan X, Xie N, Chen J, Li T, Cao R, Yu H, et al. Multiparametric MRI and machine learning-based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer. Front Oncol. 2022;12:1–12.

Google Scholar 

Cuocolo R, Stanzione A, Faletti R, Gatti M, Calleris G, Fornari A, et al. MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol. 2021;31(10):7575–83.

Article  PubMed  PubMed Central  Google Scholar