Rawla P (2019) Epidemiology of prostate cancer. World J Oncol 10(2):63

Article  PubMed  PubMed Central  CAS  Google Scholar 

Mattiuzzi C, Lippi G (2019) Current cancer epidemiology. J Epidemiol Glob Health 9(4):217–222

Article  PubMed  PubMed Central  Google Scholar 

Sung H et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249

PubMed  Google Scholar 

Simmons MN, Stephenson AJ, Klein EA (2007) Natural history of biochemical recurrence after radical prostatectomy: risk assessment for secondary therapy. Eur Urol 51(5):1175–1184

Article  PubMed  Google Scholar 

Moul JW et al (2024) Application of next-generation imaging in biochemically recurrent prostate cancer. Prostate Cancer Prostatic Dis 27(2):202–211

Article  PubMed  Google Scholar 

Simon NI et al (2022) Best Approaches and Updates for Prostate Cancer Biochemical Recurrence. Am Soc Clin Oncol Educ Book 42:1–8

PubMed  PubMed Central  Google Scholar 

Grossfeld GD et al (2003) Predicting recurrence after radical prostatectomy for patients with high risk prostate cancer. J Urol 169(1):157–163

Article  PubMed  Google Scholar 

Bejrananda T, Pliensiri P (2023) Prediction of biochemical recurrence after laparoscopic radical prostatectomy. BMC Urol 23(1):183

Article  PubMed  PubMed Central  Google Scholar 

Marin L, Casado F (2023) Prediction of prostate cancer biochemical recurrence by using discretization supports the critical contribution of the extra-cellular matrix genes. Sci Rep 13(1):10144

Article  PubMed  PubMed Central  CAS  Google Scholar 

Özkanli S et al (2014) Gleason score at the margin can predict biochemical recurrence after radical prostatectomy, in addition to preoperative PSA and surgical margin status. Turk J Med Sci 44(3):397–403

Article  PubMed  Google Scholar 

Lambin P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446

Article  PubMed  PubMed Central  Google Scholar 

Yang G et al (2024) Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics. Insights Imaging 15(1):88

Article  PubMed  PubMed Central  Google Scholar 

Lomer NB et al (2024) MRI-based radiomics for predicting prostate cancer grade groups: a systematic review and meta-analysis of diagnostic test accuracy studies. Acad Radiol. 32(6):3429–3452

Article  PubMed  Google Scholar 

Piran Nanekaran N et al (2024) Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: Utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information. Biomed Phys Eng Exp 10(6):065035

Article  Google Scholar 

Meng S et al (2022) Multiparametric MRI-based nomograms in predicting positive surgical margins of prostate cancer after laparoscopic radical prostatectomy. Front Oncol 12:973285

Article  PubMed  PubMed Central  Google Scholar 

Salimi M et al (2025) MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis. Abdom Radiol. https://doi.org/10.1007/s00261-025-04892-1

Article  Google Scholar 

Page, M.J., et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 2021. 372.

Kocak B et al (2024) METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 15(1):8

Article  PubMed  PubMed Central  Google Scholar 

Schwarzer G (2007) Meta: an R package for meta-analysis. R News 7(3):40–45

Google Scholar 

DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188

Article  PubMed  CAS  Google Scholar 

Rohatgi, A., WebPlotDigitizer. Version 4.0. Austin, Texas, USA. 2017.

Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta‐analysis. Stat Med 21(11):1539–1558

Article  PubMed  Google Scholar 

Wu XH et al (2024) Periprostatic fat magnetic resonance imaging based radiomics nomogram for predicting biochemical recurrence-free survival in patients with non-metastatic prostate cancer after radical prostatectomy. BMC Cancer 24(1):1459

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kang H et al (2020) Texture Analysis of F-18 Fluciclovine PET/CT to Predict Biochemically Recurrent Prostate Cancer: Initial Results. Tomography 6(3):301–307

Article  PubMed  PubMed Central  Google Scholar 

Hu C et al (2024) Development and validation of a multimodality model based on whole-slide imaging and biparametric MRI for predicting postoperative biochemical recurrence in prostate cancer. Radiol Imaging Cancer 6(3):e230143

Article  PubMed  PubMed Central  Google Scholar 

Li L 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

Article  PubMed  CAS  Google Scholar 

Bourbonne V et al (2020) External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers (Basel). https://doi.org/10.3390/cancers12040814

Article  PubMed  Google Scholar 

Hou Y et al (2023) Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence. Br J Cancer 129(10):1625–1633

Article  PubMed  PubMed Central  CAS  Google Scholar 

Li T et al (2024) Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy. Eur J Nucl Med Mol Imaging 51(9):2806–2818

Article  PubMed  Google Scholar 

An P et al (2023) Predicting Model of Biochemical Recurrence of Prostate Carcinoma (PCa-BCR) Using MR Perfusion-Weighted Imaging-Based Radiomics. Technol Cancer Res Treat 22:15330338231166766

Article  PubMed  PubMed Central  CAS  Google Scholar 

Lee HW et al (2023) Novel multiparametric magnetic resonance imaging-based deep learning and clinical parameter integration for the prediction of long-term biochemical recurrence-free survival in prostate cancer after radical prostatectomy. Cancers (Basel). https://doi.org/10.3390/cancers15133416

Article  PubMed  PubMed Central  Google Scholar 

Hu C et al (2024) The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy. Cancer Imaging 24(1):23

Article  PubMed  PubMed Central  Google Scholar 

Yan Y et al (2021) Deep learning with quantitative features of magnetic resonance images to predict biochemical recurrence of radical prostatectomy: a multi-center study. Cancers (Basel). https://doi.org/10.3390/cancers13123098

Article  PubMed  PubMed Central  Google Scholar 

Shiradkar R et al (2022) Prostate surface distension and tumor texture descriptors from pre-treatment MRI are associated with biochemical recurrence following radical prostatectomy: preliminary findings. Front Oncol 12:841801

Article  PubMed  PubMed Central  Google Scholar 

Wang Y et al (2022) Contrast-enhanced ultrasound-magnetic resonance imaging radiomics based model for predicting the biochemical recurrence of prostate cancer: a feasibility study. Comput Math Methods Med 2022:8090529

PubMed  PubMed Central