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Application and progress of multi-parametric magnetic resonance imaging in predicting extracapsular extension of prostate cancer
CHEN Xinyue  LIU Zaiyi  HU Lei 

Cite this article as: CHEN X Y, LIU Z Y, HU L. Application and progress of multi-parametric magnetic resonance imaging in predicting extracapsular extension of prostate cancer[J]. Chin J Magn Reson Imaging, 2025, 16(4): 198-201, 227. DOI:10.12015/issn.1674-8034.2025.04.032.


[Abstract] Prostate cancer (PCa) is one of the most common cancers among men globally, and accurately assessing extracapsular extension (ECE) is crucial for optimizing treatment plans. Traditional clinical diagnostic parameters have limitations such as low accuracy and high heterogeneity. Multi-parametric magnetic resonance imaging (mpMRI) is the preferred method for preoperative staging of PCa. However, the diagnostic efficacy of predicting ECE based on the traditional ECE risk assessment grading system using mpMRI remains limited by the experience of radiologists. With the development of emerging technologies, radiomics and deep learning (DL) have demonstrated potential in assessing ECE, but still face challenges such as insufficient external validation and weak model generalization ability. This article reviews the current research status, progress, and limitations of traditional risk assessment grading systems, radiomics, and DL based on mpMRI in diagnosing PCa ECE. The aim is to provide more comprehensive references for clinical decision-making, and accelerate the vigorous progress of precision medicine.
[Keywords] prostate cancer；extracapsular extension；magnetic resonance imaging；risk assessment grading system；radiomics；machine learning；deep learning

CHEN Xinyue1, 2   LIU Zaiyi1, 2   HU Lei1, 2*  

1 Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China

2 Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China

Corresponding author: HU L, E-mail: hulei@gdph.org.cn

Conflicts of interest   None.

Received  2025-02-13
Accepted  2025-04-10
DOI: 10.12015/issn.1674-8034.2025.04.032
Cite this article as: CHEN X Y, LIU Z Y, HU L. Application and progress of multi-parametric magnetic resonance imaging in predicting extracapsular extension of prostate cancer[J]. Chin J Magn Reson Imaging, 2025, 16(4): 198-201, 227. DOI:10.12015/issn.1674-8034.2025.04.032.

[1]
SIEGEL R L, GIAQUINTO A N, JEMAL A. Cancer statistics, 2024[J]. CA A Cancer J Clinicians, 2024, 74(1): 12-49. DOI: 10.3322/caac.21820.
[2]
KATSIMPERIS S, JULIEBØ-JONES P, TA A, et al. Surgical techniques to preserve continence after robot-assisted radical prostatectomy[J/OL]. Front Surg, 2023, 10: 1289765 [2025-01-19]. https://www.ncbi.nlm.nih.gov/pubmed/38026481. DOI: 10.3389/fsurg.2023.1289765.
[3]
BUYYOUNOUSKI M K, CHOYKE P L, MCKENNEY J K, et al. Prostate cancer - major changes in the American Joint Committee on Cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2017, 67(3): 245-253. DOI: 10.3322/caac.21391.
[4]
SUGANO D, SIDANA A, JAIN A L, et al. Index tumor volume on MRI as a predictor of clinical and pathologic outcomes following radical prostatectomy[J]. Int Urol Nephrol, 2019, 51(8): 1349-1355. DOI: 10.1007/s11255-019-02168-4.
[5]
PELLEGRINO F, FALAGARIO U G, KNIPPER S, et al. Assessing the impact of positive surgical margins on mortality in patients who underwent robotic radical prostatectomy: 20 years' report from the EAU robotic urology section scientific working group[J]. Eur Urol Oncol, 2024, 7(4): 888-896. DOI: 10.1016/j.euo.2023.11.021.
[6]
ZHU Z S, ZHU Y Y, XIAO Y Y, et al. Indications for nerve-sparing surgery for radical prostatectomy: Results from a single-center study[J/OL]. Front Oncol, 2022, 12: 896033 [2025-01-19]. https://www.ncbi.nlm.nih.gov/pubmed/35965515. DOI: 10.3389/fonc.2022.896033.
[7]
PRESTON M A, BREAU R H, LANTZ A G, et al. The association between nerve sparing and a positive surgical margin during radical prostatectomy[J/OL]. Urol Oncol, 2015, 33(1): 18.e1-18. e6 [2025-01-08]. https://www.ncbi.nlm.nih.gov/pubmed/25308562. DOI: 10.1016/j.urolonc.2014.09.006.
[8]
WEI J T, BAROCAS D, CARLSSON S, et al. Early detection of prostate cancer: AUA/SUO guideline part I: prostate cancer screening[J]. J Urol, 2023, 210(1): 46-53. DOI: 10.1097/JU.0000000000003491.
[9]
PASCHEN U, STURTZ S, FLEER D, et al. Assessment of prostate-specific antigen screening: an evidence-based report by the German institute for quality and efficiency in health care[J]. BJU Int, 2022, 129(3): 280-289. DOI: 10.1111/bju.15444.
[10]
HALPERN J A, OROMENDIA C, SHOAG J E, et al. Use of digital rectal examination as an adjunct to prostate specific antigen in the detection of clinically significant prostate cancer[J]. J Urol, 2018, 199(4): 947-953. DOI: 10.1016/j.juro.2017.10.021.
[11]
VIS A N, VAN DEN BERGH R C N, VAN DER POEL H G, et al. Selection of patients for nerve sparing surgery in robot-assisted radical prostatectomy[J]. BJUI Compass, 2021, 3(1): 6-18. DOI: 10.1002/bco2.115.
[12]
VON OSTAU N E, HANDKE A E, WIESENFARTH M, et al. Bicenter validation of a risk model for the preoperative prediction of extraprostatic extension of localized prostate cancer combining clinical and multiparametric MRI parameters[J/OL]. World J Urol, 2024, 42(1): 530 [2025-01-08]. https://www.ncbi.nlm.nih.gov/pubmed/39302458. DOI: 10.1007/s00345-024-05232-6.
[13]
TURKBEY B, ROSENKRANTZ A B, HAIDER M A, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2[J]. Eur Urol, 2019, 76(3): 340-351. DOI: 10.1016/j.eururo.2019.02.033.
[14]
CAGLIC I, SUSHENTSEV N, COLARIETI A, et al. Value of the capsular enhancement sign on dynamic contrast-enhanced prostate multiparametric MRI for the detection of extracapsular extension[J/OL]. Eur J Radiol, 2022, 150: 110275 [2025-01-11]. https://www.ncbi.nlm.nih.gov/pubmed/35358786. DOI: 10.1016/j.ejrad.2022.110275.
[15]
WALKER S M, TURKBEY B. PI-RADSv2.1: current status[J]. Turk J Urol, 2021, 47(Supp1): S45-S48. DOI: 10.5152/tud.2020.20403.
[16]
DE ROOIJ M, ISRAËL B, TUMMERS M, et al. ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists' training[J]. Eur Radiol, 2020, 30(10): 5404-5416. DOI: 10.1007/s00330-020-06929-z.
[17]
TRIQUELL M, REGIS L, WINKLER M, et al. Multiparametric MRI for staging of prostate cancer: a multicentric analysis of predictive factors to improve identification of extracapsular extension before radical prostatectomy[J/OL]. Cancers, 2022, 14(16): 3966 [2025-01-08]. https://www.ncbi.nlm.nih.gov/pubmed/36010963. DOI: 10.3390/cancers14163966.
[18]
MERDER E, ARıMAN A, ALTUNRENDE F. A modified partın table to better predict extracapsular extensıon in clinically localized prostate cancer[J]. Urol J, 2021, 18(1): 74-80. DOI: 10.22037/uj.v16i7.6477.
[19]
CALIMANO-RAMIREZ L F, VIRARKAR M K, HERNANDEZ M, et al. MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review[J]. Abdom Radiol, 2023, 48(7): 2379-2400. DOI: 10.1007/s00261-023-03924-y.
[20]
FENG T S, SHARIF-AFSHAR A R, WU J, et al. Multiparametric MRI improves accuracy of clinical nomograms for predicting extracapsular extension of prostate cancer[J]. Urology, 2015, 86(2): 332-337. DOI: 10.1016/j.urology.2015.06.003.
[21]
SHEN J K, KILDAY P, LAU C S. Possible drop metastasis after treatment of renal cell carcinoma with percutaneous cryoablation[J]. Urology, 2019, 130: 17-19. DOI: 10.1016/j.urology.2019.04.010.
[22]
WANG J G, HUANG B T, HUANG L, et al. Prediction of extracapsular extension in prostate cancer using the Likert scale combined with clinical and pathological parameters[J/OL]. Front Oncol, 2023, 13: 1229552 [2025-01-11]. https://www.ncbi.nlm.nih.gov/pubmed/37614509. DOI: 10.3389/fonc.2023.1229552.
[23]
MEHRALIVAND S, SHIH J H, HARMON S, et al. A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI[J]. Radiology, 2019, 290(3): 709-719. DOI: 10.1148/radiol.2018181278.
[24]
ASFUROĞLU U, ASFUROĞLU B B, ÖZER H, et al. Which one is better for predicting extraprostatic extension on multiparametric MRI: ESUR score, Likert scale, tumor contact length, or EPE grade?[J/OL]. Eur J Radiol, 2022, 149: 110228 [2025-01-11]. https://pubmed.ncbi.nlm.nih.gov/35255320. DOI: 10.1016/j.ejrad.2022.110228.
[25]
LI W, DONG A D, HONG G H, et al. Diagnostic performance of ESUR scoring system for extraprostatic prostate cancer extension: A meta-analysis[J/OL]. Eur J Radiol, 2021, 143: 109896 [2025-01-12]. https://www.ncbi.nlm.nih.gov/pubmed/34416449. DOI: 10.1016/j.ejrad.2021.109896.
[26]
MAYOR N, ELDRED-EVANS D, TAM H, et al. Prostagram magnetic resonance imaging in a screening population: Prostate Imaging-Reporting and Data System or Likert?[J]. BJU Int, 2024, 133(1): 112-117. DOI: 10.1111/bju.16155.
[27]
WINDISCH O, BENAMRAN D, DARIANE C, et al. Role of the prostate imaging quality PI-QUAL score for prostate magnetic resonance image quality in pathological upstaging after radical prostatectomy: a multicentre European study[J]. Eur Urol Open Sci, 2022, 47: 94-101. DOI: 10.1016/j.euros.2022.11.013.
[28]
PONSIGLIONE A, STANZIONE A, CALIFANO G, et al. MR image quality in local staging of prostate cancer: Role of PI-QUAL in the detection of extraprostatic extension[J/OL]. Eur J Radiol, 2023, 166: 110973 [2025-01-11]. https://pubmed.ncbi.nlm.nih.gov/37453275. DOI: 10.1016/j.ejrad.2023.110973.
[29]
COELHO F M A, AMARAL L T W, MITSUTAKE L K N, et al. Quality assessment of prostate MRI by PI-QUAL score: inter-reader agreement and impact on prostate cancer local staging at 3 tesla[J/OL]. Eur J Radiol, 2023, 165: 110921 [2025-01-11]. https://www.ncbi.nlm.nih.gov/pubmed/37336037. DOI: 10.1016/j.ejrad.2023.110921.
[30]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[31]
JANIESCH C, ZSCHECH P, HEINRICH K. Machine learning and deep learning[J]. Electron Mark, 2021, 31(3): 685-695. DOI: 10.1007/s12525-021-00475-2.
[32]
MA S, XIE H H, WANG H H, et al. MRI-based radiomics signature for the preoperative prediction of extracapsular extension of prostate cancer[J]. J Magn Reson Imaging, 2019, 50(6): 1914-1925. DOI: 10.1002/jmri.26777.
[33]
LOSNEGÅRD A, REISÆTER L A R, HALVORSEN O J, et al. Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients[J]. Acta Radiol, 2020, 61(11): 1570-1579. DOI: 10.1177/0284185120905066.
[34]
HUANG J Q, HE C, XU P R, et al. Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study[J/OL]. World J Urol, 2024, 42(1): 275 [2025-01-13]. https://www.ncbi.nlm.nih.gov/pubmed/38689190. DOI: 10.1007/s00345-024-04995-2.
[35]
XU L L, ZHANG G, ZHAO L, et al. Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer[J/OL]. Front Oncol, 2020, 10: 940 [2025-01-19]. https://www.ncbi.nlm.nih.gov/pubmed/32612953. DOI: 10.3389/fonc.2020.00940.
[36]
FAN X H, XIE N, CHEN J W, et al. Multiparametric MRI and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer[J/OL]. Front Oncol, 2022, 12: 839621 [2025-01-19]. https://www.ncbi.nlm.nih.gov/pubmed/35198452. DOI: 10.3389/fonc.2022.839621.
[37]
VAN DEN BERG I, SOETERIK T F W, VAN DER HOEVEN E J R J, et al. The development and external validation of artificial intelligence-driven MRI-based models to improve prediction of lesion-specific extraprostatic extension in patients with prostate cancer[J/OL]. Cancers, 2023, 15(22): 5452 [2025-01-19]. https://www.ncbi.nlm.nih.gov/pubmed/38001712. DOI: 10.3390/cancers15225452.
[38]
LIU Y. Comparison of magnetic resonance imaging-based radiomics features with nomogram for prediction of prostate cancer invasion[J]. Int J Gen Med, 2023, 16: 3043-3051. DOI: 10.2147/IJGM.S419039.
[39]
YANG L Q, JIN P F, QIAN J, et al. Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study[J]. Transl Cancer Res, 2023, 12(7): 1787-1801. DOI: 10.21037/tcr-22-2750.
[40]
LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
[41]
HUANG X R, LI Z T, ZHANG M H, et al. Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images[J/OL]. Front Oncol, 2022, 12: 994950 [2025-01-12]. https://pubmed.ncbi.nlm.nih.gov/36237311. DOI: 10.3389/fonc.2022.994950.
[42]
ZHAO L T, BAO J, QIAO X M, et al. Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study[J]. Eur J Nucl Med Mol Imaging, 2023, 50(3): 727-741. DOI: 10.1007/s00259-022-06036-9.
[43]
XU S S, HUYAN R X, WU Z, et al. Prostate cancer T-stage intelligent diagnosis based on MRI images and deep learning[J]. J Army Med Univ, 2023, 45(11): 1229-1236. DOI: 10.16016/j.2097-0927.202212188.
[44]
BHATTACHARYA I, SEETHARAMAN A, KUNDER C, et al. Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework[J/OL]. Med Image Anal, 2022, 75: 102288 [2025-01-12]. https://www.ncbi.nlm.nih.gov/pubmed/34784540. DOI: 10.1016/j.media.2021.102288.
[45]
HOU Y, ZHANG Y H, BAO J, et al. Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study[J]. Eur J Nucl Med Mol Imaging, 2021, 48(12): 3805-3816. DOI: 10.1007/s00259-021-05381-5.
[46]
MOROIANU Ş L, BHATTACHARYA I, SEETHARAMAN A, et al. Computational detection of extraprostatic extension of prostate cancer on multiparametric MRI using deep learning[J/OL]. Cancers, 2022, 14(12): 2821 [2025-01-23]. https://www.ncbi.nlm.nih.gov/pubmed/35740487. DOI: 10.3390/cancers14122821.
[47]
SIMON B D, MERRIMAN K M, HARMON S A, et al. Automated detection and grading of extraprostatic extension of prostate cancer at MRI via cascaded deep learning and random forest classification[J]. Acad Radiol, 2024, 31(10): 4096-4106. DOI: 10.1016/j.acra.2024.04.011.
[48]
PONSIGLIONE A, GAMBARDELLA M, STANZIONE A, et al. Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis[J]. Eur Radiol, 2024, 34(6): 3981-3991. DOI: 10.1007/s00330-023-10427-3.
[49]
LIN Y, BELUE M J, YILMAZ E C, et al. Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI[J]. Abdom Radiol, 2024, 49(8): 2891-2901. DOI: 10.1007/s00261-024-04468-5.
[50]
AHMAD I, ALQURASHI F. Early cancer detection using deep learning and medical imaging: a survey[J/OL]. Crit Rev Oncol Hematol, 2024, 204: 104528 [2025-01-12]. https://www.ncbi.nlm.nih.gov/pubmed/39413940. DOI: 10.1016/j.critrevonc.2024.104528.

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