MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估

朱学华; 邵立智; 刘振宇; 刘泽南; 何继德; 刘建刚; 平浩; 卢剑

doi:10.1631/jzus.B2200619

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MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估

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Review | Updated：2023-08-08

MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估
MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2023年24卷第8期页码：663-681
Affiliations：

1.Department of Urology, Peking University Third Hospital, Beijing 100191, China
2.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100080, China
4.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China
5.Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China, Beijing 100191, China
6.Department of Urology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
Funds：

the Beijing Natural Science Foundation(Z200027;L212051);the Cohort Construction Project of Peking University Third Hospital(BYSYDL2021012);the Medicine-X Project of Peking University Health Science Center(BMU2022MX014);the National Natural Science Foundation of China(61871004)
DOI：10.1631/jzus.B2200619
中图分类号：
CSTR：

朱学华, 邵立智, 刘振宇, 等. MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估[J]. 浙江大学学报（英文版）（B辑：生物医学和生物技术）, 2023,24(8):663-681.

Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, et al. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer[J]. Journal of Zhejiang University-SCIENCE B(Biomedicine & Biotechnology), 2023,24(8):663-681.
朱学华, 邵立智, 刘振宇, 等. MRI相关影像组学模型用于前列腺癌诊断、侵袭性和预后评估[J]. 浙江大学学报（英文版）（B辑：生物医学和生物技术）, 2023,24(8):663-681. DOI： 10.1631/jzus.B2200619.

Xuehua ZHU, Lizhi SHAO, Zhenyu LIU, et al. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer[J]. Journal of Zhejiang University-SCIENCE B(Biomedicine & Biotechnology), 2023,24(8):663-681. DOI： 10.1631/jzus.B2200619.

摘要

前列腺癌（PCa）是一种具有高度异质性的恶性肿瘤，这给PCa的精准诊断和最佳个性化治疗带来了难题。具备解剖和功能序列的多参数磁共振成像（mp-MRI）已经发展成为PCa检测和分期的重要工具。此外，随着人工智能（AI）和图像数据处理技术的快速发展，利用影像组学的方法定量提取医学影像数据迎来广阔的应用前景。影像组学通过提取影像特征来获得影像标志物，并在此基础上建立预测模型进行精确评估。影像组学模型提供了一个辅助精准医疗的可靠且无创的替代方案，较基于临床病理指标的传统模型具有明显优势。本综述致力于对PCa影像组学相关研究进行归纳总结，重点探讨了基于MRI的影像组学模型的开发和验证。本综述对有关PCa诊断、侵袭性和预后评估方面的影像组学预测模型相关文献进行了回顾和总结，重点关注具有临床应用潜力的预测模型。此外，我们深入探讨了不同模型可以解决的关键问题，以及在具体临床背景下可能遇到的困难。因此，本综述有助于鼓励研究人员根据实际的临床需求构建预测模型，并帮助泌尿外科医生更好地了解影像组学相关的重要研究成果。

Abstract

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.

关键词

核磁共振影像组学前列腺癌预测模型

Keywords

Magnetic resonance imaging (MRI)RadiomicsProstate cancerPredictive model

references

Abdollahi H, Mahdavi SR, Mofid B, 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. https://doi.org/10.1080/09553002.2018.1492756https://doi.org/10.1080/09553002.2018.1492756

Acharya UR, Hagiwara Y, Sudarshan VK, et al., 2018. Towards precision medicine: from quantitative imaging to radiomics. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 19(1):6-24. https://doi.org/10.1631/jzus.B1700260https://doi.org/10.1631/jzus.B1700260

Bagher-Ebadian H, Janic B, Liu C, 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. https://doi.org/10.3389/fonc.2019.01313https://doi.org/10.3389/fonc.2019.01313

Bai HL, Xia W, Ji XF, 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. https://doi.org/10.1002/jmri.27678https://doi.org/10.1002/jmri.27678

Berry B, Parry MG, Sujenthiran A, et al., 2020. Comparison of complications after transrectal and transperineal prostate biopsy: a national population-based study. BJU Int, 126(1):97-103. https://doi.org/10.1111/bju.15039https://doi.org/10.1111/bju.15039

Bi WL, Hosny A, Schabath MB, et al., 2019. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin, 69(2):127-157. https://doi.org/10.3322/caac.21552https://doi.org/10.3322/caac.21552

Bleker J, Kwee TC, Dierckx RAJO, 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. https://doi.org/10.1007/s00330-019-06488-yhttps://doi.org/10.1007/s00330-019-06488-y

Boehm K, Larcher A, Beyer B, 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. https://doi.org/10.1016/j.eururo.2015.07.051https://doi.org/10.1016/j.eururo.2015.07.051

Boevé LMS, Hulshof MCCM, Vis AN, 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. https://doi.org/10.1016/j.eururo.2018.09.008https://doi.org/10.1016/j.eururo.2018.09.008

Bourbonne V, Vallières M, Lucia F, et al., 2019. MRI-derived radiomics to guide post-operative management for high-risk prostate cancer. Front Oncol, 9:807. https://doi.org/10.3389/fonc.2019.00807https://doi.org/10.3389/fonc.2019.00807

Bourbonne V, Fournier G, Vallières M, 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. https://doi.org/10.3390/cancers12040814https://doi.org/10.3390/cancers12040814

Bourbonne V, Jaouen V, Nguyen TA, et al., 2021. Development of a radiomic-based model predicting lymph node involvement in prostate cancer patients. Cancers, 13(22):5672. https://doi.org/10.3390/cancers13225672https://doi.org/10.3390/cancers13225672

Brajtbord JS, Leapman MS, Cooperberg MR, 2017. The CAPRA score at 10 years: contemporary perspectives and analysis of supporting studies. Eur Urol, 71(5):‍705-709. https://doi.org/10.1016/j.eururo.2016.08.065https://doi.org/10.1016/j.eururo.2016.08.065

Brancato V, Aiello M, Basso L, 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. https://doi.org/10.1038/s41598-020-80749-5https://doi.org/10.1038/s41598-020-80749-5

Brunese L, Mercaldo F, Reginelli A, et al., 2020. Radiomics for gleason score detection through deep learning. Sensors, 20(18):5411. https://doi.org/10.3390/s20185411https://doi.org/10.3390/s20185411

Campbell JM, Raymond E, O'Callaghan ME, 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. https://doi.org/10.1016/j.clgc.2017.06.001https://doi.org/10.1016/j.clgc.2017.06.001

Campbell JM, O'Callaghan ME, Raymond E, 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. https://doi.org/10.1016/j.clgc.2017.03.011https://doi.org/10.1016/j.clgc.2017.03.011

Castillo TJM, Starmans MPA, Arif M, 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. https://doi.org/10.3390/diagnostics11020369https://doi.org/10.3390/diagnostics11020369

Chaddad A, Kucharczyk MJ, Niazi T, 2018. Multimodal radiomic features for the predicting gleason score of prostate cancer. Cancers, 10(8):249. https://doi.org/10.3390/cancers10080249https://doi.org/10.3390/cancers10080249

Chen T, Li MJ, Gu YF, 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. https://doi.org/10.1002/jmri.26243https://doi.org/10.1002/jmri.26243

Chen T, Zhang ZY, Tan SX, 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. https://doi.org/10.3389/fonc.2021.792456https://doi.org/10.3389/fonc.2021.792456

Cooperberg MR, Pasta DJ, Elkin EP, 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. https://doi.org/10.1097/01.ju.0000158155.33890.e7https://doi.org/10.1097/01.ju.0000158155.33890.e7

Cooperberg MR, Hinotsu S, Namiki M, et al., 2009. Risk assessment among prostate cancer patients receiving primary androgen deprivation therapy. J Clin Oncol, 27(26):4306-4313. https://doi.org/10.1200/Jco.2008.21.5228https://doi.org/10.1200/Jco.2008.21.5228

Costa DN, 2021. Multiparametric MRI of the prostate: beyond cancer detection and staging. Radiology, 299(3):624-625. https://doi.org/10.1148/radiol.2021204506https://doi.org/10.1148/radiol.2021204506

Cuocolo R, Stanzione A, Faletti R, 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. https://doi.org/10.1007/s00330-021-07856-3https://doi.org/10.1007/s00330-021-07856-3

D'Amico AV, Whittington R, Malkowicz SB, 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. https://doi.org/10.1001/jama.280.11.969https://doi.org/10.1001/jama.280.11.969

Fehr D, Veeraraghavan H, Wibmer A, et al., 2015. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA, 112(46):E6265-E6273. https://doi.org/10.1073/pnas.1505935112https://doi.org/10.1073/pnas.1505935112

Ferro M, de Cobelli O, Vartolomei MD, et al., 2021. Prostate cancer radiogenomics—from imaging to molecular characterization. Int J Mol Sci, 22(18):9971. https://doi.org/10.3390/ijms22189971https://doi.org/10.3390/ijms22189971

French WW, Wallen EM, 2020. Advances in the diagnostic options for prostate cancer. Postgrad Med, 132(S4):52-62. https://doi.org/10.1080/00325481.2020.1822067https://doi.org/10.1080/00325481.2020.1822067

Gnep K, Fargeas A, Gutiérrez-Carvajal RE, et al., 2017. Haralick textural features on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging, 45(1):103-117. https://doi.org/10.1002/jmri.25335https://doi.org/10.1002/jmri.25335

Gong LX, Xu M, Fang MJ, et al., 2020. Noninvasive prediction of high-grade prostate cancer via biparametric MRI radiomics. J Magn Reson Imaging, 52(4):1102-1109. https://doi.org/10.1002/jmri.27132https://doi.org/10.1002/jmri.27132

Gong LX, Xu M, Fang MJ, et al., 2022. The potential of prostate gland radiomic features in identifying the Gleason score. Comput Biol Med, 144:105318. https://doi.org/10.1016/j.compbiomed.2022.105318https://doi.org/10.1016/j.compbiomed.2022.105318

Han C, Ma S, Liu X, 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. https://doi.org/10.1002/jmri.27565https://doi.org/10.1002/jmri.27565

Hectors SJ, Chen C, Chen J, 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. https://doi.org/10.1002/jmri.27692https://doi.org/10.1002/jmri.27692

Hinev AI, Anakievski D, Kolev N, 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.

Hou Y, Bao ML, Wu CJ, 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. https://doi.org/10.1007/s00261-020-02678-1https://doi.org/10.1007/s00261-020-02678-1

Hou Y, Bao J, Song Y, 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. https://doi.org/10.1016/j.ebiom.2021.103395https://doi.org/10.1016/j.ebiom.2021.103395

Hu L, Zhou DW, Fu CX, 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. https://doi.org/10.1007/s00330-020-07227-4https://doi.org/10.1007/s00330-020-07227-4

Hu MB, Yang T, Hu JM, 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. https://doi.org/10.1007/s10147-017-1236-5https://doi.org/10.1007/s10147-017-1236-5

Hueting TA, Cornel EB, Somford DM, 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. https://doi.org/10.1016/j.euo.2018.04.016https://doi.org/10.1016/j.euo.2018.04.016

Isbarn H, Karakiewicz PI, Walz J, 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. https://doi.org/10.1016/j.ijrobp.2009.05.066https://doi.org/10.1016/j.ijrobp.2009.05.066

Johnson LM, Turkbey B, Figg WD, et al., 2014. Multiparametric MRI in prostate cancer management. Nat Rev Clin Oncol, 11(6):346-353. https://doi.org/10.1038/nrclinonc.2014.69https://doi.org/10.1038/nrclinonc.2014.69

Kalantar R, Lin G, Winfield JM, et al., 2021. Automatic segmentation of pelvic cancers using deep learning: state-of-the-art approaches and challenges. Diagnostics, 11(11):1964. https://doi.org/10.3390/diagnostics11111964https://doi.org/10.3390/diagnostics11111964

Kattan MW, Eastham JA, Stapleton AMF, et al., 1998. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst, 90(10):766-771. https://doi.org/10.1093/jnci/90.10.766https://doi.org/10.1093/jnci/90.10.766

Krishna S, Lim CS, McInnes MDF, et al., 2018. Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer. J Magn Reson Imaging, 47(1):176-185. https://doi.org/10.1002/jmri.25729https://doi.org/10.1002/jmri.25729

Kucharczyk M, 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/NCT05024162https://clinicaltrials.gov/ct2/show/record/NCT05024162

Lambin P, Leijenaar RTH, Deist TM, et al., 2017. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 14(12):749-762. https://doi.org/10.1038/nrclinonc.2017.141https://doi.org/10.1038/nrclinonc.2017.141

Lee CH, Tan TW, Tan CH, 2021. Multiparametric MRI in active surveillance of prostate cancer: an overview and a practical approach. Korean J Radiol, 22(7):1087-1099. https://doi.org/10.3348/kjr.2020.1224https://doi.org/10.3348/kjr.2020.1224

Li L, Shiradkar R, Leo P, 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. https://doi.org/10.1016/j.ebiom.2020.103163https://doi.org/10.1016/j.ebiom.2020.103163

Li MJ, Chen T, Zhao WL, 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. https://doi.org/10.21037/qims.2019.12.06https://doi.org/10.21037/qims.2019.12.06

Li TP, Sun LN, Li QH, 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. https://doi.org/10.3389/fonc.2021.825429https://doi.org/10.3389/fonc.2021.825429

Limkin EJ, Sun R, Dercle L, et al., 2017. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol, 28(6):‍1191-1206. https://doi.org/10.1093/annonc/mdx034https://doi.org/10.1093/annonc/mdx034

Litwin MS, Tan HJ, 2017. The diagnosis and treatment of prostate cancer: a review. JAMA, 317(24):2532-2542. https://doi.org/10.1001/jama.2017.7248https://doi.org/10.1001/jama.2017.7248

Liu LF, Yi XP, Lu C, et al., 2020. Applications of radiomics in genitourinary tumors. Am J Cancer Res, 10(8):‍2293-2308.

Losnegård A, Reisæter LAR, Halvorsen OJ, 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. https://doi.org/10.1177/0284185120905066https://doi.org/10.1177/0284185120905066

Lughezzani G, Budäus L, Isbarn H, 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. https://doi.org/10.1016/j.eururo.2009.12.003https://doi.org/10.1016/j.eururo.2009.12.003

Ma S, Xie HH, Wang HH, 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. https://doi.org/10.1002/jmri.26777https://doi.org/10.1002/jmri.26777

Mayerhoefer ME, Materka A, Langs G, et al., 2020. Introduction to radiomics. J Nucl Med, 61(4):488-495. https://doi.org/10.2967/jnumed.118.222893https://doi.org/10.2967/jnumed.118.222893

Meijer D, van Leeuwen PJ, Roberts MJ, 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. https://doi.org/10.1016/j.eururo.2021.05.006https://doi.org/10.1016/j.eururo.2021.05.006

Merriel SWD, Pocock L, Gilbert E, 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. https://doi.org/10.1186/s12916-021-02230-yhttps://doi.org/10.1186/s12916-021-02230-y

Midiri F, Vernuccio F, Purpura P, et al., 2021. Multiparametric MRI and radiomics in prostate cancer: a review of the current literature. Diagnostics, 11(10):1829. https://doi.org/10.3390/diagnostics11101829https://doi.org/10.3390/diagnostics11101829

Min XD, Li M, Dong D, 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. https://doi.org/10.1016/j.ejrad.2019.03.010https://doi.org/10.1016/j.ejrad.2019.03.010

Moreira DM, Jayachandran J, JrPresti JC, 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. https://doi.org/10.1111/j.1464-410X.2009.08623.xhttps://doi.org/10.1111/j.1464-410X.2009.08623.x

Nesbitt AL, Kapoor J, Piesse C, 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. https://doi.org/10.1111/ans.14960https://doi.org/10.1111/ans.14960

Ondracek RP, Kattan MW, Murekeyisoni C, et al., 2016. Validation of the Kattan nomogram for prostate cancer recurrence after radical prostatectomy. J Natl Compr Cancer Netw, 14(11):1395-1401. https://doi.org/10.6004/jnccn.2016.0149https://doi.org/10.6004/jnccn.2016.0149

Parra NA, Lu H, Choi J, et al., 2019. Habitats in DCE-MRI to predict clinically significant prostate cancers. Tomography, 5(1):68-76. https://doi.org/10.18383/j.tom.2018.00037https://doi.org/10.18383/j.tom.2018.00037

Puech P, Sufana-Iancu A, Renard B, et al., 2013. Prostate MRI: can we do without DCE sequences in 2013? Diagn Interv Imaging, 94(12):1299-1311. https://doi.org/10.1016/j.diii.2013.09.010https://doi.org/10.1016/j.diii.2013.09.010

Punnen S, Freedland SJ, JrPresti JC, 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. https://doi.org/10.1016/j.eururo.2013.03.058https://doi.org/10.1016/j.eururo.2013.03.058

Qi YF, Zhang ST, Wei JW, 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. https://doi.org/10.1002/jmri.27008https://doi.org/10.1002/jmri.27008

Roupret M, Hupertan V, Comperat E, 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. https://doi.org/10.1016/S0022-5347(09)62004-8https://doi.org/10.1016/S0022-5347(09)62004-8

Shao LZ, Yan Y, Liu ZY, 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. https://doi.org/10.7150/thno.48706https://doi.org/10.7150/thno.48706

Shiota M, Yokomizo A, Takeuchi A, 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. https://doi.org/10.1007/s00432-014-1828-7https://doi.org/10.1007/s00432-014-1828-7

Shiradkar R, Ghose S, Jambor I, et al., 2018. Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging, 48(6):1626-1636. https://doi.org/10.1002/jmri.26178https://doi.org/10.1002/jmri.26178

Siddiqui MM, Rais-Bahrami S, Turkbey B, 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. https://doi.org/10.1001/jama.2014.17942https://doi.org/10.1001/jama.2014.17942

Siegel RL, Miller KD, Fuchs HE, et al., 2022. Cancer statistics, 2022. CA Cancer J Clin, 72(1):7-33. https://doi.org/10.3322/caac.21708https://doi.org/10.3322/caac.21708

Soeterik TFW, Hueting TA, Israel B, 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. https://doi.org/10.1111/bju.15376https://doi.org/10.1111/bju.15376

Spohn SKB, Bettermann AS, Bamberg F, 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. https://doi.org/10.7150/thno.61207https://doi.org/10.7150/thno.61207

Stanzione A, Cuocolo R, Cocozza S, 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. https://doi.org/10.1016/j.acra.2018.12.025https://doi.org/10.1016/j.acra.2018.12.025

Stanzione A, Gambardella M, Cuocolo R, et al., 2020. Prostate MRI radiomics: a systematic review and radiomic quality score assessment. Eur J Radiol, 129:109095. https://doi.org/10.1016/j.ejrad.2020.109095https://doi.org/10.1016/j.ejrad.2020.109095

Stephenson AJ, Scardino PT, Eastham JA, 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. https://doi.org/10.1093/jnci/djj190https://doi.org/10.1093/jnci/djj190

Sun Y, Reynolds HM, Parameswaran B, et al., 2019. Multiparametric MRI and radiomics in prostate cancer: a review. Australas Phys Eng Sci Med, 42(1):3-25. https://doi.org/10.1007/s13246-019-00730-zhttps://doi.org/10.1007/s13246-019-00730-z

Tamblyn DJ, Chopra S, Yu CH, et al., 2011. Comparative analysis of three risk assessment tools in Australian patients with prostate cancer. BJU Int, 108(S2):51-56. https://doi.org/10.1111/j.1464-410X.2011.10687.xhttps://doi.org/10.1111/j.1464-410X.2011.10687.x

To MNN, Vu DQ, Turkbey B, 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. https://doi.org/10.1007/s11548-018-1841-4https://doi.org/10.1007/s11548-018-1841-4

Turkbey B, Rosenkrantz AB, Haider MA, 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. https://doi.org/10.1016/j.eururo.2019.02.033https://doi.org/10.1016/j.eururo.2019.02.033

Wallis CJD, Saskin R, Choo R, et al., 2016. Surgery versus radiotherapy for clinically-localized prostate cancer: a systematic review and meta-analysis. Eur Urol, 70(1):21-30. https://doi.org/10.1016/j.eururo.2015.11.010https://doi.org/10.1016/j.eururo.2015.11.010

Wang B, Lei Y, Tian SB, et al., 2019. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys, 46(4):1707-1718. https://doi.org/10.1002/mp.13416https://doi.org/10.1002/mp.13416

Wang J, Wu CJ, Bao ML, 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. https://doi.org/10.1007/s00330-017-4800-5https://doi.org/10.1007/s00330-017-4800-5

Wang YR, Yu B, Zhong F, 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. https://doi.org/10.1016/j.mri.2019.03.007https://doi.org/10.1016/j.mri.2019.03.007

Woźnicki P, Westhoff N, Huber T, et al., 2020. Multiparametric MRI for prostate cancer characterization: combined use of radiomics model with PI-RADS and clinical parameters. Cancers, 12(7):1767. https://doi.org/10.3390/cancers12071767https://doi.org/10.3390/cancers12071767

Wu LM, Xu JR, Ye YQ, 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. https://doi.org/10.2214/AJR.11.7634https://doi.org/10.2214/AJR.11.7634

Wu S, Jiao YN, Zhang YF, et al., 2019. Imaging-based individualized response prediction of carbon ion radiotherapy for prostate cancer patients. Cancer Manage Res, 11:9121-9131. https://doi.org/10.2147/Cmar.S214020https://doi.org/10.2147/Cmar.S214020

Xylinas E, Daché A, Rouprêt M, 2010. Is radical prostatectomy a viable therapeutic option in clinically locally advanced (cT3) prostate cancer? BJU Int, 106(11):1596-1600. https://doi.org/10.1111/j.1464-410X.2010.09630.xhttps://doi.org/10.1111/j.1464-410X.2010.09630.x

Yan Y, Shao LZ, Liu ZY, 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. https://doi.org/10.3390/cancers13123098https://doi.org/10.3390/cancers13123098

Yoneda K, Utsumi T, Somoto T, 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. https://doi.org/10.1093/jjco/hyx174https://doi.org/10.1093/jjco/hyx174

Zelic R, Garmo H, Zugna D, 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. https://doi.org/10.1016/j.eururo.2019.09.027https://doi.org/10.1016/j.eururo.2019.09.027

Zhang H, Li XL, Zhang YX, 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. https://doi.org/10.1002/jmri.27486https://doi.org/10.1002/jmri.27486

Zhang WJ, Mao N, Wang YS, et al., 2020. A radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients. Eur J Radiol, 128:109020. https://doi.org/10.1016/j.ejrad.2020.109020https://doi.org/10.1016/j.ejrad.2020.109020

Zhang YS, Chen W, Yue XJ, 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. https://doi.org/10.3389/fonc.2020.00888https://doi.org/10.3389/fonc.2020.00888

Zhong QZ, Long LH, Liu A, et al., 2020. Radiomics of multiparametric MRI to predict biochemical recurrence of localized prostate cancer after radiation therapy. Front Oncol, 10:731. https://doi.org/10.3389/fonc.2020.00731https://doi.org/10.3389/fonc.2020.00731

Zwanenburg A, Vallières M, Abdalah MA, et al., 2020. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2):328-338. https://doi.org/10.1148/radiol.2020191145https://doi.org/10.1148/radiol.2020191145

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