Research progress on precise diagnosis and treatment of bladder cancer based on multiparameter MRI radiomics

Share this content in WeChat

• Review •

BAI Jingjing ZHANG Lu WANG Xiaochun YANG Guoqiang

Cite this article as: Bai JJ, Zhang L, Wang XC, et al. Research progress on precise diagnosis and treatment of bladder cancer based on multiparameter MRI radiomics[J]. Chin J Magn Reson Imaging, 2022, 13(11): 157-160. DOI:10.12015/issn.1674-8034.2022.11.032.

Abstract

[Abstract] The incidence of bladder cancer (BCa) ranks among the forefront of urinary system malignancies in the world, with high recurrence rate and high mortality rate. Early and accurate diagnosis of pathological grade, lymph node metastasis and myometrial invasion of bladder cancer plays an important role in treatment decision making and prognosis evaluation of efficacy. Multiparameter magnetic resonance imaging (mpMRI) provides a key imaging method for early and accurate diagnosis of bladder cancer due to its high soft tissue resolution and multi-level information of structure and function.In recent years, with the development of intelligent diagnostic technology of radiomics, MRI radiomics has important clinical application value in tumor diagnosis, efficacy evaluation and prognosis prediction by mining micro-scale information hidden in multi-sequence images. This article provides a systematic review of the progress of mpMRI radiomics in preoperative grade prediction of bladder cancer, lymph node metastasis, myometrial invasion and prognosis evaluation of efficacy.

[Keywords] bladder cancer；pathological grading；lymph node metastasis；myometrial invasion；curative effect evaluation；prognosis；multiparameter magnetic resonance imaging；radiomics；magnetic resonance imaging

Contributor Information

BAI Jingjing^{1,
2}   ZHANG Lu^{1,
2}   WANG Xiaochun^{1,
2}   YANG Guoqiang^{1,
2*}

¹ Department of Magnetic Resonance Imaging, First Hospital of Shanxi Medical University, Taiyuan 030001, China

² School of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

Yang GQ, E-mail: doctor_ygq@163.com

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81971592).

Received 2022-06-08

Accepted 2022-10-12

DOI: 10.12015/issn.1674-8034.2022.11.032

Text

References

[1]

Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492.

[2]

Siegel R, Miller K, Fuchs HE, et al. Cancer statistics, 2021[J]. CA A Cancer J Clin, 2021, 71: 7-33. DOI: 10.3322/caac.21654.

[3]

Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.

[4]

Xu XP, Du P, Xu H, et al. Study on constructing prediction model of muscle-invasive status of bladder cancer based on multimodal MRI radiomics strategy[J]. China Med Equip, 2020, 17(9): 5-9.

[5]

Alfred Witjes J, Lebret T, Compérat EM, et al. Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer[J]. Eur Urol, 2017, 71(3): 462-475. DOI: 10.1016/j.eururo.2016.06.020.

[6]

Wang HJ, Xu XP, Zhang X, et al. Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study[J]. Eur Radiol, 2020, 30(9): 4816-4827. DOI: 10.1007/s00330-020-06796-8.

[7]

Yuan LH, Li DY, Mu D, et al. Combined T2 SPAIR, Dynamic Enhancement and DW Imaging Reliably Detect T Staging and Grading of Bladder Cancer With 3.0T MRI[J/OL]. Front Oncol, 2020, 10 [2022-09-21]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683786/pdf/fonc-10-582532.pdf. DOI: 10.3389/fonc.2020.582532.

[8]

Wang XY, Tu N, Sun F, et al. Detecting muscle invasion of bladder cancer using a proposed magnetic resonance imaging strategy[J]. J Magn Reson Imaging, 2021, 54(4): 1212-1221. DOI: 10.1002/jmri.27676.

[9]

Hammouda K, Khalifa F, Soliman A, et al. A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging[J/OL]. Comput Med Imaging Graph, 2021, 90 [2022-09-21]. https://www.sciencedirect.com/science/article/abs/pii/S0895611121000604. DOI: 10.1016/j.compmedimag.2021.101911.

[10]

Zhang X, Xu XP, Tian Q, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging[J]. J Magn Reson Imaging, 2017, 46(5): 1281-1288. DOI: 10.1002/jmri.25669.

[11]

Cornelissen SWE, Veenboer PW, Wessels FJ, et al. Diagnostic Accuracy of Multiparametric MRI for Local Staging of Bladder Cancer: A Systematic Review and Meta-Analysis[J/OL]. Urology, 2020, 145 [2022-09-21]. https://www.sciencedirect.com/science/article/pii/S0090429520308827. DOI: 10.1016/j.urology.2020.07.021.

[12]

van der Pol CB, Sahni VA, Eberhardt SC, et al. ACR appropriateness criteria® pretreatment staging of muscle-invasive bladder cancer[J]. J Am Coll Radiol, 2018, 15(5): S150-S159. DOI: 10.1016/j.jacr.2018.03.020.

[13]

de Haas RJ, Steyvers MJ, Fütterer JJ. Multiparametric MRI of the bladder: ready for clinical routine?[J]. AJR Am J Roentgenol, 2014, 202(6): 1187-1195. DOI: 10.2214/AJR.13.12294.

[14]

Akcay A, Yagci AB, Celen S, et al. VI-RADS score and tumor contact length in MRI: A potential method for the detection of muscle invasion in bladder cancer[J/OL]. Clin Imaging, 2021, 77 [2022-09-21]. https://www.clinicalimaging.org/article/S0899-7071(21)00083-8. DOI: 10.1016/j.clinimag.2021.02.026.

[15]

Panebianco V, Narumi Y, Altun E, et al. Multiparametric magnetic resonance imaging for bladder cancer: development of VI-RADS (vesical imaging-reporting and data system)[J]. Eur Urol, 2018, 74(3): 294-306. DOI: 10.1016/j.eururo.2018.04.029.

[16]

del Giudice F, Barchetti G, de Berardinis E, et al. Prospective assessment of vesical imaging reporting and data system (VI-RADS) and its clinical impact on the management of high-risk non-muscle-invasive bladder cancer patients candidate for repeated transurethral resection[J]. Eur Urol, 2020, 77(1): 101-109. DOI: 10.1016/j.eururo.2019.09.029.

[17]

Taguchi S, Watanabe M, Tambo M, et al. Proposal for a New Vesical Imaging-Reporting and Data System (VI-RADS)-Based Algorithm for the Management of Bladder Cancer: A Paradigm Shift From the Current Transurethral Resection of Bladder Tumor (TURBT)-Dependent Practice[J/OL]. Clin Genitourin Cancer, 2022, 22 [2022-09-21]. https://linkinghub.elsevier.com/retrieve/pii/S1558-7673(22)00047-7. DOI: 10.1016/j.clgc.2022.03.002.

[18]

Panebianco V, Pecoraro M, del Giudice F, et al. VI-RADS for bladder cancer: current applications and future developments[J]. J Magn Reson Imaging, 2022, 55(1): 23-36. DOI: 10.1002/jmri.27361.

[19]

Pizzi AD, Mastrodicasa D, Marchioni M, et al. Bladder cancer: do we need contrast injection for MRI assessment of muscle invasion? A prospective multi-reader VI-RADS approach[J]. Eur Radiol, 2021, 31(6): 3874-3883. DOI: 10.1007/s00330-020-07473-6.

[20]

Park KJ, Lee JL, Yoon SK, et al. Radiomics-based prediction model for outcomes of PD-1/PD-L1 immunotherapy in metastatic urothelial carcinoma[J]. Eur Radiol, 2020, 30(10): 5392-5403. DOI: 10.1007/s00330-020-06847-0.

[21]

Xu XP, Wang HJ, Du P, et al. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors[J]. J Magn Reson Imaging, 2019, 50(6): 1893-1904. DOI: 10.1002/jmri.26749.

[22]

Wu SX, Zheng JJ, Li Y, et al. A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer[J]. Clin Cancer Res, 2017, 23(22): 6904-6911. DOI: 10.1158/1078-0432.CCR-17-1510.

[23]

Dong Q, Zhang J, Wang HJ, et al. Prediction on muscle-invasive status of bladder cancer based on the preoperative multimodal MRI omics Radscore[J]. China Med Equip, 2021, 18(6): 17-21. DOI: 10.3969/J.ISSN.1672-8270.2021.06.006.

[24]

Wang HJ, Pui MH, Guan J, et al. Comparison of early submucosal enhancement and tumor stalk in staging bladder urothelial carcinoma[J]. AJR Am J Roentgenol, 2016, 207(4): 797-803. DOI: 10.2214/AJR.16.16283.

[25]

Luo C, Huang B, Wu YK, et al. Use of Vesical Imaging-Reporting and Data System (VI-RADS) for detecting the muscle invasion of bladder cancer: a diagnostic meta-analysis[J]. Eur Radiol, 2020, 30(8): 4606-4614. DOI: 10.1007/s00330-020-06802-z.

[26]

Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.

[27]

Babjuk M, Böhle A, Burger M, et al. EAU guidelines on non-muscle-invasive urothelial carcinoma of the bladder: update 2016[J]. Eur Urol, 2017, 71(3): 447-461. DOI: 10.1016/j.eururo.2016.05.041.

[28]

Witjes JA, Bruins HM, Cathomas R, et al. European association of urology guidelines on muscle-invasive and metastatic bladder cancer: summary of the 2020 guidelines[J]. Eur Urol, 2021, 79(1): 82-104. DOI: 10.1016/j.eururo.2020.03.055.

[29]

Cai Q, Wen ZH, Huang YP, et al. Investigation of synthetic magnetic resonance imaging applied in the evaluation of the tumor grade of bladder cancer[J]. J Magn Reson Imaging, 2021, 54(6): 1989-1997. DOI: 10.1002/jmri.27770.

[30]

Wang HJ, Hu DK, Yao HH, et al. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors[J]. Eur Radiol, 2019, 29(11): 6182-6190. DOI: 10.1007/s00330-019-06222-8.

[31]

Zheng ZT, Xu FJ, Gu ZR, et al. Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading[J]. Abdom Radiol (NY), 2021, 46(9): 4311-4323. DOI: 10.1007/s00261-021-03108-6.

[32]

Gschwend JE, Heck MM, Lehmann J, et al. Extended versus limited lymph node dissection in bladder cancer patients undergoing radical cystectomy: survival results from a prospective, randomized trial[J]. Eur Urol, 2019, 75(4): 604-611. DOI: 10.1016/j.eururo.2018.09.047.

[33]

Wu SX, Zheng JJ, Li Y, et al. Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer[J/OL]. EBio Medicine, 2018, 34 [2022-09-21]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6116473. DOI: 10.1016/j.ebiom.2018.07.029.

[34]

Wang HJ, Luo C, Zhang F, et al. Multiparametric MRI for bladder cancer: validation of VI-RADS for the detection of detrusor muscle invasion[J]. Radiology, 2019, 291(3): 668-674. DOI: 10.1148/radiol.2019182506.

[35]

Xu XP, Zhang X, Tian Q, et al. Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis[J]. J Magn Reson Imaging, 2019, 49(5): 1489-1498. DOI: 10.1002/jmri.26327.

[36]

Zheng JJ, Kong JQ, Wu SX, et al. Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach[J]. Cancer, 2019, 125(24): 4388-4398. DOI: 10.1002/cncr.32490.

[37]

Barchetti G, Simone G, Ceravolo I, et al. Multiparametric MRI of the bladder: inter-observer agreement and accuracy with the Vesical Imaging-Reporting and Data System (VI-RADS) at a single reference center[J]. Eur Radiol, 2019, 29(10): 5498-5506. DOI: 10.1007/s00330-019-06117-8.

[38]

Zheng ZT, Xu FJ, Gu ZR, et al. Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer[J/OL]. Front Oncol, 2021, 11 [2022-09-21]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155615. DOI: 10.3389/fonc.2021.619893.

[39]

Delli Pizzi A, Mastrodicasa D, Taraschi A, et al. Conspicuity and muscle-invasiveness assessment for bladder cancer using VI-RADS: a multi-reader, contrast-free MRI study to determine optimal b-values for diffusion-weighted imaging[J]. Abdom Radiol (NY), 2022, 47(5): 1862-1872. DOI: 10.1007/s00261-022-03490-9.

[40]

Jazayeri SB, Dehghanbanadaki H, Hosseini M, et al. Diagnostic accuracy of vesical imaging-reporting and data system (VI-RADS) in suspected muscle invasive bladder cancer: a systematic review and diagnostic meta-analysis[J]. Urol Oncol, 2022, 40(2): 45-55. DOI: 10.1016/j.urolonc.2021.11.008.

[41]

Hong SB, Lee NK, Kim S, et al. Vesical imaging-reporting and data system for multiparametric MRI to predict the presence of muscle invasion for bladder cancer[J]. J Magn Reson Imaging, 2020, 52(4): 1249-1256. DOI: 10.1002/jmri.27141.

[42]

Soukup V, Čapoun O, Cohen D, et al. Risk stratification tools and prognostic models in non-muscle-invasive bladder cancer: a critical assessment from the European association of urology non-muscle-invasive bladder cancer guidelines panel[J]. Eur Urol Focus, 2020, 6(3): 479-489. DOI: 10.1016/j.euf.2018.11.005.

[43]

Cambier S, Sylvester RJ, Collette L, et al. EORTC Nomograms and Risk Groups for Predicting Recurrence, Progression, and Disease-specific and Overall Survival in Non-Muscle-invasive Stage Ta-T1 Urothelial Bladder Cancer Patients Treated with 1-3 Years of Maintenance Bacillus Calmette-Guérin[J/OL]. Eur Urol, 2016, 69(1) [2022-09-21]. https://www.sciencedirect.com/science/article/abs/pii/S0302283815005564. DOI: 10.1016/j.eururo.2015.06.045.

[44]

Du P, Wang HJ, Shi YQ, et al. Prediction on the relapse of BC based on radiomics approach with multi-parametric MRI[J]. China Med Equip, 2018, 15(11): 39-43. DOI: 10.3969/J.ISSN.1672-8270.2018.11.011.

[45]

Zhang SH, Song MF, Zhao YS, et al. Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer[J/OL]. Eur J Radiol, 2020, 131 [2022-09-21]. https://www.ejradiology.com/article/S0720-048X(20)30408-3. DOI: 10.1016/j.ejrad.2020.109219.

[46]

Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338. DOI: 10.1148/radiol.2020191145.

[47]

Zhang GW. Research progress on molecular subtypings of bladder cancer[J]. J Clin Pathol Res, 2021, 41(5): 1151-1156. DOI: 10.3978/j.issn.2095-6959.2021.05.027.

[48]

Zhu HL, Jia XC, Wang YP, et al. M6A Classification Combined With Tumor Microenvironment Immune Characteristics Analysis of Bladder Cancer[J/OL]. Front Oncol, 2021, 11 [2022-09-21]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479184. DOI: 10.3389/fonc.2021.714267.

[49]

Velmahos CS, Badgeley M, Lo YC. Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images[J]. Cancer Med, 2021, 10(14): 4805-4813. DOI: 10.1002/cam4.4044.

[50]

Loeffler CML, Ortiz Bruechle N, Jung M, et al. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing?[J]. Eur Urol Focus, 2022, 8(2): 472-479. DOI: 10.1016/j.euf.2021.04.007.

PREV Application and research progress of radiomics in intraductal papillary mucinous neoplasm of the pancreas

NEXT Research progress on the brain network mechanism of Chinese medicine rehabilitation therapy intervention in knee osteoarthritis

LINK

Tel & Fax: +8610-67113815 E-mail: editor@cjmri.cn