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Research progress of radiomics in bladder cancer
BAO Kuanzhu  LIU Jiawei  HAO Jingang 

Cite this article as: BAO K Z, LIU J W, HAO J G. Research progress of radiomics in bladder cancer[J]. Chin J Magn Reson Imaging, 2023, 14(1): 189-193. DOI:10.12015/issn.1674-8034.2023.01.035.


[Abstract] Bladder cancer is one of the most common malignancies of the genitourinary system. In recent years, the morbidity and mortality rate of the disease in China has been increasing year by year. Therefore, early diagnosis of bladder cancer and prediction of its curative effect and prognosis is of great significance. At present, the primary diagnosis of bladder cancer is mainly made by traditional imaging examinations such as ultrasound, CT and MRI. It is difficult to make accurate diagnosis for the stage and grade of bladder cancer and some "homomorphic" bladder lesions. Radiomics can mine deeper information in medical images with high throughput. It has become a new method in the research of bladder cancer. This paper aims to explore the basic concept and workflow of radiomics and to review the recent progress of the application of radiomics in bladder cancer staging, pathological grading, differential diagnosis and prognosis prediction, it is expected to provide imaging guidance value for clinical in accurate diagnosis and treatment of bladder cancer.
[Keywords] bladder cancer；magnetic resonance imaging；radiomics；staging and pathological grading；differential diagnosis；prognosis prediction

BAO Kuanzhu   LIU Jiawei   HAO Jingang*  

Department of Radiology, the Second Affiliated Hospital of Kuming Medical University, Kunming 650101, China

Corresponding author: Hao JG, E-mail: kmhaohan@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Academic Leader Project Fund of Yunnan Provincial Health Commission (No. D-2018012).
Received  2022-07-14
Accepted  2022-12-05
DOI: 10.12015/issn.1674-8034.2023.01.035
Cite this article as: BAO K Z, LIU J W, HAO J G. Research progress of radiomics in bladder cancer[J]. Chin J Magn Reson Imaging, 2023, 14(1): 189-193. DOI:10.12015/issn.1674-8034.2023.01.035.

[1]
RICHTERS A, ABEN K K H, KIEMENEY L A L M. The global burden of urinary bladder cancer: an update[J]. World J Urol, 2020, 38(8): 1895-1904. DOI: 10.1007/s00345-019-02984-4.
[2]
ABOUELKHEIR R T, ABDELHAMID A, ABOU EL-GHAR M, et al. Imaging of bladder cancer: standard applications and future trends[J/OL]. Medicina (Kaunas), 2021, 57(3): 220 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000909/. DOI: 10.3390/medicina57030220.
[3]
DEMIRCIOĞLU A. Benchmarking feature selection methods in radiomics[J]. Invest Radiol, 2022, 57(7): 433-443. DOI: 10.1097/RLI.0000000000000855.
[4]
WITJES J A, BRUINS H M, 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.
[5]
ZHANG J Y. Prognostic factors of postoperative bladder cancer patients[J]. Chin J Gerontol, 2022, 42(4): 830-833. DOI: 10.3969/j.issn.1005-9202.2022.04.018.
[6]
JENSEN B T, LAURIDSEN S V. Introduction: bladder cancer[J/OL]. Semin Oncol Nurs, 2021, 37(1): 151103 [2022-07-13]. https://www.sciencedirect.com/science/article/abs/pii/S0749208120301182. DOI: 10.1016/j.soncn.2020.151103.
[7]
JOENSEN U N, MAIBOM S L, POULSEN A M. Surgical management of muscle invasive bladder cancer: a review of current recommendations[J/OL]. Semin Oncol Nurs, 2021, 37(1): 151104. DOI: 10.1016/j.soncn.2020.151104.
[8]
AHMADI H, DUDDALWAR V, DANESHMAND S. Diagnosis and staging of bladder cancer[J]. Hematol Oncol Clin North Am, 2021, 35(3): 531-541. DOI: 10.1016/j.hoc.2021.02.004.
[9]
BABJUK M, BURGER M, CAPOUN O, et al. European association of urology guidelines on non-muscle-invasive bladder cancer (Ta, T1, and carcinoma in situ)[J]. Eur Urol, 2022, 81(1): 75-94. DOI: 10.1016/j.eururo.2021.08.010.
[10]
WITJES J A. Follow-up in non-muscle invasive bladder cancer: facts and future[J]. World J Urol, 2021, 39(11): 4047-4053. DOI: 10.1007/s00345-020-03569-2.
[11]
CROCETTO F, BARONE B, FERRO M, et al. Liquid biopsy in bladder cancer: state of the art and future perspectives[J/OL]. Crit Rev Oncol Hematol, 2022, 170: 103577 [2022-07-13]. https://www.sciencedirect.com/science/article/abs/pii/S1040842822000014. DOI: 10.1016/j.critrevonc.2022.103577.
[12]
HENNING G M, BARASHI N S, SMITH Z L. Advances in biomarkers for detection, surveillance, and prognosis of bladder cancer[J]. Clin Genitourin Cancer, 2021, 19(3): 194-198. DOI: 10.1016/j.clgc.2020.12.003.
[13]
LAFATA K J, WANG Y Q, KONKEL B, et al. Radiomics: a primer on high-throughput image phenotyping[J]. Abdom Radiol (NY), 2022, 47(9): 2986-3002. DOI: 10.1007/s00261-021-03254-x.
[14]
XU X, WANG H, GUO Y, et al. Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer[J/OL]. Front Oncol, 2021,11: 704039 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321511/pdf/fonc-11-704039.pdf. DOI: 10.3389/fonc.2021.704039.
[15]
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.
[16]
GUIOT J, VAIDYANATHAN A, DEPREZ L, et al. A review in radiomics: making personalized medicine a reality via routine imaging[J]. Med Res Rev, 2022, 42(1): 426-440. DOI: 10.1002/med.21846.
[17]
SUAREZ-IBARROLA R, HEIN S, REIS G, et al. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer[J]. World J Urol, 2020, 38(10): 2329-2347. DOI: 10.1007/s00345-019-03000-5.
[18]
SCHICK U, LUCIA F, DISSAUX G, et al. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology[J/OL]. Br J Radiol, 2019, 92(1104): 20190105 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913356/pdf/bjr.20190105.pdf. DOI: 10.1259/bjr.20190105.
[19]
ZHANG X, ZHANG Y, ZHANG G, et al. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential[J/OL]. Front Oncol, 2022, 12: 773840 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891653/pdf/fonc-12-773840.pdf. DOI: 10.3389/fonc.2022.773840.
[20]
LU L, EHMKE R C, SCHWARTZ L H, et al. Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings[J/OL]. PLoS One, 2016, 11(12): e0166550 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5199063/pdf/pone.0166550.pdf. DOI: 10.1371/journal.pone.0166550.
[21]
DA-ANO R, VISVIKIS D, HATT M. Harmonization strategies for multicenter radiomics investigations[J/OL]. Phys Med Biol, 2020, 65(24): 24TR02 [2022-07-17]. https://iopscience.iop.org/article/10.1088/1361-6560/aba798. DOI: 10.1088/1361-6560/aba798.
[22]
CUI Y, YIN F F. Impact of image quality on radiomics applications[J/OL]. Phys Med Biol, 2022, 67(15) [2022-07-17]. https://iopscience.iop.org/article/10.1088/1361-6560/ac7fd7. DOI: 10.1088/1361-6560/ac7fd7.
[23]
FAN Y, FENG M, WANG R. Application of Radiomics in Central Nervous System Diseases: a Systematic literature review[J/OL]. Clin Neurol Neurosurg, 2019, 187: 105565 [2022-07-13]. https://sci-hub.st/10.1016/j.clineuro.2019.105565. DOI: 10.1016/j.clineuro.2019.105565.
[24]
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.
[25]
FANG J, HUANG X H, LIU N, et al. Research progress of radiomics in evaluation of curative effect of pancreatic cancer[J]. Chin J Magn Reson Imaging, 2021, 12(10): 105-108. DOI: 10.12015/issn.1674-8034.2021.10.027.
[26]
LITVIN A A, BURKIN D A, KROPINOV A A, et al. Radiomics and digital image texture analysis in oncology (review)[J]. Sovrem Tekhnologii Med, 2021, 13(2): 97-104. DOI: 10.17691/stm2021.13.2.11.
[27]
FUSCO R, GRANATA V, GRAZZINI G, et al. Radiomics in medical imaging: pitfalls and challenges in clinical management[J]. Jpn J Radiol, 2022, 40(9): 919-929. DOI: 10.1007/s11604-022-01271-4.
[28]
LEWIS S, HECTORS S, TAOULI B. Radiomics of hepatocellular carcinoma[J]. Abdom Radiol, 2021, 46(1): 111-123. DOI: 10.1007/s00261-019-02378-5.
[29]
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: 101911 [2022-07-17]. https://sci-hub.se/10.1016/j.compmedimag.2021.101911. DOI: 10.1016/j.compmedimag.2021.101911.
[30]
LIU Y, XU X, WANG H, et al. The Additional Value Evaluation of Tri-parametric MRI in Identifying Muscle-invasive Status in Bladder Cancer[J]. Acad Radiol, 2022, 30(1): 64-76. DOI: 10.1016/j.acra.2022.04.014.
[31]
ZHANG G, XU L L, ZHAO L, et al. CT-based radiomics to predict the pathological grade of bladder cancer[J]. Eur Radiol, 2020, 30(12): 6749-6756. DOI: 10.1007/s00330-020-06893-8.
[32]
ZHENG Z T, XU F J, GU Z R, 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.
[33]
XU S S, YAO Q Y, LIU G Q, et al. Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer[J]. Eur Radiol, 2020, 30(3): 1804-1812. DOI: 10.1007/s00330-019-06484-2.
[34]
ZHENG Z, XU F, GU Z, 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: 619893 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155615/pdf/fonc-11-619893.pdf. DOI: 10.3389/fonc.2021.619893.
[35]
KOZIKOWSKI M, SUAREZ-IBARROLA R, OSIECKI R, et al. Role of radiomics in the prediction of muscle-invasive bladder cancer: a systematic review and meta-analysis[J]. Eur Urol Focus, 2022, 8(3): 728-738. DOI: 10.1016/j.euf.2021.05.005.
[36]
ZHANG S, SONG M, ZHAO Y, 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: 109219 [2022-07-13]. https://sci-hub.st/10.1016/j.ejrad.2020.109219. DOI: 10.1016/j.ejrad.2020.109219.
[37]
WANG H J, XU X P, 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.
[38]
GAO R Z, WEN R, WEN D Y, et al. Radiomics analysis based on ultrasound images to distinguish the tumor stage and pathological grade of bladder cancer[J]. J Ultrasound Med, 2021, 40(12): 2685-2697. DOI: 10.1002/jum.15659.
[39]
ZHENG Z, GU Z, XU F, et al. Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer[J/OL]. Cancer Imaging, 2021, 21(1): 65 [2022-07-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642943/pdf/40644_2021_Article_433.pdf. DOI: 10.1186/s40644-021-00433-3.
[40]
YE F, HU Y, GAO J, et al. Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer[J/OL]. Front Immunol, 2021, 12: 722642 [2022-07-17]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559436/pdf/fimmu-12-722642.pdf. DOI: 10.3389/fimmu.2021.722642.
[41]
CAGLIC I, PANEBIANCO V, VARGAS H A, et al. MRI of bladder cancer: local and nodal staging[J]. J Magn Reson Imaging, 2020, 52(3): 649-667. DOI: 10.1002/jmri.27090.
[42]
WU S X, ZHENG J J, 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]. EBioMedicine, 2018, 34: 76-84. DOI: 10.1016/j.ebiom.2018.07.029.
[43]
WU S X, ZHENG J J, 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.
[44]
LIU S, CHEN X, LIN T X. Lymphatic metastasis of bladder cancer: molecular mechanisms, diagnosis and targeted therapy[J]. Cancer Lett, 2021, 505: 13-23. DOI: 10.1016/j.canlet.2021.02.010.
[45]
WANG Y Q, GAO B, XIA C H, et al. The value of CT-based radiomics modelin differential diagnosis of bladder Papilloma and bladder cancer[J]. J Clin Radiol, 2021, 40(2): 315-319. DOI: 10.13437/j.cnki.jcr.2021.02.026.
[46]
HUA H, GAO Y, LIN J, et al. Quantitative Analysis of Enhanced Computed Tomography in Differentiating Cystitis Glandularis and Bladder Cancer[J/OL]. Biomed Res Int, 2020, 2020: 4930621 [2022-07-13]. https://www.hindawi.com/journals/bmri/2020/4930621/. DOI: 10.1155/2020/4930621.
[47]
CHEN P F, YU Y M, WU Q, et al. Differential diagnosis of cystitis glandularis and bladder cancer based on CT radiomics model[J]. Chin J Interv Imaging Ther, 2021, 18(6): 360-365. DOI: 10.13929/j.issn.1672-8475.2021.06.009.
[48]
CARDENAS C E, YANG J Z, ANDERSON B M, et al. Advances in auto-segmentation[J]. Semin Radiat Oncol, 2019, 29(3): 185-197. DOI: 10.1016/j.semradonc.2019.02.001.
[49]
SHI Z W, LIU Z Y. The challenges and solutions in radiomics study[J]. Chin J Radiol, 2022, 56(1): 9-11. DOI: 10.3760/cma.j.cn112149-20211111-00998.
[50]
SIRACUSANO S, RIZZETTO R, PORCARO A B. Bladder cancer genomics[J]. Urologia, 2020, 87(2): 49-56. DOI: 10.1177/0391560319899011.
[51]
PANUNZIO A, TAFURI A, PRINCIOTTA A, et al. Omics in urology: an overview on concepts, current status and future perspectives[J]. Urologia, 2021, 88(4): 270-279. DOI: 10.1177/03915603211022960.

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