Advances in deep learning and Radiomics for precision diagnosis and treatment of bladder cancer

Share this content in WeChat

• Review •

WANG Dong ZHOU Chuan WANG Chao ZHANG Yunfeng GUO Sheng ZHOU Fenghai

WANG D, ZHOU C, WANG C, et al. Advances in deep learning and Radiomics for precision diagnosis and treatment of bladder cancer[J]. Chin J Magn Reson Imaging, 2023, 14(9): 186-191. DOI:10.12015/issn.1674-8034.2023.09.034.

Abstract

[Abstract] In recent years, the incidence of bladder cancer (BCa) has been increasing year by year and has become one of the important factors threatening the health of middle-aged and elderly people, and the early detection and prognosis monitoring of BCa has increasingly become a hot spot of current research. Radiomics is a high-throughput quantitative feature extraction method that mines the information contained in multimodal medical images, then synthesises these massive images to extract phenotypic features and explores the relationship between patient prognosis and these extracted features. Deep learning is a representation learning approach in which complex multilayer neural network architectures automatically learn data representations by transforming input information into multi-level abstractions. This paper reviews the research progress of radiomics and deep learning in precision diagnosis and treatment of BCa from the perspective of urological clinicians, including pathological grading and staging prediction, tumour lymph node metastasis prediction and efficacy assessment, and provides an outlook on future research directions.

[Keywords] bladder cancer；radiomics；deep learning；magnetic resonance imaging；precision medicine

Contributor Information

WANG Dong^{1,
3}   ZHOU Chuan²   WANG Chao²   ZHANG Yunfeng^{1,
3}   GUO Sheng^{1,
3}   ZHOU Fenghai^{1,
3*}

¹ The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou 730000, China

² The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, China

³ Department of Urology, Gansu Provincial People's Hospital, Lanzhou 730000, China

Corresponding author: Zhou FH, E-mail: ldyy_zhoufh@lzu.edu.cn

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS Natural Science Foundation of Gansu Province (No. 22JR5RA650); Key Science and Technology Program in Gansu Province (No. 21YF5FA016); Internal Fund of Gansu Provincial People's Hospita (No. 22GSSYD-15).

Received 2023-04-11

Accepted 2023-08-09

DOI: 10.12015/issn.1674-8034.2023.09.034

Text

References

[1]

SUNG H, FERLAY J, SIEGEL R L, 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.

[2]

SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2018[J]. CA, 2018, 68(1): 7-30. DOI: 10.3322/caac.21442.

[3]

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.

[4]

XU X P, LIU Y, ZHANG X, et al. Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps[J]. Abdom Radiol, 2017, 42(7): 1896-1905. DOI: 10.1007/s00261-017-1079-6.

[5]

ISFOSS B. The sensitivity of fluorescent-light cystoscopy for the detection of carcinoma in situ (CIS) of the bladder: a meta-analysis with comments on gold standard[J]. BJU Int, 2011, 108(11): 1703-1707. DOI: 10.1111/j.1464-410X.2011.10485.x.

[6]

National Institute for Health and Care Excellence. Bladder cancer: diagnosis and management of bladder cancer[J]. BJU Int, 2017, 120(6): 755-765. DOI: 10.1111/bju.14045.

[7]

WU J, LU A D, ZHANG L P, et al. Study of clinical outcome and prognosis in pediatric core binding factor-acute myeloid leukemia[J]. Chin J Hematol, 2019, 40(1): 52-57. DOI: 10.3760/cma.j.issn.0253-2727.2019.01.010.

[8]

AVANZO M, STANCANELLO J, NAQA I EL. Beyond imaging: the promise of radiomics[J]. Phys Med, 2017, 38: 122-139. DOI: 10.1016/j.ejmp.2017.05.071.

[9]

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.

[10]

LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.

[11]

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.

[12]

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.

[13]

ZHANG X, XU X P, 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.

[14]

FENG C, ZHOU Z L, HUANG Q H, et al. Radiomics nomogram based on high-b-value diffusion-weighted imaging for distinguishing the grade of bladder cancer[J/OL]. Life, 2022, 12(10): 1510 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC9604764. DOI: 10.3390/life12101510.

[15]

CARUSO G, SALVAGGIO G, CAMPISI A, et al. Bladder tumor staging: comparison of contrast-enhanced and gray-scale ultrasound[J]. AJR Am J Roentgenol, 2010, 194(1): 151-156. DOI: 10.2214/AJR.09.2741.

[16]

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.

[17]

FAJKOVIC H, CHA E K, JELDRES C, et al. Extranodal extension is a powerful prognostic factor in bladder cancer patients with lymph node metastasis[J]. Eur Urol, 2013, 64(5): 837-845. DOI: 10.1016/j.eururo.2012.07.026.

[18]

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.

[19]

GRESSER E, WOŹNICKI P, MESSMER K, et al. Radiomics signature using manual versus automated segmentation for lymph node staging of bladder cancer[J]. Eur Urol Focus, 2023, 9(1): 145-153. DOI: 10.1016/j.euf.2022.08.015.

[20]

STARMANS M P A, HO L S, SMITS F, et al. Optimization of preoperative lymph node staging in patients with muscle-invasive bladder cancer using radiomics on computed tomography[J/OL]. J Pers Med, 2022, 12(5): 726 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC9147130. DOI: 10.3390/jpm12050726.

[21]

MCKIBBEN M J, WOODS M E. Preoperative imaging for staging bladder cancer[J/OL]. Curr Urol Rep, 2015, 16(4): 22 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/25724433. DOI: 10.1007/s11934-015-0496-8.

[22]

VERMA S, RAJESH A, PRASAD S R, et al. Urinary bladder cancer: role of MR imaging[J]. Radiographics, 2012, 32(2): 371-387. DOI: 10.1148/rg.322115125.

[23]

KOBAYASHI S, KOGA F, YOSHIDA S, et al. Diagnostic performance of diffusion-weighted magnetic resonance imaging in bladder cancer: potential utility of apparent diffusion coefficient values as a biomarker to predict clinical aggressiveness[J]. Eur Radiol, 2011, 21(10): 2178-2186. DOI: 10.1007/s00330-011-2174-7.

[24]

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.

[25]

ZHENG J J, KONG J Q, WU S X, 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.

[26]

ZHANG G, WU Z, ZHANG X X, et al. CT-based radiomics to predict muscle invasion in bladder cancer[J]. Eur Radiol, 2022, 32(5): 3260-3268. DOI: 10.1007/s00330-021-08426-3.

[27]

LIU Y, XU X P, WANG H J, et al. The additional value of tri-parametric MRI in identifying muscle-invasive status in bladder cancer[J]. Acad Radiol, 2023, 30(1): 64-76. DOI: 10.1016/j.acra.2022.04.014.

[28]

ZHENG Z T, GU Z R, XU F J, 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 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC8642943. DOI: 10.1186/s40644-021-00433-3.

[29]

FAN X H, YU H W, NI X, et al. Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer[J/OL]. Br J Radiol, 2023, 96(1145): 20221086 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC10161909. DOI: 10.1259/bjr.20221086.

[30]

ZHENG Z T, GUO Y D, HUANG X S, et al. CD8A as a prognostic and immunotherapy predictive biomarker can be evaluated by MRI radiomics features in bladder cancer[J/OL]. Cancers, 2022, 14(19): 4866 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC9564077. DOI: 10.3390/cancers14194866.

[31]

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.

[32]

BABJUK M, BURGER M, COMPÉRAT E M, et al. European association of urology guidelines on non-muscle-invasive bladder cancer (TaT1 and carcinoma in situ) - 2019 update[J]. Eur Urol, 2019, 76(5): 639-657. DOI: 10.1016/j.eururo.2019.08.016.

[33]

WOŹNICKI P, LAQUA F C, MESSMER K, et al. Radiomics for the prediction of overall survival in patients with bladder cancer prior to radical cystectomy[J/OL]. Cancers, 2022, 14(18): 4449 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC9497387. DOI: 10.3390/cancers14184449.

[34]

XU X P, WANG H J, 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.

[35]

QIAN J, YANG L, HU S, et al. Feasibility study on predicting recurrence risk of bladder cancer based on radiomics features of multiphase CT images[J/OL]. Front Oncol, 2022, 12: 899897 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC9201948. DOI: 10.3389/fonc.2022.899897.

[36]

TANG X, QIAN W L, YAN W F, et al. Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study[J/OL]. BMC Cancer, 2021, 21(1): 823 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC8285848. DOI: 10.1186/s12885-021-08569-y.

[37]

CHOI S J, PARK K J, HEO C, et al. Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer[J/OL]. Clin Radiol, 2021, 76(8): 627.e13-e21 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/33762138. DOI: 10.1016/j.crad.2021.03.001.

[38]

PARK K J, LEE J L, YOON S K, 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.

[39]

LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.

[40]

MONSHI M M A, POON J, CHUNG V. Deep learning in generating radiology reports: a survey[J/OL]. Artif Intell Med, 2020, 106: 101878 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC7227610. DOI: 10.1016/j.artmed.2020.101878.

[41]

ZHENG Q Y, YANG R, NI X M, et al. Accurate diagnosis and survival prediction of bladder cancer using deep learning on histological slides[J/OL]. Cancers, 2022, 14(23): 5807 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/PMC9737237. DOI: 10.3390/cancers14235807.

[42]

YANG R, DU Y, WENG X D, et al. Automatic recognition of bladder tumours using deep learning technology and its clinical application[J/OL]. Int J Med Robot, 2021, 17(2): e2194 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/33119212. DOI: 10.1002/rcs.2194.

[43]

SHKOLYAR E, JIA X, CHANG T C, et al. Augmented bladder tumor detection using deep learning[J]. Eur Urol, 2019, 76(6): 714-718. DOI: 10.1016/j.eururo.2019.08.032.

[44]

CHAN E O T, PRADERE B, TEOH J Y C, et al. The use of artificial intelligence for the diagnosis of bladder cancer: a review and perspectives[J]. Curr Opin Urol, 2021, 31(4): 397-403. DOI: 10.1097/MOU.0000000000000900.

[45]

BANDYK M G, GOPIREDDY D R, LALL C, et al. MRI and CT bladder segmentation from classical to deep learning based approaches: current limitations and lessons[J/OL]. Comput Biol Med, 2021, 134: 104472 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/34023696. DOI: 10.1016/j.compbiomed.2021.104472.

[46]

YU J, CAI L K, CHEN C X, et al. Cascade Path Augmentation Unet for bladder cancer segmentation in MRI[J]. Med Phys, 2022, 49(7): 4622-4631. DOI: 10.1002/mp.15646.

[47]

MA X Y, HADJIISKI L M, WEI J, et al. U-Net based deep learning bladder segmentation in CT urography[J]. Med Phys, 2019, 46(4): 1752-1765. DOI: 10.1002/mp.13438.

[48]

ZOU Y, CAI L K, CHEN C X, et al. Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer[J/OL]. Comput Biol Med, 2022, 151(Pt A): 106219 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/36343408. DOI: 10.1016/j.compbiomed.2022.106219.

[49]

ZHOU X Q, YUE X D, XU Z K, et al. PENet: prior evidence deep neural network for bladder cancer staging[J]. Methods, 2022, 207: 20-28. DOI: 10.1016/j.ymeth.2022.08.010.

[50]

LUCAS M, JANSEN I, VAN LEEUWEN T G, et al. Deep learning-based recurrence prediction in patients with non-muscle-invasive bladder cancer[J]. Eur Urol Focus, 2022, 8(1): 165-172. DOI: 10.1016/j.euf.2020.12.008.

[51]

WU E, HADJIISKI L M, SAMALA R K, et al. Deep learning approach for assessment of bladder cancer treatment response[J]. Tomography, 2019, 5(1): 201-208. DOI: 10.18383/j.tom.2018.00036.

[52]

CHA K H, HADJIISKI L M, COHAN R H, et al. Diagnostic accuracy of CT for prediction of bladder cancer treatment response with and without computerized decision support[J]. Acad Radiol, 2019, 26(9): 1137-1145. DOI: 10.1016/j.acra.2018.10.010.

[53]

LI J P, QIU Z X, CAO K Y, et al. Predicting muscle invasion in bladder cancer based on MRI: a comparison of radiomics, and single-task and multi-task deep learning[J/OL]. Comput Methods Programs Biomed, 2023, 233: 107466 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/36907040. DOI: 10.1016/j.cmpb.2023.107466.

[54]

NAPEL S, GIGER M. Special section guest editorial: radiomics and imaging genomics: quantitative imaging for precision medicine[J/OL]. J Med Imaging, 2015, 2(4): 041001 [2023-02-30]. https://pubmed.ncbi.nlm.nih.gov/36300676. DOI: 10.1117/1.JMI.2.4.041001.

PREV Research progress of multimodal magnetic resonance imaging in evaluating preoperative staging, restaging after chemoradiotherapy and efficacy of chemoradiotherapy in rectal cancer

NEXT Research status and progress in the application of MRI quantitative techniques in osteoporosis

LINK

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