Top

European Radiology

Published in:

01-12-2020 | Computed Tomography | Urogenital

CT-based radiomics to predict the pathological grade of bladder cancer

Authors: Gumuyang Zhang, Lili Xu, Lun Zhao, Li Mao, Xiuli Li, Zhengyu Jin, Hao Sun

Published in: European Radiology | Issue 12/2020

Abstract

Objective

To build a CT-based radiomics model to predict the pathological grade of bladder cancer (BCa) preliminarily.

Methods

Patients with surgically resected and pathologically confirmed BCa and who received CT urography (CTU) in our institution from October 2014 to September 2017 were retrospectively enrolled and randomly divided into training and validation groups. After feature extraction, we calculated the linear dependent coefficient between features to eliminate the collinearity. F-test was then used to identify the best features related to pathological grade. The logistic regression method was used to build the prediction model, and diagnostic performance was analyzed by plotting receiver operating characteristic (ROC) curve and calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

Out of 145 included patients, 108 constituted the training group and 37 the validation group. The AUC value of the radiomics prediction model to diagnose the pathological grade of BCa was 0.950 (95% confidence interval [CI] 0.912–0.988) in the training group and 0.860 (95% CI 0.742–0.979) in the validation group, respectively. In the validation group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 83.8%, 88.5%, 72.7%, 88.5%, and 72.7%, respectively.

Conclusions

CT-based radiomics model can differentiate high-grade from low-grade BCa with a fairly good diagnostic performance.

Key Points

•CT-based radiomics model can predict the pathological grade of bladder cancer.

•This model has good diagnostic performance to differentiate high-grade and low-grade bladder cancer.

•This preoperative and non-invasive prediction method might become an important addition to biopsy.

Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F (2017) Bladder cancer incidence and mortality: a global overview and recent trends. Eur Urol 71:96–108CrossRefPubMed

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424CrossRefPubMed

Roupret M, Babjuk M, Comperat E et al (2018) European Association of Urology guidelines on upper urinary tract urothelial carcinoma: 2017 update. Eur Urol 73:111–122CrossRefPubMed

Humphrey PA, Moch H, Cubilla AL, Ulbright TM, Reuter VE (2016) The 2016 WHO classification of tumours of the urinary system and male genital organs-part B: prostate and bladder tumours. Eur Urol 70:106–119CrossRefPubMed

Fiuk JV, Schwartz BF (2016) Upper tract urothelial carcinoma: paradigm shift towards nephron sparing management. World J Nephrol 5:158–165CrossRefPubMedPubMedCentral

Reis LO, Taheri D, Chaux A et al (2016) Significance of a minor high-grade component in a low-grade noninvasive papillary urothelial carcinoma of bladder. Hum Pathol 47:20–25CrossRefPubMed

Klaassen Z, Kamat AM, Kassouf W et al (2018) Treatment strategy for newly diagnosed T1 high-grade bladder urothelial carcinoma: new insights and updated recommendations. Eur Urol 74:597–608CrossRefPubMed

Palou J, Sylvester RJ, Faba OR et al (2012) Female gender and carcinoma in situ in the prostatic urethra are prognostic factors for recurrence, progression, and disease-specific mortality in T1G3 bladder cancer patients treated with Bacillus Calmette-Guerin. Eur Urol 62:118–125CrossRefPubMed

Leblanc B, Duclos AJ, Benard F et al (1999) Long-term followup of initial Ta grade 1 transitional cell carcinoma of the bladder. J Urol 162:1946–1950CrossRefPubMed

10.

Gudjonsson S, Adell L, Merdasa F et al (2009) Should all patients with non-muscle-invasive bladder cancer receive early Intravesical chemotherapy after transurethral resection? The results of a prospective randomised multicentre study. Eur Urol 55:773–780CrossRefPubMed

11.

Babjuk M, Bohle A, Burger M et al (2017) EAU guidelines on non-muscle-invasive urothelial carcinoma of the bladder: update 2016. Eur Urol 71:447–461CrossRefPubMed

12.

Hansel DE, Amin MB, Comperat E et al (2013) A contemporary update on pathology standards for bladder cancer: transurethral resection and radical cystectomy specimens. Eur Urol 63:321–332CrossRefPubMed

13.

Zhang GM, Sun H, Shi B, Jin ZY, Xue HD (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY) 42:561–568CrossRef

14.

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed

15.

Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentral

16.

Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262CrossRefPubMed

17.

Barchetti G, Simone G, Ceravolo I et al (2019) 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. Eur Radiol 29:5498–5506CrossRefPubMed

18.

Xu S, Yao Q, Liu G et al (2019) Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer. Eur Radiol. https://doi.org/10.1007/s00330-019-06484-2

19.

Wang F, Chen HG, Zhang RY et al (2019) Diffusion kurtosis imaging to assess correlations with clinicopathologic factors for bladder cancer: a comparison between the multi-b value method and the tensor method. Eur Radiol 29:4447–4455CrossRefPubMed

20.

Zhang X, Xu XP, Tian Q et al (2017) Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging 46:1281–1288CrossRefPubMedPubMedCentral

21.

Wang H, Hu D, Yao H et al (2019) Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol. https://doi.org/10.1007/s00330-019-06222-8

22.

Garapati SS, Hadjiiski L, Cha KH et al (2017) Urinary bladder cancer staging in CT urography using machine learning. Med Phys 44:5814–5823CrossRefPubMed

23.

Cha KH, Hadjiiski Ph DL, Cohan Md RH et al (2019) Diagnostic accuracy of CT for prediction of bladder cancer treatment response with and without computerized decision support. Acad Radiol 26:1137–1145CrossRefPubMed

24.

Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23:6904–6911CrossRefPubMed

25.

Mammen S, Krishna S, Quon M et al (2018) Diagnostic accuracy of qualitative and quantitative computed tomography analysis for diagnosis of pathological grade and stage in upper tract urothelial cell carcinoma. J Comput Assist Tomogr 42:204–210CrossRefPubMed

26.

Lin P, Wen DY, Chen L et al (2020) A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma. Eur Radiol 30:547–557CrossRefPubMed

27.

Cowan NC (2012) CT urography for hematuria. Nat Rev Urol 9:218–226CrossRefPubMed

28.

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRefPubMedPubMedCentral

29.

Meyer M, Ronald J, Vernuccio F et al (2019) Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings. Radiology 293:583–591CrossRefPubMed

30.

Wakai K, Utsumi T, Yoneda K et al (2018) Development and external validation of a nomogram to predict high-grade papillary bladder cancer before first-time transurethral resection of the bladder tumor. Int J Clin Oncol 23:957–964CrossRefPubMed

31.

Morera DS, Lahorewala SS, Belew D et al (2019) Clinical parameters outperform molecular subtypes for predicting outcome in bladder cancer: results from multiple cohorts including TCGA. J Urol. https://doi.org/10.1097/JU.0000000000000351:101097JU0000000000000351

Title: CT-based radiomics to predict the pathological grade of bladder cancer
Authors: Gumuyang Zhang
Lili Xu
Lun Zhao
Li Mao
Xiuli Li
Zhengyu Jin
Hao Sun
Publication date: 01-12-2020
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Published in: European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06893-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

CT-based radiomics to predict the pathological grade of bladder cancer

Abstract

Objective

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 12/2020

Quality of same-day CT colonography following incomplete optical colonoscopy

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm

Post-processing of computed tomography perfusion in patients with acute cerebral ischemia: variability of inter-reader, inter-region of interest, inter-input model, and inter-software

Forensic age estimation in living adolescents with CT imaging of the clavicula—impact of low-dose scanning on readers’ confidence

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas