Abstract
Objectives
This study investigated the feasibility of a computed tomography (CT)-based radiomics prediction model to evaluate muscle invasive status in bladder cancer.
Methods
Patients who underwent CT urography at two medical centers from October 2014 to May 2020 and had bladder urothelial carcinoma confirmed by postoperative histopathology were retrospectively enrolled. In total, 441 cases were collected and randomized into a training cohort (n = 293), an internal testing cohort (n = 73), and an external testing cohort (n = 75). The images were first filtered, and then, 1218 features were extracted. The best features related to muscle invasiveness of bladder cancer were identified by ANOVA. A prediction model was built by using the logistic regression method. Statistical analysis was performed by plotting the receiver operating characteristic curve. Indicators of the diagnostic performance of the prediction model, including sensitivity, specificity, accuracy, and area under curve (AUC), were evaluated.
Results
In the training, internal testing, and external testing cohorts, the prediction model diagnosed muscle-invasive bladder cancer with AUCs of 0.885 (95% confidence interval [95% CI] 0.841–0.929), 0.820 (95% CI 0.698–0.941), and 0.784 (95% CI 0.674–0.893), respectively. In the internal testing cohort, the sensitivity, specificity, and accuracy of the model were 0.667 (95% CI 0.387–0.870), 0.845 (95% CI 0.721–0.922), and 0.782 (95% CI 0.729–0.827), respectively. In the external testing cohort, the sensitivity, specificity, and accuracy of the model were 0.742 (95% CI 0.551–0.873), 0.750 (95% CI 0.594–0.863), and 0.782 (95% CI 0.729–0.827), respectively.
Conclusions
CT-based radiomics prediction model can evaluate muscle invasiveness of bladder cancer before surgery with a good diagnostic performance.
Key Points
• CT-based radiomics model can evaluate muscle invasive status in bladder cancer.
• The radiomics model shows good diagnostic performance to differentiate muscle-invasive bladder cancer from non-muscle-invasive bladder cancer.
• This preoperative CT-based prediction method might complement MR evaluation of bladder cancer and supplement biopsy.
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Abbreviations
- ADC:
-
Apparent diffusion coefficient
- AUC:
-
Area under the curve
- BCa:
-
Bladder cancer
- CI:
-
Confidence interval
- CT:
-
Computed tomography
- CTU:
-
CT urography
- DSDE:
-
Dual-source dual-energy
- GLCM:
-
Gray-level co-occurrence matrix
- GLDM:
-
Gray-level dependence matrix
- GLRLM:
-
Gray-level run-length matrix
- GLSZM:
-
Gray-level size zone matrix
- MIBC:
-
Muscle-invasive BCa
- NMIBC:
-
Non-muscle-invasive BCa
- ROC:
-
Receiver operating characteristic
- ROI:
-
Region of interest
- TURBT:
-
Transurethral resection of bladder tumor
References
Bray F, Ferlay J, Soerjomataram I et al (2018) Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424
Wong MC, Fung FDH, Leung C, Cheung WWL, Goggins WB, Ng ACF (2018) The global epidemiology of bladder cancer: a joinpoint regression analysis of its incidence and mortality trends and projection. Sci Rep 8:1129
Roupret M, Babjuk M, Comperat E et al (2017) European Association of Urology guidelines on upper urinary tract urothelial carcinoma: update. Eur Urol 73:111–122
Humphrey PA, Moch H, Cubilla AL et al (2016) The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs—Part B: prostate and bladder tumours. Eur Urol 70:106–119
Kamat AM, Hahn NM, Efstathiou JA et al (2016) Bladder cancer. Lancet 388:2796–2810
Bellmunt J, Orsola A, Leow JJ et al (2014) Bladder cancer: ESMO Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 25(Suppl 3):iii40-48
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 63:321–332
Arendt CT, Leithner D, Mayerhoefer ME et al (2021) Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study. Eur Radiol 31:4071–4078
Martini K, Baessler B, Bogowicz M et al (2021) Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept. Eur Radiol 31:1987–1998
Ursprung S, Beer L, Bruining A et al (2020) Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. Eur Radiol 30:3558–3566
Fang X, Li X, Bian Y, Ji X, Lu J (2020) Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2. Eur Radiol 30:6888–6901
Zhang G, Xu L, Zhao L et al (2020) CT-based radiomics to predict the pathological grade of bladder cancer. Eur Radiol 30:6749–6756
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107
Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238
He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969
Spiess PE, Agarwal N, Bangs R et al (2017) Bladder Cancer, Version 5.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 15:1240–1267
Ueno Y, Takeuchi M, Tamada T et al (2019) Diagnostic accuracy and interobserver agreement for the vesical imaging-reporting and data system for muscle-invasive bladder cancer: a multireader validation study. Eur Urol 76:54–56
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 23:6904–6911
Zhang GM, Sun H, Shi B et al (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY) 42:561–568
Garapati SS, Hadjiiski L, Cha KH et al (2017) Urinary bladder cancer staging in CT urography using machine learning. Med Phys 44:5814–5823
Zheng J, Kong J, Wu S et al (2019) Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach. Cancer 125:4388–4398
Wang H, Xu X, Zhang X et al (2020) Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study. Eur Radiol 30:4816–4827
Xu S, Yao Q, Liu G et al (2020) Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer. Eur Radiol 30:1804–1812
Xu X, Zhang X, Tian Q et al (2019) Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. J Magn Reson Imaging 49:1489–1498
Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61:488–495
Acknowledgements
We would like to thank Deepwise AI Lab, Deepwise Inc. for their technical support in this study.
Funding
This study has received funding by the National Natural Science Foundation of China (81901742, 91859119); the Natural Science Foundation of Beijing Municipality (7192176); the Clinical and Translational Research Project of Chinese Academy of Medical Sciences (XK320028); and the National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences (2018PT32003, 2019PT320008).
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The scientific guarantor of this publication is Hao Sun.
Conflict of interest
The authors of this manuscript declare relationships with the following companies: Li Mao and Xiuli Li are employees of Deepwise AI Lab, Deepwise Inc., which contributed to the development of radiomics models described in the study. All remaining authors have declared no conflicts of interest.
Statistics and biometry
One of the authors has significant statistical expertise.
Informed consent
Written informed consent was waived by the Institutional Review Board.
Ethical approval
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects have been previously reported in the study of “CT-based radiomics to predict the pathological grade of bladder cancer” published in European Radiology (doi: https://doi.org/10.1007/s00330-020-06893-8), and the study entitled “Deep learning on enhanced CT images can predict the muscular invasiveness of bladder cancer” published in Frontiers in oncology (doi: https://doi.org/10.3389/fonc.2021.654685). The article published in European Radiology focused on pathological grade of bladder cancer while the article published in Frontiers in oncology applied deep learning technique rather than radiomics. Both studies were different from the present study.
Methodology
• retrospective
• diagnostic or prognostic study
• performed at multicenter
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Zhang, G., Wu, Z., Zhang, X. et al. CT-based radiomics to predict muscle invasion in bladder cancer. Eur Radiol 32, 3260–3268 (2022). https://doi.org/10.1007/s00330-021-08426-3
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DOI: https://doi.org/10.1007/s00330-021-08426-3