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CT-based radiomics to predict muscle invasion in bladder cancer

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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

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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).

Author information

Author notes

  1. Gumuyang Zhang and Zhe Wu have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China

    Gumuyang Zhang, Xiaoxiao Zhang, Lili Xu, Hao Sun & Zhengyu Jin

  2. Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, , Liaoning Province, China

    Zhe Wu

  3. Deepwise AI Lab, Deepwise Inc., Beijing, China

    Li Mao & Xiuli Li

  4. Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China

    Yu Xiao

  5. Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China

    Zhigang Ji

Authors
  1. Gumuyang Zhang
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  2. Zhe Wu
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Corresponding authors

Correspondence to Hao Sun or Zhengyu Jin.

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Guarantor

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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Cite this article

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

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