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

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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.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

BCa:

Bladder cancer

CI:

Confidence interval

CT:

Computed tomography

CTTA:

CT texture analysis

CTU:

CT urography

DWI:

Diffusion-weighted imaging.

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

ICC:

Interclass correlation coefficients

MIBC:

Muscle-invasive BCa

NMIBC:

Non-muscle-invasive BCa

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

ROI:

Region of interest

T2WI:

T2-weighted imaging

TURBT:

Transurethral resection of bladder tumor

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Funding

This study has received funding from the National Natural Science Foundation of China (Grant No. 81901742), the Natural Science Foundation of Beijing Municipality (Grant No. 7192176), the Clinical and Translational Research Project of Chinese Academy of Medical Sciences (Grant No. 2019XK320028), the National Natural Science Foundation of China (Grant No. 91859119), and the National Public Welfare Basic Scientific Research Project of Chinese Academy of Medical Sciences (Grant Nos. 2018PT32003 and 2019PT320008).

Author information

Author notes

  1. Gumuyang Zhang and Lili Xu contributed equally to this work.

Authors and Affiliations

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

    Gumuyang Zhang, Lili Xu, Zhengyu Jin & Hao Sun

  2. Deepwise AI Lab, Deepwise Inc., Haidian Avenue No. 8, Sinosteel International Plaza, Beijing, 100080, China

    Lun Zhao, Li Mao & Xiuli Li

Authors
  1. Gumuyang Zhang
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  2. Lili Xu
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  3. Lun Zhao
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  4. Li Mao
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  5. Xiuli Li
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  6. Zhengyu Jin
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  7. Hao Sun
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Corresponding authors

Correspondence to Zhengyu Jin or Hao Sun.

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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 company: Deepwise Inc. Lun Zhao, Li Mao, and Xiuli Li are employees of Deepwise Inc., which contributed to the development of radiomics models described in the study.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Hao Sun is the first corresponding author and Zhengyu Jin is the second corresponding author of this work.

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Cite this article

Zhang, G., Xu, L., Zhao, L. et al. CT-based radiomics to predict the pathological grade of bladder cancer. Eur Radiol 30, 6749–6756 (2020). https://doi.org/10.1007/s00330-020-06893-8

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  • DOI: https://doi.org/10.1007/s00330-020-06893-8

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