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Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

  • Oncology
  • Published:
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Abstract

Purpose

To investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC).

Methods

This retrospective study included 218 pathologically confirmed bladder cancer patients (training set: 131 patients, 86 MIBC; validation set: 87 patients, 55 MIBC) who underwent DWI before biopsy through transurethral resection (TUR) between July 2014 and December 2018. Radiomics models based on DWI for discriminating state of muscle-invasive were built using random forest (RF) and all-relevant (AR) methods on the training set and were tested on validation set. Combination models based on TUR data were also built. Discrimination performances were evaluated with the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and F1 and F2 scores. Qualitative MRI evaluation based on morphology was performed for comparison.

Results

No significant difference was found between RF and AR models. RF model was more sensitive than TUR (0.873 vs 0.655, p = 0.019) for discriminating muscle-invasive bladder cancer. When combining RF with TUR, the sensitivity increased to 0.964, significantly higher than TUR (0.655, p < 0.001), MRI evaluation (0.764, p = 0.006), and the combination of TUR and MRI (0.836, p = 0.046). Combining RF and TUR achieved the highest accuracy of 0.897 and F2 score of 0.946.

Conclusion

Combining DWI radiomics features with TUR could improve the sensitivity and accuracy in discriminating the presence of muscle invasion in bladder cancer for clinical practice. Multicenter, prospective studies are needed to confirm our results.

Key Points

• Twenty-seven to 51% of superficial bladder cancers diagnosed by transurethral resection are upstaged to muscle-invasive at radical cystectomy, suggesting its poor sensitivity for discriminating muscle-invasive bladder cancer.

• A small subset of selected all-relevant radiomics features exhibited an equivalent performance compared to that of all the extracted features, confirming that radiomics data contained redundant or irrelevant features and that feature selection should be performed in building radiomics models.

• Combining DWI radiomics features with transurethral resection could improve in clinical practice the sensitivity and accuracy for the detection of muscle invasion in bladder cancer.

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Abbreviations

ACC:

Accuracy

AR:

All-relevant

AUC:

Area under the receiver operating characteristic curve

BC:

Bladder cancer

CIS:

Carcinoma in situ

DKI:

Diffusion kurtosis imaging

DTI:

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

ICC:

Intraclass correlation coefficient

MDGini:

Mean Decrease in Gini index

MIBC:

Muscle-invasive bladder cancer

NGTDM:

Neighborhood gray-tone difference matrix

NMIBC:

Non-muscle-invasive bladder cancer

PPV:

Positive predictive value

RC:

Radical cystectomy

RF:

Random forest

ROC:

Receiver operating characteristic

SEN:

Sensitivity

SPE:

Specificity

TUR:

Transurethral resection

VOI:

Volume of interest

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Acknowledgements

The authors thank their colleagues of the department of radiology of their institute.

Funding

This study has received funding by the National Natural Science Foundation of China; contract grant numbers are the following: Youth Program Nos. 81601487 and 81672514.

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Authors and Affiliations

  1. Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, No.1630, Dongfang road, Pudong, Shanghai, 200127, China

    Shuaishuai Xu, Qiuying Yao, Guiqin Liu, Jianrong Xu & Guangyu Wu

  2. Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Di Jin & Haige Chen

  3. Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhicheng Li

  4. Shenzhen Peng Cheng Laboratory, Shenzhen, China

    Zhicheng Li

Authors
  1. Shuaishuai Xu
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  2. Qiuying Yao
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  3. Guiqin Liu
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  4. Di Jin
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  5. Haige Chen
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  6. Jianrong Xu
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  7. Zhicheng Li
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  8. Guangyu Wu
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Corresponding authors

Correspondence to Zhicheng Li or Guangyu Wu.

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Guarantor

The scientific guarantor of this publication is Guangyu Wu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Xu, S., Yao, Q., Liu, G. et al. 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 (2020). https://doi.org/10.1007/s00330-019-06484-2

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