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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

  • Shuaishuai Xu
  • Qiuying Yao
  • Guiqin Liu
  • Di Jin
  • Haige Chen
  • Jianrong Xu
  • Zhicheng LiEmail author
  • Guangyu WuEmail author
Oncology
  • 46 Downloads

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.

Keywords

Urinary bladder cancer Magnetic resonance imaging Radiomics 

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

Notes

Acknowledgements

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

Funding information

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.

Compliance with ethical standards

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.

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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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology, Ren Ji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Urology, Ren Ji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  4. 4.Shenzhen Peng Cheng LaboratoryShenzhenChina

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