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Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors

Abstract

Objectives

To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors.

Methods

A primary cohort of 70 patients (31 high-grade BCa and 39 low-grade BCa) with BCa were retrospectively enrolled. Three sets of radiomics features were separately extracted from tumor volumes on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Two sets of multimodal features were separately generated by the maxout and concatenation of the above mentioned single-modality features. Each feature set was subjected to a two-sample t test and the least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. Multivariable logistic regression (LR) analysis was used to obtain five corresponding radiomics models. The diagnostic abilities of the radiomics models were evaluated using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. Validation was performed on a time-independent cohort containing 30 consecutive patients.

Results

The areas under the ROC curves (AUCs) of single-modality T2WI, DWI, and ADC models in the training cohort were 0.7933 (95% confidence interval [CI] 0.7471–0.8396), 0.8083 (95% CI 0.7565–0.8601), and 0.8350 (95% CI 0.7924–0.8776), respectively. Both multimodality models achieved higher AUCs (maxout 0.9233, 95% CI 0.9001–0.9466; concatenation 0.9233, 95% CI 0.9001–0.9466) than single-modality models. The AUCs of the maxout and concatenation models in the validation cohort were 0.9186 and 0.9276, respectively.

Conclusions

The MRI-based multiparametric radiomics approach has the potential to be used as a noninvasive imaging tool for preoperative grading of BCa tumors. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Key Points

• Multiparametric MRI may help in the preoperative grading of BCa tumors.

• The Joint_Model established from T2WI, DWI, and ADC feature subsets demonstrated a high diagnostic accuracy for preoperative prediction of pathological grade in BCa tumors.

• The radiomics approach has the potential to preoperatively assess tumor grades in BCa and avoid subjectivity.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

BCa:

Bladder cancer

CI:

Confidence interval

DTI:

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

LASSO:

Least absolute shrinkage and selection operator

MIBCa:

Muscle-invasive bladder cancer

NMIBCa:

Non-muscle-invasive bladder cancer

PWI:

Perfusion-weighted imaging

ROC:

Receiver operating characteristic

TIS:

Tumor in situ

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Acknowledgements

The abstract of this study has been accepted as a scientific oral presentation at the European Congress of Radiology 2019 annual meeting.

Funding

This study has received funding by National Natural Science Foundation of China (81701747) and Natural Science Foundation of Guangdong Province (2017A030313902).

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Corresponding authors

Correspondence to Yanqiu Feng or Yan Guo.

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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.

Guarantor

The scientific guarantors of this publication are Yanqiu Feng and Yan Guo.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board of the First Affiliated Hospital of Sun Yat-sen University.

Ethical approval

Ethical approval was obtained from the Institutional Review Board of The First Affiliated Hospital of Sun Yat-sen University.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cite this article

Wang, H., Hu, D., Yao, H. et al. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol 29, 6182–6190 (2019). https://doi.org/10.1007/s00330-019-06222-8

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Keywords

  • Magnetic resonance imaging
  • Urinary bladder
  • ROC curve
  • Regression analysis