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Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Pathological grade is important for the treatment selection and outcome prediction in bladder cancer (BCa). We aimed to construct a radiomics-clinical nomogram to preoperatively differentiate high-grade BCa from low-grade BCa.

Methods

A total of 185 BCa patients who received multiparametric MRI (mpMRI) before surgery between August 2014 and April 2020 were enrolled in our study. Radiomics features were extracted from the largest tumor located on dynamic contrast-enhancement and T2WI images. After feature selection, the synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (low-grade group). Radiomics signatures were constructed in the training set and assessed in the validation set. Univariable and multivariable logistic regression were applied to build a nomogram.

Results

The radiomics signature generated by the least absolute shrinkage and selection operator model achieved the optimal performance for BCa grading in both the SMOTE-balanced training [accuracy: 93.2%, area under the curve (AUC): 0.961] and validation sets (accuracy: 89.9%, AUC: 0.952). A radiomics-clinical nomogram incorporating the radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score had novel calibration and discrimination both in the training (AUC: 0.956) and validation sets (AUC: 0.958). Decision curve analysis presented the clinical utility of the nomogram for decision-making.

Conclusions

The mpMRI-based radiomics signature had the potential to preoperatively predict the pathological grade of BCa. The proposed nomogram combining the radiomics signature with the VI-RADS score improved the diagnostic power, which may aid in clinical decision-making.

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Data availability

The processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Code availability

The analytic code is available on request to the corresponding author.

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Funding

This work was supported by the Outstanding Talent of Shanghai Tenth People's Hospital (20215YPDRC048), the Natural Science Foundation of China (81472389), the Shanghai Science Committee Foundation (19411967700), and the Shanghai Youth Science and Technology Talents Sailing Program (20YF1437200).

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Author notes

  1. Zongtai Zheng and Feijia Xu contributed equally to this paper and should be considered co-first authors.

Authors and Affiliations

  1. Department of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Yan Chang Zhong Road 301, Shanghai, 200072, China

    Zongtai Zheng, Zhuoran Gu, Tianyuan Xu, Shenghua Liu & Xudong Yao

  2. Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China

    Feijia Xu, Yang Yan & Tianyuan Xu

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Contributions

Y.Y, S.L and Z.Z contributed to the study design. Z.Z, S.L, F.X and Z.G contributed to data collection. Z.Z, F.X, Y.Y and T.X performed the statistical analyses. Z.Z and F.X wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Shenghua Liu or Xudong Yao.

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The study was reviewed and approved by the Ethics Committee of Shanghai Tenth People’s Hospital (approval number SHSY-IEC-4.1/19–120/01) and conducted in accordance with the ethical standards.

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Written informed consent was waived due to its retrospective nature.

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Cite this article

Zheng, Z., Xu, F., Gu, Z. et al. Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading. Abdom Radiol 46, 4311–4323 (2021). https://doi.org/10.1007/s00261-021-03108-6

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