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Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study

European Radiology volume 30, pages 4816–4827 (2020)Cite this article

Abstract

Objectives

To develop a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer (BCa).

Methods

This retrospective study involved 106 eligible patients from two independent clinical centers. All patients underwent a preoperative 3.0 T MRI scan with T2-weighted image (T2WI) and multi-b-value diffusion-weighted image (DWI) sequences. In total, 1404 radiomics features were extracted from the largest region of the reported tumor locations on the T2WI, DWI, and corresponding apparent diffusion coefficient map (ADC) of each patient. A radiomics signature, namely the Radscore, was then generated using the recursive feature elimination approach and a logistic regression algorithm in a training cohort (n = 64). Its performance was then validated in an independent validation cohort (n = 42). The primary imaging and clinical factors in conjunction with the Radscore were used to determine whether the performance could be further improved.

Results

The Radscore, generated by 36 selected radiomics features, demonstrated a favorable ability to predict muscle-invasive BCa status in both the training (AUC 0.880) and validation (AUC 0.813) cohorts. Subsequently, integrating the two independent predictors (including the Radscore and MRI-determined tumor stalk) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.924 and 0.877 in both cohorts, respectively.

Conclusions

The proposed multisequence MRI-based radiomics signature alone could be an effective tool for quantitative prediction of muscle-invasive status of BCa. Integrating the Radscore with MRI-determined tumor stalk could further improve the discriminatory power, realizing more accurate prediction of nonmuscle-invasive and muscle-invasive BCa.

Key Points

• DWI is superior to T2WI sequence in reflecting the heterogeneous differences between NMIBC and MIBC, and multisequence MRI helps in the preoperative prediction of muscle-invasive status of BCa.

• Co-occurrence (CM), run-length matrix (RLM), and gray-level size zone matrix (GLSZM) features were the favorable feature categories for the prediction of muscle-invasive status of BCa.

• The Radscore (proposed multisequence MRI-based radiomics signature) helps predict preoperatively muscle invasion. Combination with the MRI-determined tumor stalk further improves prediction.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

BCa:

Bladder cancer

CM:

Co-occurrence matrices

DCE:

Dynamic contrast enhanced

DWI:

Diffusion-weighted image

GLSZM:

Gray-level size zone matrix

LASSO:

Least absolute shrinkage and selection operator

MIBC:

Muscle-invasive bladder carcinoma

MRI:

Magnetic resonance imaging

NAC:

Neoadjuvant chemotherapy

NGTDM:

Neighborhood gray-tone difference matrix

NMIBC:

Nonmuscle-invasive bladder carcinoma

RLM:

Run-length matrix

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM-RFE:

Support vector machine-based recursive feature elimination

T1WI:

T1-weighted image

T2WI:

T2-weighted image

TUR:

Transurethral resection

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Funding

This research received financial support from the National Natural Science Foundation of China under grant numbers 81701747, 81871424, and 81901698; the National Key Research and Development Program of China under grant number 2017YFC0107400; the Military Science and Technology Foundation under grant number BWS14C030; and the Natural Science Foundation of Guangdong Province under grant number 2017A030313902.

Author information

Author notes

  1. Huanjun Wang and Xiaopan Xu have contributed equally to this work, and they are co-first authors.

Affiliations

  1. Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, Guangdong, People’s Republic of China

    Huanjun Wang, Longyuan Ouyang, Shurong Li, Jian Ling & Yan Guo

  2. School of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), No. 169 Changle West Road, Xi’an, 710032, Shaanxi, People’s Republic of China

    Xiaopan Xu, Xi Zhang, Yang Liu, Peng Du & Hongbing Lu

  3. Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi’an, Shaanxi, People’s Republic of China

    Qiang Tian

Authors
  1. Huanjun Wang
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  2. Xiaopan Xu
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  3. Xi Zhang
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  4. Yang Liu
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  10. Yan Guo
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  11. Hongbing Lu
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Corresponding authors

Correspondence to Yan Guo or Hongbing Lu.

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Guarantor

The scientific guarantors of this publication are Yan Guo and Hongbing Lu.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived from all subjects in this retrospective study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Wang, H., Xu, X., Zhang, X. et al. Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study. Eur Radiol 30, 4816–4827 (2020). https://doi.org/10.1007/s00330-020-06796-8

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  • DOI: https://doi.org/10.1007/s00330-020-06796-8

Keywords

  • MRI
  • Bladder cancer
  • Diffusion-weighted image
  • Apparent diffusion coefficient
  • Logistic regression algorithm