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Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps

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Abstract

Purpose

To determine radiomic features which are capable of reflecting muscular invasiveness of bladder cancer (BC) and propose a non-invasive strategy for the differentiation of muscular invasiveness preoperatively.

Methods

Sixty-eight patients with clinicopathologically confirmed BC were included in this retrospective study. A total of 118 cancerous volumes of interest (VOI) were segmented from patients’ T2 weighted MR images (T2WI), including 34 non-muscle invasive bladder carcinomas (NMIBCs, stage <T2) and 84 muscle invasive ones (MIBCs, stage ≥T2). The radiomic features quantifying tumor signal intensity and textures were extracted from each VOI and its high-order derivative maps to characterize heterogeneity of tumor tissues. Statistical analysis was used to build radiomic signatures with significant inter-group differences of NMIBCs and MIBCs. The synthetic minority oversampling technique (SMOTE) and a support vector machine (SVM)-based feature selection and classification strategy were proposed to first rebalance the imbalanced sample size and then further select the most predictive and compact signature subset to verify its differentiation capability.

Results

From each tumor VOI, a total of 63 radiomic features were derived and 30 of them showed significant inter-group differences (P ≤ 0.01). By using the SVM-based feature selection algorithm with rebalanced samples, an optimal subset including 13 radiomic signatures was determined. The area under receiver operating characteristic curve and Youden index were improved to 0.8610 and 0.7192, respectively.

Conclusion

3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscular invasiveness in patients with bladder cancer.

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Acknowledgements

We would like to thank Mr. Long-Biao Cui from Department of Radiology, Xijing Hospital, the Fourth Military Medical University for the inspiration and discussion of the research idea in this study, and Mr. Dan Xiao for his technical support on medical image processing. More importantly, we would like to thank all editors and the anonymous reviewers for their insightful, helpful, and thought-provoking suggestions on the quality improvement of this work.

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Authors and Affiliations

  1. School of Biomedical Engineering, Fourth Military Medical University, Xi’an, 710032, China

    Xiaopan Xu, Yang Liu, Xi Zhang, Yuxia Wu, Guopeng Zhang, Jiang Meng & Hongbing Lu

  2. Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, 710038, China

    Qiang Tian

  3. Department of Urology, Tangdu Hospital, Fourth Military Medical University, Xi’an, 710038, China

    Zengyue Yang

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  1. Xiaopan Xu
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Correspondence to Hongbing Lu.

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Funding

National Nature Science Foundation of China under Grant No. 81230035; Shaanxi Provincial Foundation for Social Development and Key Technology under Grant Nos. 2015SF177 and 2016SF302.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

Informed consent

Statement of informed consent was not applicable since the manuscript does not contain any patient data.

Additional information

Xiaopan Xu and Yang Liu are co-first authors.

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Xu, X., Liu, Y., Zhang, X. et al. Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps. Abdom Radiol 42, 1896–1905 (2017). https://doi.org/10.1007/s00261-017-1079-6

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