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Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer

Abstract

Objectives

To develop and validate radiomic models in evaluating biological characteristics of rectal cancer based on multiparametric magnetic resonance imaging (MP-MRI).

Methods

This study consisted of 345 patients with rectal cancer who underwent MP-MRI. We focused on evaluating five postoperative confirmed characteristics: lymph node (LN) metastasis, tumor differentiation, fraction of Ki-67-positive tumor cells, human epidermal growth factor receptor 2 (HER-2), and KRAS-2 gene mutation status. Data from 197 patients were used to develop the biological characteristics evaluation models. Radiomic features were extracted from MP-MRI and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by using two feature-ranking methods (MRMR and WLCX) and three classifiers (RF, SVM, and LASSO). Multivariable logistic regression was used to build an integrated evaluation model combining radiomic signatures and clinical characteristics. The performance was evaluated using an independent validation dataset comprising 148 patients.

Results

The MRMR and LASSO regression produced the best-performing radiomic signatures for evaluating HER-2, LN metastasis, tumor differentiation, and KRAS-2 gene status, with AUC values of 0.696 (95% CI, 0.610–0.782), 0.677 (95% CI, 0.591–0.763), 0.720 (95% CI, 0.621–0.819), and 0.651 (95% CI, 0.539–0.763), respectively. The best-performing signatures for evaluating Ki-67 produced an AUC value of 0.699 (95% CI, 0.611–0.786), and it was developed by WLCX and RF algorithm. The integrated evaluation model incorporating radiomic signature and MRI-reported LN status had improved AUC of 0.697 (95% CI, 0.612–0.781).

Conclusion

Radiomic signatures based on MP-MRI have potential to noninvasively evaluate the biological characteristics of rectal cancer.

Key Points

• Radiomic features were extracted from MP-MRI images of the rectal tumor.

The proposed radiomic signatures demonstrated discrimination ability in identifying the histopathological, immunohistochemical, and genetic characteristics of rectal cancer.

• All MRI sequences were important and could provide complementary information in radiomic analysis.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

CRC:

Colorectal cancer

DCE:

Dynamic contrast-enhanced

DWI:

Diffusion-weighted imaging

HER-2:

Human epidermal growth factor receptor 2

LASSO:

Least absolute shrinkage and selection operator

MP-MRI:

Multiparametric magnetic resonance imaging

MRMR:

Minimum redundancy maximum relevance

RF:

Random forest

WLCX:

Wilcoxon rank-sum test

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Acknowledgements

Medical editor Katharine O’Moore-Klopf, ELS (East Setauket, NY, USA) provided professional English-language editing of this article.

Funding

This study has received funding by the National Natural Science Foundation of China [grant number 81571772, 81430041]; Science, Technology Plan Projects of Jiangsu—Society Development Project [grant number BE2017671]; and Foundation for Pearl River Science & Technology Young Scholars of Guangzhou [grant 201610010059].

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Corresponding authors

Correspondence to Lei Wang or Xin Gao.

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Guarantor

The scientific guarantor of this publication is Xin Gao.

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

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic experimental

• performed at one institution

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Meng, X., Xia, W., Xie, P. et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol 29, 3200–3209 (2019). https://doi.org/10.1007/s00330-018-5763-x

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  • DOI: https://doi.org/10.1007/s00330-018-5763-x

Keywords

  • Rectal neoplasms
  • Magnetic resonance imaging
  • Algorithms