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Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer.

Methods

Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).

Results

Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654–0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569–0.794) and 0.714 (95% CI, 0.602–0.827), respectively. DCA confirmed its clinical usefulness.

Conclusions

The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancer patients.

Key Points

• T2WI-based radiomics showed a moderate diagnostic significance for KRAS status.

• The best prediction model was obtained with SVM classifier.

• The baseline clinical and histopathological characteristics were not associated with KRAS mutation.

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Abbreviations

3D:

Three-dimensional

ANOVA:

Analysis of variance

ARMS:

Amplification-refractory mutation system

AUC:

Area under the ROC curve

CA199:

Carbohydrate antigen-199

CEA:

Carcinoembryonic antigen

CRC:

Colorectal cancer

DCA:

Decision curve analysis

DKI:

Diffusion kurtosis imaging

DT:

Decision tree

DWI:

Diffusion weighted imaging

EGFR:

Epidermal growth factor receptor

FFPE:

Formalin-fixed, paraffin-embedded

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

gray-Level size zone matrix

IVIM:

Intravoxel incoherent motion

KRAS:

Kirsten rat sarcoma

LoG:

Laplacian of Gaussian

LR:

Logistic regression

LVI:

Lymphangiovascular invasion

MRI:

Magnetic resonance imaging

NCCN:

National Comprehensive Cancer Network

PACS:

Picture archiving and communication system

pCR:

Pathological complete response

PCR:

Polymerase chain reaction

RBF:

Radial basis function

ROC:

Receiver operating characteristic

ROI:

Regions of interests

SVM:

Support vector machine

T2W:

T2-weighted

VOI:

Volume of interest

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Funding

This study was supported by the National Key Research and Development Program of China (No. 2017YFC0109003), the Special Research Program of Shanghai Municipal Commission of Heath and Family Planning on medical intelligence (No. 2018ZHYL0108), Shanghai Sailing Program (19YF1433100), the Science and Technology Project of Shanxi Province (No. 20150313007-5), and Applied Basic Research Programs of Shanxi Province (Grant No. 201801D121307 and 201801D221390). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Yanfen Cui and Huanhuan Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China

    Yanfen Cui, Xiaosong Du, Lei Xin, Dandan Li & Xiaotang Yang

  2. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China

    Huanhuan Liu & Dengbin Wang

  3. GE Healthcare China, Beijing, China

    Jialiang Ren

Authors
  1. Yanfen Cui
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Corresponding authors

Correspondence to Xiaotang Yang or Dengbin Wang.

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Guarantor

The scientific guarantor of this publication is Dengbin Wang.

Conflict of interest

One of the authors (JR) is an employee of GE Healthcare. The remaining 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.

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Institutional Review Board approval was obtained.

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• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Cui, Y., Liu, H., Ren, J. et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol 30, 1948–1958 (2020). https://doi.org/10.1007/s00330-019-06572-3

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  • DOI: https://doi.org/10.1007/s00330-019-06572-3

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