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Feasibility of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer

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Summary

The mutation status of KRAS is a significant biomarker in the prognosis of rectal cancer. This study investigated the feasibility of MRI-based radiomics in predicting the mutation status of KRAS with a composite index which could be an important criterion for KRAS mutation in clinical practice. In this retrospective study, a total of 127 patients with rectal cancer were enrolled. The 3D Slicer was used to extract the radiomics features from the MRI images, and sparse support vector machine (SVM) with linear kernel was applied for feature reduction. The radiomics classifier for predicting the KRAS status was then constructed by Linear Discriminant Analysis (LDA) and its performance was evaluated. The composite index was determined with LDA model. Out of 127 rectal cancer subjects, there were 44 KRAS mutation cases and 83 wild cases. A total of 104 radiomics features were extracted, 54 features were filtered by linear SVM with L1-norm regularization and 6 features that had no significant correlations within them were finally selected. The radiomics classifier constructed using the 6 features featured an AUC value of 0.669 (specificity: 0.506; sensitivity: 0.773) with LDA. Furthermore, the composite index (Radscore) had statistically significant difference between the KRAS mutation and wild groups. It is suggested that the MRI-based radiomics has the potential in predicting the KRAS status in patients with rectal cancer, which may enhance the diagnostic value of MRI in rectal cancer.

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Change history

  • 14 May 2021

    The correct author name GUO is correct given in the PDF file, but tagged incorrectly to QUO.

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Authors and Affiliations

  1. Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China

    Xiao-fang Guo & Hai-bo Xu

  2. Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430079, China

    Xiao-fang Guo, Qian Yang, Zi-long Yuan & Yu-lin Liu

  3. College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China

    Wen-qian Yang & Xiao-hui Niu

Authors
  1. Xiao-fang Guo
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  2. Wen-qian Yang
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  3. Qian Yang
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  4. Zi-long Yuan
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  5. Yu-lin Liu
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  6. Xiao-hui Niu
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  7. Hai-bo Xu
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Correspondence to Hai-bo Xu.

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The authors declare that they have no conflicts of interest.

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Guo, Xf., Yang, Wq., Yang, Q. et al. Feasibility of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer. CURR MED SCI 40, 1156–1160 (2020). https://doi.org/10.1007/s11596-020-2298-6

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  • DOI: https://doi.org/10.1007/s11596-020-2298-6

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