MRI Radiomics Signature as a Potential Biomarker for Predicting KRAS Status in Locally Advanced Rectal Cancer Patients

ZhiYuan Zhang; LiJun Shen; Yan Wang; Jiazhou Wang; Hui Zhang; Fan Xia; JueFeng Wan; Zhen Zhang

doi:10.3389/fonc.2021.614052

MRI Radiomics Signature as a Potential Biomarker for Predicting KRAS Status in Locally Advanced Rectal Cancer Patients

Front Oncol. 2021 May 7:11:614052. doi: 10.3389/fonc.2021.614052. eCollection 2021.

Authors

ZhiYuan Zhang^{1

2

3}, LiJun Shen^{1

2

3}, Yan Wang^{1

2

3}, Jiazhou Wang^{1

2

3}, Hui Zhang^{1

2

3}, Fan Xia^{1

2

3}, JueFeng Wan^{1

2

3}, Zhen Zhang^{1

2

3}

Affiliations

¹ Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Shanghai Key Laboratory of Radiation Oncology, Shanghai, China.

Abstract

Background and purpose: Locally advanced rectal cancer (LARC) is a heterogeneous disease with little information about KRAS status and image features. The purpose of this study was to analyze the association between T2 magnetic resonance imaging (MRI) radiomics features and KRAS status in LARC patients.

Material and methods: Eighty-three patients with KRAS status information and T2 MRI images between 2012.05 and 2019.09 were included. Least absolute shrinkage and selection operator (LASSO) regression was performed to assess the associations between features and gene status. The patients were divided 7:3 into training and validation sets. The C-index and the average area under the receiver operator characteristic curve (AUC) were used for performance evaluation.

Results: The clinical characteristics of 83 patients in the KRAS mutant and wild-type cohorts were balanced. Forty-two (50.6%) patients had KRAS mutations, and 41 (49.4%) patients had wild-type KRAS. A total of 253 radiomics features were extracted from the T2-MRI images of LARC patients. One radiomic feature named X.LL_scaled_std, a standard deviation value of scaled wavelet-transformed low-pass channel filter, was selected from 253 features (P=0.019). The radiomics-based C-index values were 0.801 (95% CI: 0.772-0.830) and 0.703 (95% CI: 0.620-0.786) in the training and validation sets, respectively.

Conclusion: Radiomics features could differentiate KRAS status in LARC patients based on T2-MRI images. Further validation in a larger dataset is necessary in the future.

Keywords: KRAS; local advanced rectal cancer; magnetic resonance imaging; prediction; radiomic.