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Cancer Research and Treatment > Accepted Articles
doi: https://doi.org/10.4143/crt.2019.050    [Accepted]
Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer
Ji Eun Oh1, Min Ju Kim2, Joohyung Lee1, Bo Yun Hur2, Bun Kim2, Dae Yong Kim2, Ji Yeon Baek2, Hee Jin Chang2, Sung Chan Park2, Jae Hwan Oh2, Sun Ah Cho1, Dae Kyung Sohn1,2
1Innovative Medical Engineering & Technology, Research Institute and Hospital, National Cancer Center, Goyang, Korea
2Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Korea
Correspondence  Dae Kyung Sohn ,Tel: 82-31-920-1636, Fax: 82-31-920-2507, Email: gsgsbal@ncc.re.kr
Received: January 22, 2019;  Accepted: May 6, 2019.  Published online: May 7, 2019.
ABSTRACT
Purpose
Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS.
Materials and Methods
Sixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred.
Results
Three radiomic features were significantly associated with KRAS mutational status (p<0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively.
Conclusion
Using MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.
Key words: Rectal neoplasms, Texture analysis, KRAS gene, Magnetic resonance imaging
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