Cancer Res Treat.  2020 Jan;52(1):51-59. 10.4143/crt.2019.050.

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

• Oh JE
• Kim MJ
• Lee J
• Hur BY
• Kim B
• Kim DY
• Baek JY
• Chang HJ
• Park SC
• Oh JH
• Cho SA
• Sohn DK

• KMID: 2501201
• DOI: http://doi.org/10.4143/crt.2019.050

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 KRASmutational 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.

Keyword

Rectal neoplasms; Texture analysis; gene; Magnetic resonance imaging

Figure

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  Fig. 1. Diagram showing the flow of patients through the study. MR, magnetic resonance.

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  Fig. 2. Example of segmentation of rectal cancer in T2-weighted magnetic resonance images. (A) Original image. (B, C) Segmentation by two experienced radiologists. (D) Ground truth tumor image showing the area of overlap between the two readers.

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  Fig. 3. Examples of preprocessing images. (A) An original image. (B) A preprocessed image.

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  Fig. 4. A box plot comparing the distribution of wild-type KRAS with that of mutant KRAS. The central line in the box plot indicates the median value of the data. The lower and upper boundary lines of the central box represent the 25% and 75% quartiles. The box indicates the 95% confidence interval.

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  Fig. 5. Decision tree for identification of KRAS mutations in patients with rectal cancer using T2-weighted magnetic resonance imaging features. M, number of tumors with mutant KRAS; WT, number of tumors with wild-type KRAS; SD, standard deviation; ssf, spatial scale factor.

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  Fig. 6. Receiver-operating characteristic (ROC) curve showing the performance of the decision tree model. The area under the curve is 0.884.

Reference

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