Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer
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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.
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.
참고문헌 (38)
- Derbel O, "Impact of KRAS, BRAF and PI3KCA mutations in rectal carcinomas treated with neoadjuvant radiochemotherapy and surgery", BMC Cancer, 13, pp.200~ (2013)
- Karapetis CS, "K-ras mutations and benefit from cetuximab in advanced colorectal cancer", N Engl J Med, 359, pp.1757~1765 (2008)
- Dinapoli N, "Radiomics for rectal cancer", Transl Cancer Res, 5, pp.424~431 (2016)
- Liu Z, "Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer", Clin Cancer Res, 23, pp.7253~7262 (2017)
- Wu J, "Radiomics and radiogenomics for precision radiotherapy", J Radiat Res, 59, pp.i25~31 (2018)
- Nie K, "Rectal cancer : assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI", Clin Cancer Res, 22, pp.5256~5264 (2016)
- De Cecco CN, "Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance", Invest Radiol, 50, pp.239~245 (2015)
- Giannini V, "Predicting locally advanced rectal cancer response to neoadjuvant therapy with(18)F-FDG PET and MRI radiomics features", Eur J Nucl Med Mol Imaging, 46, pp.878~888 (2019)
- Horvat N, "MR imaging of rectal cancer : radiomics analysis to assess treatment response after neoadjuvant therapy", Radiology, 287, pp.833~843 (2018)
- Dinapoli N, "Magnetic resonance, vendor-independent, intensity histogram analysis predicting pathologic complete response after radiochemotherapy of rectal cancer", Int J Radiat Oncol Biol Phys, 102, pp.765~774 (2018)
- Miles KA, "Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog(KRAS)mutations in colorectal cancer", J Nucl Med, 55, pp.386~391 (2014)
- Yang L, "Can CTbased radiomics signature predict KRAS", NRAS, Eur Radiol, pp.2018, 28[DOI]
- Kawada K, "Relationship between 18F-FDG PET", CT scans and KRAS mutations in metastatic colorectal cancer, 2015, pp.56, 1322~1327
- Sun Y, "Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer : Preliminary findings", J Magn Reson Imaging, 48, pp.615~621 (2018)
- Srivaramangai R, "Preprocessing MRI images of colorectal cancer", Int J Comput Sci Issues, 14, pp.48~ (2017)
- Zuiderveld K, "Graphics Gems IV", pp.474~485 (1994), Academic Press Professional, Inc
- Ghose S, "A random forest based classification approach to prostate segmentation in MRI", MICCAI Grand Challenge: Prostate MR Image Segmentation (2012)
- Szczypinski PM, "MaZda : a software package for image texture analysis", Comput Methods Programs Biomed, 94, pp.66~76 (2009)
- Materka A, "Texture analysis in machine vision", pp.197~206 (2000), World Scientific Publishing Co., Inc
- Miles KA, "CT texture analysis using the filtration-histogram method: what do the measurements mean?", Cancer Imaging, 13, pp.400~406 (2013)
- Weiss GJ, "Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic", PLoS One, 9, pp.e100244~ (2014)
- Chaddad A, "Multi texture analysis of colorectal cancer continuum using multispectral imagery", PLoS One, 11, pp.e0149893~ (2016)
- Haralick RM, "Textural features for image classification", IEEE Trans Syst Man Cybern, SMC-3, pp.610~621 (1973)
- Sachdeva J, "Segmentation, feature extraction, and multiclass brain tumor classification", J Digit Imaging, 26, pp.1141~1150 (2013)
- Ozturk S, "Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA", Procedia Comput Sci, 132, pp.40~46 (2018)
- Galloway MM, "Texture analysis using gray level run lengths", Comput Graph Image Process, 4, pp.172~179 (1975)
- Karahaliou A, "Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis", Br J Radiol, 80, pp.648~656 (2007)
- Krishnan KR, "Soft computing applications. Advances in intelligent systems and computing, Vol. 195", pp.611~624 (2013), Springer
- Fonarow GC, "Study Group, and Investigators. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis", JAMA, 293, pp.572~580 (2005)
- Meng X, "Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer", Eur Radiol, 29, pp.3200~3209 (2019)
- Cui Y, "Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma : preliminary findings", J Magn Reson Imaging, 50, pp.930~939 (2019)
- Xu Y, "Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer?", Eur Radiol, 28, pp.3059~3065 (2018)
- Yeo DM, "Histogram analysis of perfusion parameters from dynamic contrast-enhanced MR imaging with tumor characteristics and therapeutic response in locally sdvanced tectal vancer", Biomed Res Int, 2018, pp.3724393~ (2018)
- Jeon SH, "Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer", Radiat Oncol, 14, pp.43~ (2019)
- Lubner MG, "CT Texture analysis : definitions, applications, biologic correlates, and challenges", Radiographics, 37, pp.1483~1503 (2017)
- Ganeshan B, "Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis : a potential marker of survival", Eur Radiol, 22, pp.796~802 (2012)
- Bashir U, "Imaging heterogeneity in lung cancer : techniques, applications, and challenges", AJR Am J Roentgenol, 207, pp.534~543 (2016)
- Semenza GL, "HIF-1 and tumor progression: pathophysiology and therapeutics", Trends Mol Med, 8(4 Suppl), pp.S62~S67 (2002)