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

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      https://www.riss.kr/link?id=A106509062

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      다국어 초록 (Multilingual 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.
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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 resonanc...

      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.

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      참고문헌 (Reference)

      1 Ganeshan B, "Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis : a potential marker of survival" 22 : 796-802, 2012

      2 Galloway MM, "Texture analysis using gray level run lengths" 4 : 172-179, 1975

      3 Karahaliou A, "Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis" 80 : 648-656, 2007

      4 Materka A, "Texture analysis in machine vision" World Scientific Publishing Co., Inc 197-206, 2000

      5 De Cecco CN, "Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance" 50 : 239-245, 2015

      6 Haralick RM, "Textural features for image classification" SMC-3 : 610-621, 1973

      7 Fonarow GC, "Study Group, and Investigators. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis" 293 : 572-580, 2005

      8 Krishnan KR, "Soft computing applications. Advances in intelligent systems and computing, Vol. 195" Springer 611-624, 2013

      9 Sachdeva J, "Segmentation, feature extraction, and multiclass brain tumor classification" 26 : 1141-1150, 2013

      10 Kawada K, "Relationship between 18F-FDG PET/CT scans and KRAS mutations in metastatic colorectal cancer" 56 : 1322-1327, 2015

      1 Ganeshan B, "Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis : a potential marker of survival" 22 : 796-802, 2012

      2 Galloway MM, "Texture analysis using gray level run lengths" 4 : 172-179, 1975

      3 Karahaliou A, "Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis" 80 : 648-656, 2007

      4 Materka A, "Texture analysis in machine vision" World Scientific Publishing Co., Inc 197-206, 2000

      5 De Cecco CN, "Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance" 50 : 239-245, 2015

      6 Haralick RM, "Textural features for image classification" SMC-3 : 610-621, 1973

      7 Fonarow GC, "Study Group, and Investigators. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis" 293 : 572-580, 2005

      8 Krishnan KR, "Soft computing applications. Advances in intelligent systems and computing, Vol. 195" Springer 611-624, 2013

      9 Sachdeva J, "Segmentation, feature extraction, and multiclass brain tumor classification" 26 : 1141-1150, 2013

      10 Kawada K, "Relationship between 18F-FDG PET/CT scans and KRAS mutations in metastatic colorectal cancer" 56 : 1322-1327, 2015

      11 Nie K, "Rectal cancer : assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI" 22 : 5256-5264, 2016

      12 Dinapoli N, "Radiomics for rectal cancer" 5 : 424-431, 2016

      13 Wu J, "Radiomics and radiogenomics for precision radiotherapy" 59 : i25-31, 2018

      14 Liu Z, "Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer" 23 : 7253-7262, 2017

      15 Sun Y, "Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer : Preliminary findings" 48 : 615-621, 2018

      16 Srivaramangai R, "Preprocessing MRI images of colorectal cancer" 14 : 48-, 2017

      17 Meng X, "Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer" 29 : 3200-3209, 2019

      18 Giannini V, "Predicting locally advanced rectal cancer response to neoadjuvant therapy with(18)F-FDG PET and MRI radiomics features" 46 : 878-888, 2019

      19 Weiss GJ, "Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic" 9 : e100244-, 2014

      20 Miles KA, "Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog(KRAS)mutations in colorectal cancer" 55 : 386-391, 2014

      21 Chaddad A, "Multi texture analysis of colorectal cancer continuum using multispectral imagery" 11 : e0149893-, 2016

      22 Dinapoli N, "Magnetic resonance, vendor-independent, intensity histogram analysis predicting pathologic complete response after radiochemotherapy of rectal cancer" 102 : 765-774, 2018

      23 Szczypinski PM, "MaZda : a software package for image texture analysis" 94 : 66-76, 2009

      24 Horvat N, "MR imaging of rectal cancer : radiomics analysis to assess treatment response after neoadjuvant therapy" 287 : 833-843, 2018

      25 Karapetis CS, "K-ras mutations and benefit from cetuximab in advanced colorectal cancer" 359 : 1757-1765, 2008

      26 Derbel O, "Impact of KRAS, BRAF and PI3KCA mutations in rectal carcinomas treated with neoadjuvant radiochemotherapy and surgery" 13 : 200-, 2013

      27 Bashir U, "Imaging heterogeneity in lung cancer : techniques, applications, and challenges" 207 : 534-543, 2016

      28 Yeo DM, "Histogram analysis of perfusion parameters from dynamic contrast-enhanced MR imaging with tumor characteristics and therapeutic response in locally sdvanced tectal vancer" 2018 : 3724393-, 2018

      29 Semenza GL, "HIF-1 and tumor progression: pathophysiology and therapeutics" 8 (8): S62-S67, 2002

      30 Zuiderveld K, "Graphics Gems IV" Academic Press Professional, Inc 474-485, 1994

      31 Cui Y, "Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma : preliminary findings" 50 : 930-939, 2019

      32 Jeon SH, "Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer" 14 : 43-, 2019

      33 Xu Y, "Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer?" 28 : 3059-3065, 2018

      34 Yang L, "Can CTbased radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer" 28 : 2058-2067, 2018

      35 Miles KA, "CT texture analysis using the filtration-histogram method: what do the measurements mean?" 13 : 400-406, 2013

      36 Lubner MG, "CT Texture analysis : definitions, applications, biologic correlates, and challenges" 37 : 1483-1503, 2017

      37 Ozturk S, "Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA" 132 : 40-46, 2018

      38 Ghose S, "A random forest based classification approach to prostate segmentation in MRI" 2012

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2024 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2021-01-01 평가 등재학술지 선정 (해외등재 학술지 평가) KCI등재
      2020-12-01 평가 등재후보로 하락 (해외등재 학술지 평가) KCI등재후보
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-05-27 학술지명변경 한글명 : 대한암학회지 -> Cancer Research and Treatment KCI등재
      2005-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2002-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 3.58 0.89 3.01
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      2.62 2.28 1.846 0.26
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