2020
DOI: 10.3390/app10186214
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Abstract: In this work, by using descriptive techniques, the characteristics of the texture of the CT (computed tomography) image of patients with colorectal cancer were extracted and, subsequently, classified in KRAS+ or KRAS-. This was accomplished by using different classifiers, such as Support Vector Machine (SVM), Grading Boosting Machine (GBM), Neural Networks (NNET), and Random Forest (RF). Texture analysis can provide a quantitative assessment of tumour heterogeneity by analysing both the distribution and relati… Show more

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Cited by 13 publications
(8 citation statements)
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“…The authors also showed that a model with 14 CT-TA parameters had superior prediction performance compared to the previously studied 18F-FDG PET/CT SUVmax. Similarly, other authors proposed a CT texture-based approach to predict the KRAS mutation [ 22 , 23 ]. Regarding the relationship between imaging features and the MSI status, Pernicka et al showed that CT TA can predict the MSI status with low sensitivity (32%) and high specificity (95%) [ 24 ].…”
Section: Discussionmentioning
confidence: 99%
“…The authors also showed that a model with 14 CT-TA parameters had superior prediction performance compared to the previously studied 18F-FDG PET/CT SUVmax. Similarly, other authors proposed a CT texture-based approach to predict the KRAS mutation [ 22 , 23 ]. Regarding the relationship between imaging features and the MSI status, Pernicka et al showed that CT TA can predict the MSI status with low sensitivity (32%) and high specificity (95%) [ 24 ].…”
Section: Discussionmentioning
confidence: 99%
“…This proposed method effectively solves the brain imaging MR binarization problem. González-Castro et al [ 38 ] extracted texture features of CT radiomics of colorectal cancer patients and used support vector machines and random forest models for classification studies with 83% classification accuracy, and the experimental results demonstrated that texture feature analysis can quantitatively assess tumor heterogeneity by analyzing the distribution and relationships of pixels in images.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, AI algorithms can also be used in other genetic applications; for instance, a CNN model was used to predict tumor mutational burden-high (TMB-H) with an AUC of 0.93 ( 71 ). Moreover, AI has been shown to detect the presence of the KRAS proto-oncogene which may be implicated in the pathogenesis of CRC ( 72 ). In fact, 65% of carcinomas in the colon have been linked to mutations in the RAS family of genes, which includes the KRAS proto-oncogene ( 73 ).…”
Section: Cancer Genetics: Prediction Of Mutation Type and Microsatell...mentioning
confidence: 99%