CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning

TítuloCT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning
AutoresVíctor González-Castro, Eva Cernadas 2, Emilio Huelga, Manuel Fernández-Delgado, Jacobo Porto, José Ramón Antunez and Miguel Souto-Bayarri
TipoArtículo de revista
Fonte Applied Sciences, MDPI, Vol. 18, No. 10, pp. 15 , 2020.
RankProvisionally ranked Q1 in Materials Science (all) by SJR 2019
ISSN2076-3417
DOIhttps://doi.org/10.3390/app10186214
AbstractIn 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 relationship between the pixels in the image. The objective of this research is to demonstrate that CT-based Radiomics can predict the presence of mutation in the KRAS gene in colorectal cancer. This is a retrospective study, with 47 patients from the University Hospital, with a confirmatory pathological analysis of KRAS mutation. The highest accuracy and kappa achieved were 83% and 64.7%, respectively, with a sensitivity of 88.9% and a specificity of 75.0%, achieved by the NNET classifier using the texture feature vectors combining wavelet transform and Haralick coefficients. The fact of being able to identify the genetic expression of a tumour without having to perform either a biopsy or a genetic test is a great advantage, because it prevents invasive procedures that involve complications and may present biases in the sample. As well, it leads towards a more personalized and effective treatment.
Palabras chaveKRAS mutation; colorectal cancer; texture analysis; wavelets; haralick texture descriptors; Support Vector Machine; Grading Boosting Machine; Neural Network; Random Forest