RT Journal Article
T1 CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning
A1 González Castro, Víctor
A1 Cernadas García, Eva
A1 Huelga Zapico, Emilio
A1 Fernández Delgado, Manuel
A1 Antúnez López, José Ramón
A1 Souto Bayarri, José Miguel
K1 KRAS mutation
K1 Colorectal cancer
K1 Texture analysis
K1 Wavelets
K1 Haralick texture descriptors
K1 Support Vector Machine
K1 Grading Boosting Machine
K1 Neural Network
K1 Random Forest
AB 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 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
PB MDPI
YR 2020
FD 2020
LK http://hdl.handle.net/10347/23660
UL http://hdl.handle.net/10347/23660
LA eng
NO González-Castro, V.; Cernadas, E.; Huelga, E.; Fernández-Delgado, M.; Porto, J.; Antunez, J.R.; Souto-Bayarri, M. CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning. Appl. Sci. 2020, 10, 6214
NO This work has received financial support from the Xunta de Galicia (Centro singular de investigación de Galicia, accreditation 2020–2023) and the European Union (European Regional Development Fund—ERDF), Project MTM2016-76969-P
DS Minerva
RD 23 abr 2026