Publications

@article{porto-alvarez_ct-based_2023,

title = {CT-based radiomics to predict KRAS mutation in CRC patients using a machine learning algorithm: a retrospective study},

author = {Jacobo Porto-Álvarez and Eva Cernadas and Rebeca Aldaz-Martínez and Manuel Fernández-Delgado and Emilio Huelga and Víctor González-Castro and Sandra Baleato-González and Roberto García-Figueiras and José Ramón Antúnez-López and Miguel Souto-Bayarri},

url = {https://www.mdpi.com/2227-9059/11/8/2144},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Biomedicines},

volume = {11},

number = {8},

pages = {2144},

abstract = {This study examines the use of CT-based radiomics to predict KRAS mutations in colorectal cancer (CRC) patients. Several classifiers were tested, with AdaBoost on clinical data achieving the highest accuracy (76.8%). Texture descriptors also showed a correlation with KRAS mutations. Radiomics could reduce the need for invasive diagnostic methods for CRC in the future.},

note = {Publisher: MDPI},

keywords = {colorectal cancer, KRAS Mutation, Radiogenomics, Radiomics, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_ct_2020,

title = {CT radiomics in colorectal cancer: Detection of KRAS mutation using texture analysis and machine learning},

author = {Víctor González-Castro and Eva Cernadas and Emilio Huelga and Manuel Fernández-Delgado and Jacobo Porto-Álvarez and José Ramón Antúnez-López and Miguel Souto-Bayarri},

url = {https://www.mdpi.com/2076-3417/10/18/6214},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Applied Sciences},

volume = {10},

number = {18},

pages = {6214},

abstract = {This study analyzes CT image textures to classify colorectal cancer patients as KRAS+ or KRAS- using machine learning classifiers (SVM, GBM, NNET, RF). Texture analysis quantifies tumor heterogeneity, supporting the use of radiomics to predict KRAS mutations. A retrospective study with 47 patients achieved the highest accuracy (83%) and kappa (64.7%) using NNET with wavelet transform and Haralick features. This non-invasive approach could eliminate the need for biopsies, reducing risks and enabling more personalized treatment.},

note = {Publisher: MDPI},

keywords = {colorectal cancer, KRAS Mutation, machine learning, Non-invasive Diagnosis, Radiomics},

pubstate = {published},

tppubtype = {article}

}