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 Zapico and Víctor González-Castro and Sandra Baleato-González and Roberto García-Figueiras and J Ramon Antúnez-López and Miguel Souto-Bayarri},

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

year  = {2023},

date = {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 and José Ramón Antunez and Miguel Souto-Bayarri},

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

year  = {2020},

date = {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}

}

@article{cueto-lopez_comparative_2019,

title = {A comparative study on feature selection for a risk prediction model for colorectal cancer},

author = {Nahúm Cueto-López and Maria Teresa García-Ordás and Verónica Dávila-Batista and Víctor Moreno and Nuria Aragonés and Rocío Alaiz-Rodríguez},

url = {https://arxiv.org/abs/2402.05293},

year  = {2019},

date = {2019-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {177},

pages = {219–229},

abstract = {The aim of this study is to evaluate risk prediction models to identify individuals at higher risk of developing colorectal cancer, focusing on feature selection methods. This is crucial for improving model performance, avoiding overfitting, and highlighting key risk factors. Additionally, the stability of feature selection/ranking methods is analyzed using conventional metrics and a visual approach proposed in this study.},

note = {Publisher: Elsevier},

keywords = {algorithm stability, colorectal cancer, feature selection, ranking methods, risk prediction models},

pubstate = {published},

tppubtype = {article}

}

@article{alaiz_rodriguez_comparative_2019,

title = {A comparative study on feature selection for a risk prediction model for colorectal cancer},

author = {Rocío Alaiz Rodríguez and Nahúm Cueto López and Maria Teresa García Ordás and Verónica Dávila Batista and Víctor Moreno and Nuria Aragonés},

url = {https://arxiv.org/abs/2402.05293},

year  = {2019},

date = {2019-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {177},

pages = {219–229},

abstract = {The aim of this study is to evaluate risk prediction models to identify individuals at higher risk of developing colorectal cancer, focusing on feature selection methods. This is crucial for improving model performance, avoiding overfitting, and highlighting key risk factors. Additionally, the stability of feature selection/ranking methods is analyzed using conventional metrics and a visual approach proposed in this study.},

note = {Publisher: Elsevier},

keywords = {algorithm stability, colorectal cancer, feature selection, ranking methods, risk prediction models},

pubstate = {published},

tppubtype = {article}

}

@article{cueto_lopez_assessing_2018,

title = {Assessing feature selection techniques for a colorectal cancer prediction model},

author = {Nahúm Cueto López and Rocío Alaiz Rodríguez and María Teresa García Ordás and Carmen González Donquiles and Vicente Martín},

url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_46},

year  = {2018},

date = {2018-01-01},

journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},

pages = {471–481},

abstract = {Risk prediction models for colorectal cancer help identify high-risk individuals and key risk factors. This study evaluates feature ranking algorithms in terms of stability and performance. Results show that Random Forest (RF) is the most stable but not the best-performing model, while SVM-wrapper and Pearson correlation achieve a balance between stability and predictive accuracy.},

note = {Publisher: Springer International Publishing},

keywords = {colorectal cancer, feature selection, healthcare analytics, machine learning, risk prediction},

pubstate = {published},

tppubtype = {article}

}