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CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study

Colorectal cancer (CRC) is one of the most common types of cancer worldwide. The KRAS mutation is present in 30–50% of CRC patients. This mutation confers resistance to treatment with anti-EGFR therapy. This article aims at proving that computer tomography (CT)-based radiomics can predict the KRAS m...

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Autores principales: Porto-Álvarez, Jacobo, Cernadas, Eva, Aldaz Martínez, Rebeca, Fernández-Delgado, Manuel, Huelga Zapico, Emilio, González-Castro, Víctor, Baleato-González, Sandra, García-Figueiras, Roberto, Antúnez-López, J Ramon, Souto-Bayarri, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10452272/
https://www.ncbi.nlm.nih.gov/pubmed/37626641
http://dx.doi.org/10.3390/biomedicines11082144
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author Porto-Álvarez, Jacobo
Cernadas, Eva
Aldaz Martínez, Rebeca
Fernández-Delgado, Manuel
Huelga Zapico, Emilio
González-Castro, Víctor
Baleato-González, Sandra
García-Figueiras, Roberto
Antúnez-López, J Ramon
Souto-Bayarri, Miguel
author_facet Porto-Álvarez, Jacobo
Cernadas, Eva
Aldaz Martínez, Rebeca
Fernández-Delgado, Manuel
Huelga Zapico, Emilio
González-Castro, Víctor
Baleato-González, Sandra
García-Figueiras, Roberto
Antúnez-López, J Ramon
Souto-Bayarri, Miguel
author_sort Porto-Álvarez, Jacobo
collection PubMed
description Colorectal cancer (CRC) is one of the most common types of cancer worldwide. The KRAS mutation is present in 30–50% of CRC patients. This mutation confers resistance to treatment with anti-EGFR therapy. This article aims at proving that computer tomography (CT)-based radiomics can predict the KRAS mutation in CRC patients. The piece is a retrospective study with 56 CRC patients from the Hospital of Santiago de Compostela, Spain. All patients had a confirmatory pathological analysis of the KRAS status. Radiomics features were obtained using an abdominal contrast enhancement CT (CECT) before applying any treatments. We used several classifiers, including AdaBoost, neural network, decision tree, support vector machine, and random forest, to predict the presence or absence of KRAS mutation. The most reliable prediction was achieved using the AdaBoost ensemble on clinical patient data, with a kappa and accuracy of 53.7% and 76.8%, respectively. The sensitivity and specificity were 73.3% and 80.8%. Using texture descriptors, the best accuracy and kappa were 73.2% and 46%, respectively, with sensitivity and specificity of 76.7% and 69.2%, also showing a correlation between texture patterns on CT images and KRAS mutation. Radiomics could help manage CRC patients, and in the future, it could have a crucial role in diagnosing CRC patients ahead of invasive methods.
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spelling pubmed-104522722023-08-26 CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study Porto-Álvarez, Jacobo Cernadas, Eva Aldaz Martínez, Rebeca Fernández-Delgado, Manuel Huelga Zapico, Emilio González-Castro, Víctor Baleato-González, Sandra García-Figueiras, Roberto Antúnez-López, J Ramon Souto-Bayarri, Miguel Biomedicines Article Colorectal cancer (CRC) is one of the most common types of cancer worldwide. The KRAS mutation is present in 30–50% of CRC patients. This mutation confers resistance to treatment with anti-EGFR therapy. This article aims at proving that computer tomography (CT)-based radiomics can predict the KRAS mutation in CRC patients. The piece is a retrospective study with 56 CRC patients from the Hospital of Santiago de Compostela, Spain. All patients had a confirmatory pathological analysis of the KRAS status. Radiomics features were obtained using an abdominal contrast enhancement CT (CECT) before applying any treatments. We used several classifiers, including AdaBoost, neural network, decision tree, support vector machine, and random forest, to predict the presence or absence of KRAS mutation. The most reliable prediction was achieved using the AdaBoost ensemble on clinical patient data, with a kappa and accuracy of 53.7% and 76.8%, respectively. The sensitivity and specificity were 73.3% and 80.8%. Using texture descriptors, the best accuracy and kappa were 73.2% and 46%, respectively, with sensitivity and specificity of 76.7% and 69.2%, also showing a correlation between texture patterns on CT images and KRAS mutation. Radiomics could help manage CRC patients, and in the future, it could have a crucial role in diagnosing CRC patients ahead of invasive methods. MDPI 2023-07-29 /pmc/articles/PMC10452272/ /pubmed/37626641 http://dx.doi.org/10.3390/biomedicines11082144 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Porto-Álvarez, Jacobo
Cernadas, Eva
Aldaz Martínez, Rebeca
Fernández-Delgado, Manuel
Huelga Zapico, Emilio
González-Castro, Víctor
Baleato-González, Sandra
García-Figueiras, Roberto
Antúnez-López, J Ramon
Souto-Bayarri, Miguel
CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
title CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
title_full CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
title_fullStr CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
title_full_unstemmed CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
title_short CT-Based Radiomics to Predict KRAS Mutation in CRC Patients Using a Machine Learning Algorithm: A Retrospective Study
title_sort ct-based radiomics to predict kras mutation in crc patients using a machine learning algorithm: a retrospective study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10452272/
https://www.ncbi.nlm.nih.gov/pubmed/37626641
http://dx.doi.org/10.3390/biomedicines11082144
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