¿Existen ventajas clínicas al evaluar el estado de
los genes KRAS, NRAS, BRAF, PIK3CA, PTEN y
HER2 en pacientes con cáncer colorrectal?

Vergara, Edgar Mario; Alvis, Nelson; Suárez, Amileth

doi:10.30944/20117582.7

Cited by 3 publications

(2 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These changes are influenced by dietary, environmental, and microbiotic factors and the host’s immune response [ 3 , 4 ]. The successive activation of oncogenes ( KRAS , NRAS , BRAF , PIK3CA , ERRB2 ) while inactivating tumor suppressor genes ( APC , TP53 , PTEN , TGF-β , DCC ) guides the adenoma–carcinoma transition [ 5 , 6 ]. KRAS is a gene of the RAS/MAPK pathway.…”

Section: Introductionmentioning

confidence: 99%

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

Porto-Álvarez,

Cernadas,

Aldaz Martínez

et al. 2023

Biomedicines

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Porto-Álvarez,

Cernadas,

Aldaz Martínez

et al. 2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…In patients with metastatic colorectal cancer, the median overall survival without treatment is approximately 6 months. With the use of current therapies, survival can be prolonged to a median of 30 months, with a response rate for some therapies of 50% [6]. The introduction of targeted therapies resulted in an increase in progression-free survival and also in overall survival in patients with metastatic colorectal cancer.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

CT-Based Radiomics to Predict the KRAS Mutation in CRC patients. A Retrospective Study

Álvarez¹,

Cernadas²,

Martínez³

et al. 2023

Preprint

View full text Add to dashboard Cite

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 from the 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 specific were of 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%, showing also 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.

show abstract

¿Existen ventajas clínicas al evaluar el estado de los genes KRAS, NRAS, BRAF, PIK3CA, PTEN y HER2 en pacientes con cáncer colorrectal?

Cited by 3 publications

References 92 publications

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

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

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

CT-Based Radiomics to Predict the KRAS Mutation in CRC patients. A Retrospective Study

Contact Info

Product

Resources

About