In the present study, we assessed the association of RAS mutation status and radiomics derived data by Contrast Enhanced Magnetic Resonance Imaging (CE-MRI) in liver metastases by CRC.We performed the evaluation extracting by CE-MRI both texture and morphological metrics in a 3D setting. We demonstrated that radiomics with texture parameters could add value to qualitative assessment of MR studies and with better results compared to morphological metrics, providing individualized evaluation of CRLM. Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status. Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36-80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.

Radiomics-derived data by contrast enhanced magnetic resonance in RAS mutations detection in colorectal liver metastases

Sbordone C.;Brunese L.;
2021

Abstract

In the present study, we assessed the association of RAS mutation status and radiomics derived data by Contrast Enhanced Magnetic Resonance Imaging (CE-MRI) in liver metastases by CRC.We performed the evaluation extracting by CE-MRI both texture and morphological metrics in a 3D setting. We demonstrated that radiomics with texture parameters could add value to qualitative assessment of MR studies and with better results compared to morphological metrics, providing individualized evaluation of CRLM. Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status. Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36-80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/96621
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