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Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases

Authors :
Alfonso De Stefano
Antonella Petrillo
Luca Brunese
Vincenza Granata
Roberta Fusco
Alessandro Ottaiano
Antonio Avallone
Francesco Izzo
Carolina Sbordone
Source :
Cancers, Vol 13, Iss 453, p 453 (2021), Cancers, Volume 13, Issue 3
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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<br />59 years of mean age and 36&ndash<br />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.

Details

Language :
English
ISSN :
20726694
Volume :
13
Issue :
453
Database :
OpenAIRE
Journal :
Cancers
Accession number :
edsair.doi.dedup.....4702477ca12b9be3d950d916792d52a5