scholarly journals Local tumour progression after percutaneous ablation of colorectal liver metastases according to RAS mutation status

2017 ◽  
Vol 104 (6) ◽  
pp. 760-768 ◽  
Author(s):  
B. C. Odisio ◽  
S. Yamashita ◽  
S. Y. Huang ◽  
S. Harmoush ◽  
S. E. Kopetz ◽  
...  
2019 ◽  
Vol 156 (6) ◽  
pp. S-1395
Author(s):  
Timothy J. Vreeland ◽  
Katharina Joechle ◽  
Masayuki Okuno ◽  
Eduardo A. Vega ◽  
Timothy E. Newhook ◽  
...  

2016 ◽  
Vol 150 (4) ◽  
pp. S1174
Author(s):  
Jason Denbo ◽  
Guillaume Passot ◽  
Yun Shin Chun ◽  
Suguru Yamashita ◽  
Scott Kopetz ◽  
...  

2013 ◽  
Vol 258 (4) ◽  
pp. 619-627 ◽  
Author(s):  
Jean-Nicolas Vauthey ◽  
Giuseppe Zimmitti ◽  
Scott E. Kopetz ◽  
Junichi Shindoh ◽  
Su S. Chen ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Vincenza Granata ◽  
Roberta Fusco ◽  
Antonio Avallone ◽  
Alfonso De Stefano ◽  
Alessandro Ottaiano ◽  
...  

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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