EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases

Abstract

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

“…SVM methods also differentiated between epithelial ovarian cancer (EOC) and surrounding tissues, aiding EOC diagnosis 13 . In addition, the SVM methods successfully screened colorectal biomarkers for personalized treatment of patients with post-operative liver metastases 14 . However, there have been few studies on potential NSCLC biomarkers.…”

A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls

“…Accurate diagnosis and staging of HCC can be achieved by CT ( Figure 3 ) or MRI ( Figure 4 ) in the absence of invasive methods, when precise and stringent criteria are applied [ 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 ]. Although MRI proves to be more sensitive in the characterization of liver lesions, particularly with hepatospecific contrast agents, there is no unambiguous indication from the pool of experts [ 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ].…”

“…Accurate diagnosis and staging of HCC can be achieved by CT (Figure 3) or MRI (Figure 4) in the absence of invasive methods, when precise and stringent criteria are applied . Although MRI proves to be more sensitive in the characterization of liver lesions, particularly with hepatospecific contrast agents, there is no unambiguous indication from the pool of experts [131][132][133][134][135][136][137][138][139][140][141][142][143][144][145][146][147][148]. The choice to use CT rather than MRI remains at the discretion of individual institutions.…”

An Overview of the Role of Multiparametric MRI in the Investigation of Testicular Tumors

“…Radiomics is an emerging quantitative approach to imaging that aims to provide information beyond that which can be perceived from human imaging interpretation alone, by means of advanced mathematical analysis [ 74 , 75 ]. Texture analysis represents a technique used for the quantification of tissue heterogeneity.…”