CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

Abstract

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer

“…Several previous researches demonstrated that wavelet features were significantly correlated with heterogeneity indices at the cellular level, which were promising radiomic features to evaluate prognosis in colorectal liver metastases patients (37). In this study, the radiomic signature that we constructed showed a reliable model to predict MMR status in GC, outperforming traditional semantic features extracted by radiologists.…”

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.…”

Multimodality Imaging Assessment of Desmoid Tumors: The Great Mime in the Era of Multidisciplinary Teams

“…In the correct radiological disease management, multimodality imaging, including ultrasound (US), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), should also be preferred concerning the different phases of DTs approaches [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ]. In fact, during radiologist work-up, different moments may be considered: detection and characterization, adjacent structures involvement assessment, treatment response evaluation and surveillance [ 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 ]. During each of these moments, the different techniques can be associated with and/or follow each other.…”