## 3 Present
### Radiomics and Radiogenomics in Colorectal Cancer  
Currently, in the process of diagnosing and treating colorectal cancer (CRC), genomic analysis has a fundamental role. Radiogenomics uses radiomics data to develop a predictive model of tumor genomics [41]. In the future, radiogenomics may represent a complementary model to biopsy, the so-called \"virtual biopsy\". Genomic diagnosis by biopsy may lose key tumor features given the intratumoral heterogeneity. In this scenario, radiogenomics may define and locate tumor habitats susceptible to being biopsied because they present a greater probability of mutation of certain genes. In patients with CRC, the presence of mutations in the RAS-RAF-MEK-ERK signaling pathway causes these tumors to present resistance to anti-EGFR monoclonal treatments (cetuximab and  
Figure 4: Three-dimensional semi-automatic segmentation of liver metastases of colorectal cancer. panitumumab) [38]. However, the articles about radiomics in CRC do not only focus on the genetic determination. As can be seen in the literature, there are numerous studies about the therapy response, prognostic predictions, survival determination, microsatellite status, prediction of metastatic disease, etc. The majority of radiomics studies in CRC used a CT-based radiomics model because CT is the imaging technique of choice for staging in CRC patients (Figure 5). CT-Based Radiomics/Radiogenomics in Colorectal Cancer  
Regarding radiomics and radiogenomics models that use CT images (Table 2), most articles are focused on the prediction of treatment response and the development of liver metastasis. Manual segmentation (M) is usually the type of segmentation used in the radiomics assessment of CRC with only a few papers based on a semi-automatic segmentation (SA). The main studies in the literature on this topic are retrospective (R), reflecting a need for prospective studies (P). There is also a wide variation in both the number of patients (N) and features extracted (RF) included in the studies. Many of the studies only  
Figure 5: **(a)** Number of CRC-Radiomics articles that used manual (M), semi-automatic (SA) and automatic (A) segmentation. (NA: Not Available). **(b)** Timeline of the CRC-Radiomics articles published since 2016 included in this manuscript. **(c)** Number of CRC-Radiomics articles according to their target. reflect the features that they considered significant. Other studies reflect all the features extracted and the posterior features which were considered. In the results and conclusion, radiomics features show good results, but it can be seen in many studies that the results improve when the radiomics information and other clinical characteristics (such as CEA, T staging, etc.) are incorporated. Concerning the prognostic value of radiomics features in CRC, in the survival prediction, [PERSON] et al. concluded that a combined nomogram with a radiomics signature based on CT images and clinical predictors improves the predicted accuracy of the overall survival (OS) in CRC patients [43]. Similar results were reported by [PERSON] et al. which achieved a hazard ratio (HR) of 6.670, 2.866 and 3.342 in the training and two external validation cohorts in the association between radiomic features and OS [44]. [PERSON] et al. obtained an HR between 3.93 and 21.04 in the prediction of OS using three different radiomics features [45]. In a different study, [PERSON] et al. compared the correlation between texture features obtained from contrast-enhanced (CE) and noncontrast enhanced (NCE) CT images and OS and concluded that some of the second and third-order features were associated with patient's survival [46]. Finally, [PERSON] et al. evaluated the prediction of 1-year survival in patients with metastatic CRC with a comparative analysis of quantitative imaging biomarkers based on the geometric analysis (GMS) of the whole liver tumor burden (WLTB), in comparison to predictions based on the tumor burden score (TBS), WLTB alone, and a clinical model [47].