## 3 Present
### Radiomics and Radiogenomics in Colorectal Cancer
The combined model with HC-radiomics + DL-Radiomics, obtained better results [80]. [PERSON] et al. used radiomics features from CT images to predict the KRAS mutation in CRC. They also used a model based on the deep learning method with a residual neural network (ResNet) for predicting KRAS status [81]. [PERSON] et al. made a prediction of the KRAS mutation by comparing the three different phases of a CT study (non-contrast, arterial-phase, and venous-phase). They achieved the best results with radiomics features from the arterial and venous phases (AUC 0.826). The results for the combination of the three phases showed the worst AUC but the sensitivity, specificity and accuracy were higher [82]. [PERSON] et al. compared a deep learning model and a radiogenomics model for predicting the KRAS mutation. They obtained better results with the radiomics model [83]. [PERSON] et al. also studied the KRAS prediction. They obtained an AUC of 0.75 and 0.84 for the 2D and 3D radiomics models, respectively. They obtained better results with a combined model using clinical information (AUC of 0.92) [84]. In the prediction of BRAF status, [PERSON] et al. obtained remarkable results. The AUC of the score with the five selected radiomics features was 0.93 and 0.87 for the training and validation cohorts. They also conducted a nomogram with clinical information and radiomics features (AUC of 0.95 and 0.88) [85]. [PERSON] et al. also studied the radiomics features associated with 5-year OS. They found that some radiomics features were significantly different between BRAF mutation and BRAF wild-type [86]. In recent years, studies about the application of radiomics in the prediction of microsatellite instability have become more frequent. [PERSON] et al. studied the association between radiomics features and microsatellite instability (MSI). They found that a radiomics model had better results than a clinical model to predict the MSI, but the combined model (clinical and radiomic) had the best results [87]. Other studies achieved similar results. [PERSON] et al. concluded that their combined model could predict the MSI status in CRC [88]. [PERSON] et al. established a clinical-radiomics nomogram for the preoperative prediction of MSI status in CRC patients. They achieved an AUC of 0.87 and 0.90 in the training and validation set for predicting MSI status [89]. [PERSON] et al. studied the prediction of microsatellite instability using RF and clinical information. In their results, the radiomics signature obtained better results than the combined model [90]. [PERSON] et al. used a radiomics nomogram with RF, clinical information, and pathological information for predicting MSI status. The radiomics nomogram with clinical information and RF obtained an AUC of 0.74 and 0.77 for the training and validation cohorts in the discrimination between high MSI (MSI-H) and no MSI-H [91]. [PERSON] et al. found 32 radiomics features that are associated with the MSI status [92]. [PERSON] et al. studied the application of radiomics analysis of iodine-based material decomposition images with dual-energy CT (DECT) for predicting MSI status in CRC. They obtained an AUC of 0.961 and 0.875 (training and test cohorts) predicting MSI status [93]. Finally, [PERSON] et al. concluded that radiomics features could help to the determination of MSI status in CRC [94]. Another application of radiomics in CRC is the determination of lymph node (LN) metastasis. [PERSON] et al. concluded that 73 radiomics features were significant for differentiating nonmetastatic LN from metastatic LN in patients with colorectal mucinous adenocarcinoma [95]. [PERSON] et al. used multiphase CT for metastatic LN prediction. They obtained nine significant RF [96]. [PERSON] et al. concluded that the radiomics signature combined with clinical risk factors facilitates the preoperative individualized prediction of LN metastasis [97]. [PERSON] et al. compared a clinical prediction model (based on LN size) and a combined model. The results were better for the combined model [98]. [PERSON] et al.