## 3 Present
### Radiomics and Radiogenomics in Colorectal Cancer
compared a combined model with an RF-only model for the prediction of lymphovascular invasion (LVI). Their best results were with the combined model. One interesting thing in this study was that they used radiomics features from the intra-tumoral area (1490 RF) and peri-tumoral area (1605 RF) [99]. Finally, [PERSON] et al. also concluded that the peri-tumor features can improve the prediction performance of LVI in patients with rectal cancer [100]. There are very few articles that used radiomics to investigate the metastatic pulmonary nodules in CRC. [PERSON] et al. found that some RF remain unchanged from small to large malignant nodules, while they do not remain stable in benign nodules [101]. [PERSON] et al. tried to assess local control of CRC lung metastases treated with radiofrequency ablation. They concluded that RF could help to discriminate nodules with a risk of local progression that could benefit from complementary treatment [102]. Radiomics have also been used in CRC patients with liver metastases. In the field of treatment response and progression prediction of liver metastasis, [PERSON] et al. demonstrate that the radiomics model can predict the response to chemotherapy of metastatic liver lesions [103]. [PERSON] et al. in their prospective study had non-significant results for predicting new liver metastases in patients who will undergo ablation for liver metastases of CRC [104]. [PERSON] et al. studied the prediction of local tumor progression after ablation of colorectal liver metastasis. The best results were obtained with the combined model [105]. [PERSON] et al. studied the survival of hepatic arterial infusion chemotherapy. They also studied the 1-year, 2-year and 3-year survival [106]. There are other studies with similar results. [PERSON] et al. tried to predict the response of targeted HER2 treatment in liver metastasis [107]. [PERSON] et al. tried to predict liver recurrence, extrahepatic recurrence, or no evidence of disease after the resection of liver metastases from CRC [108]. Finally, [PERSON] et al. had significant results for the progress prediction of liver metastases [109]. There are few studies that investigate the histopathological growth patterns of liver metastasis and liver heterogeneity in CRC patients. [PERSON] et al. had significant results for predicting a pure histopathological growth pattern (100% desmoplastic or dHGP) from a replacement histopathological pattern (rHGP) [110]. [PERSON] et al. also tried to predict the histopathologic growth pattern (HGP) of colorectal liver metastasis [111]. [PERSON] et al. studied the tumor heterogeneity in liver metastasis of CRC [112]. Finally,Devoto et al. demonstrated with a radiomics analysis that a normal apparently metastatic liver was significantly more heterogeneous than a non-metastatic liver (\(p<0.05\)) [113]. In the field of survival prediction in CRC patients with liver metastasis, [PERSON] et al. tried to predict OS. They found three significant RF which predict OS [114]. Finally, in the prediction of liver metastasis in CRC patients, [PERSON] et al. studied the value of the RF for predicting the high risk of liver metastases in patients with CRC [115]. [PERSON] et al. had an AUC of 0.79-0.72 (in the internal and external validation cohort, respectively) in the prediction of liver metastasis in patients with CRC [116]. [PERSON] et al. also studied the performance of a nomogram for predicting liver metastasis [117]. [PERSON] et al. obtained a global accuracy of 0.933 for predicting liver metastasis in patients with CRC [118]. Finally, [PERSON] et al. tried to improve the performance of a clinical model for the prediction of 5-year liver metastasis using a combined model. The AUC for the combined model was better [119]. In the evaluation of perineural invasion, there are a few studies with radiomics. [PERSON] et al. developed a radiomics prediction model based on radiomics and CEA levels, and they obtained better results than the radiomics-only model [120]. [PERSON] et al.