## 3 Present
### Radiomics and Radiogenomics in Colorectal Cancer
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Author** & **Year** & **Type** & **N** & **Target** & **ROI** & **RF** & **Results** & **Conclusions** \\ \hline Roca [118] & 2021 & R & 30 & Liver metastasis & M & 22 & General Acc of 0.933 & CT-based radiomics cm detect LnCRC \\ \hline Lee [119] & 2020 & R & 2019 & Liver metastasis & M & 4096 & AUC of 0.767 in prediction 5-year liver metastasis & Combined model improved the performance \\ \hline Huang [120] & 2018 & R & 346 & \begin{tabular}{c} Perivental \\ inversion \\ \end{tabular} & M & 29 & Cinda 0.817 for combined nomogram & Combined nomogram was easy and effective \\ \hline Li [211] & 2020 & R & 207 & \begin{tabular}{c} Perivental \\ inversion \\ \end{tabular} & M & 306 & AUC of 0.793 (PNL prediction) & Machine learning models can predict PN and AUC of 0.862 (GKAS prediction) & KJA mutation in CRC patients \\ \hline Li [122] & 2021 & R & 303 & \
\begin{tabular}{c} Perivental \\ inversion \\ \end{tabular} & M & 3095 & AUC of 0.828 and 0.801 for the combined model for & The combined model can help to evaluate the PN predicting PN status \\ \hline Li [123] & 2020 & R & 779 &
\begin{tabular}{c} Perivental \\ metastasis \\ \end{tabular} & SA & 8900 & AUC of 0.856 for combined model & Combined model, with CT-based radiomics, can be applied in the prediction of PM \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Radiomics features can also be used for the prediction of some risk factors. [PERSON] et al. obtained an AUC of 0.799 for the combined model (radiomics-CT staging) for the prediction of high-risk colon cancer [66]. [PERSON] et al. try to use the radiomics features for the differentiation between mucinous (MA) and non-mucinous (NMA) rectal cancer. MA rectal cancer has a poorer prognosis than NMA. They obtained an AUC of 0.93 in both training and validation cohorts [67]. [PERSON] et al. concluded that some radiomics features are stable enough to be used for treatment monitoring and prognosis prediction [68]. [PERSON] et al. concluded that radiomics features could help T staging [69]. [PERSON] et al. obtained an AUC of 0.945 and 0.754 (primary and validation cohorts) in the prediction of metastatic CRC. [70]. [PERSON] et al. obtained an AUC of 0.812 and 0.735 (training and validation cohorts) in the differentiation between low-grade and high-grade CRC [71]. Finally, [PERSON] et al. obtained an AUC of 0.792 and 0.798 (training and validation cohorts) in the preoperative differentiation between stages I-II and III-IV in patients with CRC [72]. In terms of the use of radiomics features for predicting gene expression, [PERSON] et al. concluded that there are genes (ABCC2, CD166, CDKNV1 and INHB) whose expression changes are significantly associated with radiomics features [73]. [PERSON] et al. conclude that there is an association between radiomics signature and the expression of CXCL8 [74]. [PERSON] et al. also studied gene expression in addition to recurrence prediction. They found 10 between 1037 RF which had a significant association with recurrence prediction and with the gene expression of PECAM11, PRDM1, AIF1, IL10, ISG20 and TLR8 [75]. [PERSON] et al. tried to predict the differences in tumor mutation burden (TMB) between primary and metastatic lesions using radiogenomics. They concluded that radiogenomics can infer the TMB status [76]. On the other hand, [PERSON] et al. had significant results for the prediction of the mutations in KRAS/NRAS/BRAF genes of patients with CRC [77]. [PERSON] et al. also studied the prediction of KRAS/NRAS/BRAF mutations in patients with CRC and liver metastasis. The AUC for the combined model (radiomics-clinical) was 0.995 and 0.79 for the primary and validation cohorts [78]. Other studies predicted KRAS status with similar results. [PERSON] et al. studied the detection of KRAS mutation using radiomics based on CT images. Their highest accuracy was 0.83 [79]. [PERSON] et al. had a very interesting study. They compared hand-crafted (HC) radiomics with deep learning (DL) based radiomics in predicting KRAS mutation.