## 4 Results and Discussion
### Discussion
This study also refers to the comparison between the texture analysis by magnetic resonance and by CT. It concludes that the impact of image noise may be less in magnetic resonance imaging, and the texture analysis in CT may be artifacted due to its lower contrast resolution. However, in the process of diagnosis and staging of colorectal cancer, CT is indicated in all patients with both colon cancer and rectal cancer, while the main indication for magnetic resonance imaging is located in the staging of patients with rectal cancers; therefore, its global impact in the group of patients with colorectal cancer is less than CT. Furthermore, this study only refers to T2-weighted (T2w) MRI leaving the door open for further research including other magnetic resonance imaging sequences. In other study, [PERSON] et al. also presented a T2w MRI-based radiomics signature for predicting KRAS mutation in rectal cancer [32]. The evaluation on an external validation dataset (n = 86) yielded an accuracy, sensitivity and specificity lower than ours (i.e., 69.8%, 71.1%, and 68.8%, respectively). Besides, [PERSON] et al. developed and validated radiomic models for assessing biological characteristics of rectal cancer based on multiparametric magnetic resonance imaging (MP-MRI), one of which is the KRAS-2 gene mutation status [33]. They reported an accuracy of 61.6%, a sensitivity of 58.1%, and a specificity of 64.3% on a cohort of 99 patients. This study acknowledges that, even though MP-MRI can provide more valuable data for radiomics through high-throughput extraction of quantitative image features, MRI signal is very sensitive to many parameters, such as the sequence, the strength and uniformity of the magnetic field or the normalization, and regularization methods, which may influence the classification and complicate radiomics. Our results indicate the importance that these techniques may have the future in the medical decision making, together with the possibilities in terms of monitoring and early detection of recurrence that are reflected in the results of studies, such as [PERSON] et al., 2017 [34] and [PERSON] et al., 2020 [35]. Therefore, it is essential to establish a common methodological criterion to obtain the best results that are possible and bring radiomics closer to the daily hospital clinic [35]. Regarding the best observed parameters, in our case, the texture features combining wavelet and Haralick's coefficients energy, correlation, contrast, homogeneity, and entropy (WCF\({}_{\text{dm}}\) vector with d = 1 pixels and m = 5) are clearly superior than the other texture descriptors. Regarding to the classifier, the neural network (NNET) achieved the best results for two texture families. As for the manual delimitation of the tumour by the radiologist, the automatic delimitation currently available for the segmentation of an organ or bone structure cannot be performed in a cancer because of the morphological heterogeneity of the tumour; this makes the radiologist's intervention in this process to be crucial. In any case, it is estimated that the differences between the segmentation performed by the radiologist and the segmentation that would be performed would not automatically be significantly different. In this study, a radiologist specialized in abdominal-pelvic CT analysis, with years of experience in the technique, was in charge of delimiting the tumour. Tumour delimitation lacks additional complication than being able to see the difference in texture between the tumour lesion and the wall of the abdominal viscera. Between two radiologists skilled in the art and with years of experience, the difference between the delimitations is not significant, as we verified when contrasting several delimitations made with other specialists in the field.