## 4 Results and Discussion
### Discussion  
Our study demonstrated that CT based radiomics can predict the presence of the KRAS mutation in patients with colorectal cancer. In this study, we selected the subset or radiomic features based on the texture of the CT in the cancerous region, which we considered more useful for this purpose. Nonetheless, standard intensity features of the original image (i.e., mean, energy, entropy, and variance) were also considered in the feature vector DWT. In this study, the geometrical shape descriptors were discarded in favour of texture features, although their relevance may be explored in future works. There is a substantial agreement between the data obtained from an imaging technique and an underlying genetic alteration, as reflected by the highest kappa achieved (64.7%). Texture descriptors and automatic classifiers allowed to identify, only from the data obtained from a CT image of the tumour, a characteristic pattern that is different between a colorectal cancer with mutated KRAS and a colorectal cancer without such mutation, with a sensitivity of 88.9%, a specificity of 75.0% and an accuracy of 83%. Our results are in good agreement with those from other investigators. From their publications, [PERSON] et al. closely resembles our study, and obtained a lower sensitivity (75%) and a little higher specificity (83%), with a slightly higher number of patients included in the study (61 in the main cohort) [27]. More recently, [PERSON] et al. [28] presented a method based on a Deep Learning, i.e., a ResNet network, to predict KRAS mutation in colorectal cancer. The best result they report is an AUC of 0.93, although the sensitivity and specificity (i.e., 0.59 and 1, respectively) are imbalanced. However, the patients' cohort (i.e., 117 for the training set) is not too large for training a deep learning model, which are known to need lots of training data to be adjusted [29]. Our work deals with this problem using traditional machine learning techniques (which can be trained reliably with much less samples) and handcrafted features, extracted only from the tumour area, thus avoiding pixels in the non-cancerous tissue that may distort the analysis. However, other studies consulted, such as [PERSON] et al., 2019 [30], used 36 patients, even lower than the current study. Other similar studies with different radiological techniques also show that there is a statistically significant agreement between the radiological characteristics and the mutational state of the KRAS oncogene. [PERSON] et al. studied the applications of Magnetic Resonance Imaging (MRI) in the detection of the KRAS mutation in rectal cancer, and their results also reflect lower sensitivity (84%) \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **KRAS+** & **KRAS-** \\ \hline \multirow{2}{*}{**True class**} & **KRAS+** & 24 (51.1\%) & 5 (10.6\%) \\  & **KRAS-** & 3 (6.4\%) & 15 (31.9\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix achieved with the texture descriptors and classifier that yielded the highest kappa (i.e., texture vector WCF\({}_{15}\) and classifier NNET). The percentage is shown in parenthesis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Texture Features** & **Configuration** & **Classifier** & **Kappa (\%)** \\ \hline \(\mathrm{Hf_{df}}\) & \(d\,=\,2,\,f\,=\,5\) & GBM & 17.8 \\ \(\mathrm{LPB_{Bi}}\) & \(i\,=\,3\) & RF & 34.3 \\ \(\mathrm{DWT_{sfm}}\) & \(s\,=\,3,\,f\,=\,All,\) & NNET & 34.3 \\ \(\mathrm{WCF_{dm}}\) & \(d\,=\,1,\,m\,=\,5\) & NNET & **64.7** \\ \(\mathrm{WDCF_{fm}}\) & \(m\,=\,4,\,f\,=\,All\) & SVM & 33.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Highest kappa (in %) achieved by each family of texture descriptors, with the parameters’ values and classifier for which such kappa was achieved. and accuracy (81.7%), but higher specificity (80%), in their case with a number of patients somewhat higher than ours (60) [31].