## 4 Discussion  
As the molecular mechanisms underlying lung cancer are increasingly understood, the approach to NSCLC treatment has evolved, with a greater emphasis on identifying oncogenic driver mutations. The detection of these driver mutations serves as a crucial guide for physicians, helping them to tailor distinct therapeutic strategies for treating patients. Radiomics is a crucial technique used to achieve accurate medical imaging-based diagnosis and tailor management appropriately [21]. The core idea of radiomics is that quantitative data can potentially be extracted from medical imaging that human practitioners are unable to discern, and such data provide valuable information on the underlying pathophysiology of the tissue. Preliminary research in the area of radiomics has focused on the oncology discipline according to the finding that success in this area is predominantly influenced by tumor heterogeneity [22; 23]. Invasive biopsies taken to acquire a small number of samples are not consistently reflective of the tumor's features in in its entirety. Conversely, non-invasive evaluation of the entire volume using objective radiomics data is a significant technique used for effective treatment planning catered to the individual patient and predicting prognosis [24]. This assessment is crucial for advancing personalized medicine, where treatments are customized according to the specific characteristics of each patient and their tumors. In this regard, radiomics shows potential for personalized medicine. In the radiomics imaging analysis approach, a significant number of features are extracted from medical imaging, which are then correlated with genetic or clinical data. In the general workflow, the steps involved include acquiring images, detecting and segmenting the region of interest, extracting features, and then utilizing the features that have been extracted to form a database. While there are constraints and difficulties associated with each of the aforementioned steps, the general challenge throughout the process involves the heterogeneity of the input data. Although it remains in its infancy, the possibilities for applying radiomics should not be ignored, and it is necessary to conduct multicenter prospective studies with larger patient samples to determine how radiomics data can be applied in standard clinical practice. In the current study, we utilized CT images and clinicopathologic features from 430 patients with pulmonary adenocarcinoma or NSCLC-NOS to facilitate the development \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Indictors** & **KNN** & **SVM** & **XGBoost** & **RF** & **LR** & **DT** \\ \hline \multirow{4}{*}{_EGFR-WT_} & Precision & 0.78 & 0.82 & 0.67 & 0.66 & 0.8 & 0.38 \\ \cline{2-7}  & Recall & 0.75 & 0.85 & 0.64 & 0.64 & 0.74 & 0.55 \\ \cline{2-7}  & F1-score & 0.76 & 0.82 & 0.64 & 0.64 & 0.76 & 0.44 \\ \cline{2-7}  & Support & 11 & 11 & 11 & 11 & 11 \\ \hline \multirow{4}{*}{_EGFR_} & Precision & 0.85 & 0.88 & 0.60 & 0.80 & 0.81 & 0.74 \\ \cline{2-7}  & Recall & 0.82 & 0.85 & 0.67 & 0.66 & 0.71 & 0.58 \\ \cline{2-7}  & F1-score & 0.78 & 0.87 & 0.63 & 0.62 & 0.76 & 0.55 \\ \cline{2-7}  & Support & 24 & 24 & 24 & 24 & 24 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the four indicators—precision, recall, F1-score, and support—in the test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mutation Status**} & \multicolumn{3}{c}{**SVM**} \\ \cline{2-5}  & **False** & **Positive** & **Total Validation Data** & **Accuracy (\%)** \\ \hline _EGFR_-WT & 4 & 28 & 32 & 87.5 \\ _EGFR_m & 5 & 32 & 37 & 86.4 \\ Accuracy (\%) & 9 & 60 & 69 & 86.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Table presents the Confusion Matrix used for identifying groups using the highest learning ML Classifier, SVM. of a model based on radiomics for the purpose of predicting tumor _EGFR_ mutation status. The _EGFR_ mutation rate was 13.49% (58/430) in the selected patients.