## 4 Discussion
Female patients had an increased prevalence of _EGFR_ mutations (63.8% vs. 36.2%), as did nonsmokers (60.5% vs. 39.5%). Two groups were formed from the dataset, with a training group comprising XX VOIs of 344 patients and a test group comprising the remaining XX VOIs of 86 patients. The Radcloud platform was used to extract 1,409 quantitative imaging features from CT images, which were then categorized into three groups. Among the six radiomics-based classifiers (Decision Tree, Random Forest, Logistic Regression, eXtreme Gradient Boosting, SVM, and k-Nearest Neighbor), SVM achieved the highest AUCs in both the testing and training groups, with values of 0.869 and 0.977, respectively. These findings indicate that the accuracy with which radiomics-based models are capable of predicting tumor _EGFR_ mutation status is 86.9%. Previous studies have shown possible advantages of using CT images for the prediction of _EGFR_ mutation status in NSCLC (18-20, 25, 26). For example, the meta-analysis conducted by [PERSON] et al. (2015) revealed an increased prevalence of _EGFR_ mutations in NSCLC patients with part-solid ground glass opacities on CT images. [PERSON] et al. (2016) created a radiomics model based on CT scans from 21 _EGFR_nn and 88 _EGFR_-WT NSCLC patients, achieving an accuracy of 88.1% with an AUC of 0.85 in terms of _EGFR_ mutation status prediction, comparable to our study. Similarly, [PERSON] et al. (2018) utilized CT scans from 34 _EGFR_nn and 33 _EGFR_-WT NSCLC patients, extracting 849 features. The AUC achieved by their clinical and radiomics model was 0.9724, with 85.3% sensitivity and 90.9% specificity for the prediction of _EGFR_ mutation status ([PERSON] et al., 2018), similar to our findings. [PERSON] et al. (2018) established a model using CT scans of 99 patients with early-stage NSCLC to whom surgical resection was applied. Their clinical and radiomics combined model recorded a mean AUC of 0.83 for the prediction of _EGFR_ mutation status, further supporting the utility of radiomics in this context. According to the findings of the meta-analysis of [PERSON] et al. (2018), pure radiomics models could predict _EGFR_ mutation status with an AUC of 0.8 (95% confidence interval: 0.757-0.845), whereas combined models incorporating clinical and radiomics features yielded better results. In our study, in which a radiomics model was used, the AUC in the training group was 0.977, which concurs with the findings of other researchers. In addition, we employed up to six different classifier algorithms for model construction. By using multiple algorithms, we can comprehensively evaluate the performance of different methods to determine which one offers optimal effectiveness for our specific task. Using multiple algorithms provides a benchmark for future studies, helping to establish a standard for evaluating the performance of new methods. This approach allows us to compare the strengths and weaknesses of different algorithms, providing insights into which methods are best suited for similar tasks in future research. According to our study findings, radiomics models offer significant potential as non-invasive instruments for the prediction of _EGFR_ mutation status in lung tumors, as the accuracy is 86.9%. This non-invasive technique could be particularly valuable for patients with contraindications regarding invasive tissue biopsies, such as those on strict anticoagulation therapy or those at risk of complications from invasive procedures. Although liquid biopsy represents an alternative method for such patients, it has limitations, such as the possibility of false negative results. In contrast, radiomics offers a non-invasive approach that could limit the necessity for biopsy in cases where tissue samples are insufficient. However, it is important to acknowledge that the current study also has limitations, such as that it was conducted in a single center and the number of patients in both the training and testing groups was relatively limited.