# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
## 3 Results  
To evaluate the performance of our proposed methods, we compared the radiomics-SVM model, the CNN model, and the fusion model against several baseline models. The baseline models included in this study were the Logistic Regression (LR) model and the Random Forest (RF) model, both referenced from reference [20]. However, it is important to note that while reference [20] applied these models to predict EGFR mutation status, our study focuses on differentiating between benign post-RT changes and residual/recurrent malignancies, which presents a distinct challenge in PET/CT imaging. Due to the differences in datasets and objectives, the results from reference [20] are not directly comparable to ours. In this study, we implemented LR and RF in our dataset, and these baseline models utilized the same radiomics features as those used in our proposed SVM model. The evaluation criteria used were sensitivity, specificity, accuracy, and area under the ROCs (AUC). The ROC comparison curves for all models are illustrated in Figure 5.  
The performance of various models for predicting benign versus malignant cases is summarized in Table 4. The radiomics-LR model demonstrated a sensitivity of 0.60, specificity of 0.68, accuracy of 0.64, and an AUC of 0.66. The radiomics-RF model showed a similar sensitivity of 0.60 but slightly a lower specificity of 0.66 and an accuracy of 0.63, with an AUC of 0.68. The radiomics-SVM model achieved a sensitivity of 0.60, specificity  
Figure 4: Workflow of the fusion model combining radiomics and CNN models. of 0.64, accuracy of 0.62, and an AUC of 0.70, indicating a relatively higher AUC among the radiomics-based models. The CNN model, while having a lower sensitivity of 0.56, maintained a specificity of 0.68, an accuracy of 0.62, and an AUC of 0.65. Notably, the fusion model, which combines predictive probabilities from both radiomics-SVM and CNN models, outperformed the individual models. It achieved the highest sensitivity (0.67), specificity (0.72), accuracy (0.69), and AUC (0.72). These results indicate that the fusion model, leveraging the strengths of both radiomics-based and deep learning-based approaches, provides a more robust and accurate prediction performance for distinguishing between benign and malignant cases. This integrated approach enhances the model's ability to capture diverse and complementary aspects of the imaging data, leading to improved diagnostic capabilities. The ROC comparison curves (Figure 5) further illustrate the performance differences among the models. The fusion model consistently outperforms the individual radiomics and CNN models, as evidenced by its superior ROC curve positioning, which corresponds to higher sensitivity and specificity across various threshold settings. This graphical representation reinforces the quantitative results presented in Table 4, highlighting the efficacy of the fusion model in achieving robust predictive performance. To further demonstrate the effectiveness of the fusion strategy, we applied ER fusion to combine the predictive probabilities from the LR and RF radiomics models with the CNN model. The fusion results are presented in Table 5, showing that fusion models consistently \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensitivity** & **Specificity** & **Accuracy** & **AUC** \\ \hline Radiomics-LR [20] & 0.60 & 0.68 & 0.64 & 0.66 \\ Radiomics-RF [20] & 0.60 & 0.66 & 0.63 & 0.68 \\ Radiomics-SVM & 0.60 & 0.64 & 0.62 & 0.70 \\ CNN & 0.56 & 0.68 & 0.62 & 0.65 \\ SVM-CNN-Fusion & **0.67** & **0.72** & **0.69** & **0.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benign vs. malignancy prediction performance of the radiomics-LR model, radiomics-RF model, radiomics—model, the CNN model, and the fusion model. Figure 5: The receiver operating characteristic (ROC) curves of the radiomics-LR model, the radiomics-RF model, the radiomics-SVM model, the CNN model, and the fusion model. deliver more robust and accurate prediction performance.