## 2 Related Work  
### Pulmonary Nodule Prediction  
Radiomics has been a cornerstone in tumor diagnosis, staging, treatment efficacy evaluation, and prognosis prediction for nearly two decades [4; 5; 6]. Specifically, CT-based radiomics models have shown promising diagnostic performance in predicting malignant lung nodules. For instance, [PERSON] et al. [7] demonstrated that these models effectively predict malignancy through radiomics analysis of CT images. Similarly, [PERSON] et al. [8] extracted radiomics features from CT images of patients with pulmonary nodules. Using hierarchical clustering for feature selection, they developed a prediction model that combined support vector machines (SVMs) with LASSO, achieving an accuracy of 84.6%. This further underscores the potential of traditional machine learning models for medical image classification and prediction. [PERSON] et al. [9] also integrated radiomics features with clinical annotated features to improve predictive accuracy, reaching 93.6%. This approach significantly outperforms traditional methods that rely solely on clinical annotated or radiomics features. While radiomics features are effective in quantifying shallow image information and offer good interpretability, the study by [PERSON] et al. [10] demonstrates that these features also have notable limitations. These include insufficient ability to capture global image context and relatively weak noise filtering, which can affect prediction accuracy. Deep learning techniques have addressed many of the limitations of traditional detection methods, leading to significant advancements in the medical field, especially in disease detection, recognition, and image classification. One of the key innovations is the application of Convolutional Neural Networks (CNNs), and the study [11] by [PERSON], [PERSON] et al. demonstrates that they significantly improve the model's prediction performance. As an effective feature extractor, CNNs are capable of capturing high-dimensional global features from images, enabling end-to-end learning. Research [12] has shown that features automatically extracted by deep neural networks often outperform manually designed features. For example, [PERSON] et al. [13] constructed a nine-layer CNN and compared the automatically extracted features with traditional handcrafted ones. Their results showed no significant difference, demonstrating the effectiveness of CNNs in deep feature extraction. Similarly, [PERSON] et al. [14] applied CNNs to develop radiomics models for CT images of 498 pulmonary nodule patients, achieving an accuracy of 89.5%. However, the CNN architectures in both studies were relatively shallow, suggesting that their classification performance could be further improved. [PERSON] et al. [15] validated through \"Very Deep Convolutional Networks\" that deeper CNNs can capture more comprehensive semantic information from images, thereby enhancing performance. For instance, [PERSON] et al. [16] deepened their network by using a 22-layer GoogleNet to diagnose benign and malignant nodules, achieving an accuracy of 94.38%. While increasing the depth of the network allows for more detailed feature extraction, excessively deep networks can introduce challenges such as gradient vanishing, gradient explosion, and overfitting. To mitigate these issues, [PERSON] et al. [17] introduced the residual network (ResNet), which incorporates shortcut connections to prevent gradient-related problems during training. The success of ResNet has led to its widespread use in medical imaging. In a comparative study [18] conducted in 2022, ResNet-50 outperformed other CNN models. As a result, many recent studies [19; 20; 21; 22] now employ ResNet-50 or its variations as feature extractors, further demonstrating its effectiveness in the field. In current studies, radiology or CNNs are commonly used to extract nodule features from CT images of lung nodules. These features are then processed by traditional machine learning models or optimized for metastasis-based prediction.