## 4 Discussion
can better model the survival time. Nevertheless, this approach was the worst in terms of classification performance. As shown in Table 9, related works in the literature that used traditional radiomic features to predict NSCLC two-year survival obtained similar or even lower results than our GP_GAIN approach. These studies utilized LUNG1 and LUNG2 datasets, so we can directly compare our results with these approaches. The AUC values informed by [35] were 0.65-0.67, and those reported by [9] were 0.54-0.66, whereas GP_GAIN achieved 0.64-0.70. In a recent study, [PERSON] et al. [11] demonstrated that all the studies adopting the LUNG1 dataset to assess the ability of radiomics to predict two-year survival presented AUC values in the range of 0.61-0.66. Also, [PERSON] et al. [12] included clinical information and increased the two-year survival prediction performance if the population was separately analyzed by considering tumor subtypes and stage groups. In this case, a higher prediction performance (AUC = 0.76) was achieved for patients in Stage I of the TNM (Tumor, Node, Metastasis) system. The AUC differences between previous works and our approach indicate that traditional radiomic features could not fully stratify NSCLC patients. However, constructing new GP-based features can improve the classification performance [51]. This behavior can also be confirmed by comparing GP_GAIN with two feature selection methods that selected a subset of original radiomic features: the minimal redundancy-maximal relevance method (AUC was 0.61-0.65) and the sequential forward selection method (AUC was 0.60-0.66). On the other hand, as indicated in Table 9, deep learning models obtained C-index and AUC values within the same range as, or even lower than, those obtained by traditional radiomic models. Therefore, learning deep features did not notably improve the prognostic power of radiomic models since CNNs require large training datasets to improve generalization [11], though LUNG1 is a small-sized dataset. Furthermore, CNN models are black-box models since interpreting why a particular prediction is made can be difficult [52]. In contrast, GP-based feature construction naturally produces an analytical human-readable expression represented as a tree structure, where terminal nodes select radiomic features combined with mathematical operators. Moreover, the two-year survival prediction performance improvement by the proposed GP-based approach is due to its ability to maximize the AUC output-to-input ratio. This way, the AUC value is expected to be higher with constructed features at the trees' \begin{table}
\begin{tabular}{l l l l} \hline
**Reference** & **Approach** & **Dataset** & **Main Result** \\ \hline
[9] & Radiomics & L1-T & AUC = 0.65–0.66 \\
[12] & Radiomics & L1-T, V & AUC = 0.76 \\  & & & for TNM stage I group \\
[16] & Radiomics and DL & L1-T, V & C-index = 0.623 for R + DL \\  & & & C-index = 0.585 for DL \\
[13] & Radiomics & L1-T & AUC = 0.61 \\
[33] & Radiomics & L1-T & C-index = 0.60 \\
[11] & Radiomics and DL & L1-T, V & AUC = 0.67 for R \\  & & & AUC = 0.63 for DL \\  & & & AUC = 0.67 for R + DL \\
[14] & Radiomics & L1-T & C-index = 0.62–0.73 \\  & & Other-V & C-index = 0.71, \\ Ours & GP-based radiomics & L1-T & AUC = 0.71, \\  & & L2-V & C-index = 0.66 \\ \hline \end{tabular}
\end{table} 
Table 9: The comparison of distinct related works to predict the two-year survival of NSCLC patients using the LUNG1 (L1) and LUNG2 (L2) datasets. The suffixes T and V indicate whether the dataset was used for training or validation. The radiomics and deep learning approaches are denoted as R and DL, respectively. top nodes (i.e., outputs) than the original radiomic features at the trees' terminal nodes (i.e., inputs). Therefore, the GP_GAIN fitness function outperformed GP_AUC, which maximized the output AUC value (AUC was 0.60-0.62) and GP_CORR, where the fitness function measured the relevancy-redundancy of constructed features (AUC was 0.59-0.67).