## 1 Introduction
This issue is because the more learnable parameters, the higher the possibility that activation patterns specific to examples in the training set will happen [15]. This limitation should be considered in NSCLC survival prediction, where collecting many annotated cases is challenging. To address the above limitations, we propose enhancing radiomic features by automatically creating new ones with greater discriminant power. The created features can then be used in a traditional ML-based survival prediction system, where the prediction models are less complex than DL models and simpler to learn. We devised a feature construction approach based on genetic programming (GP) since it has proved versatile and effective in different cancer applications [19; 20; 21; 22; 23; 24] and the design of radiomics systems [25], and its application to NSCLC survival prediction is novel. GP is an evolutionary computation technique that uses Darwinian principles of reproduction and survival of the fittest to evolve a population of computer programs or mathematical expressions codified in syntactic tree representations [26]. GP-based feature construction approaches usually adopt a multi-tree representation to create a new multi-dimensional feature space, where each tree constructs a single feature [27]. Moreover, feature construction can be addressed by wrapper and filter approaches. The former measures the quality of the constructed features using classification performance, while the latter measures the statistical dependency of constructed features and the target variable. Wrapper GP-based feature construction approaches typically use a fitness function that assesses the classification performance of the constructed features at the trees' outputs [19; 20; 28; 29]. These methods assume that the constructed features should outperform the original ones at the trees' inputs. However, such an assumption is not guaranteed, and the constructed features may underperform the original ones. Our previous research involved preliminary experiments with GP-based radiomic features to improve survival prediction in NSCLC compared to the traditional ML procedure [30]. In the current research, we have extended the previous work by proposing a new fitness function that measures the classification performance ratio of output to input to evaluate the impact of increasing both the trees' depth and the number of trees (i.e., constructed features) using a multi-tree representation. This fitness function ensures that the constructed features improve upon the original radiomic features to address the wrapper approach's limitations. Moreover, considering six classification methods, the proposed GP-based feature construction approach is evaluated on LUNG1 and LUNG2 public datasets [31; 32]. Both contain NSCLC patients' CT images and survival data and have been extensively used to develop ML and DL models [11]. Therefore, using the same datasets allows for a fair comparison with previous studies to predict the same clinical outcome. It is worth remarking that clinical models tested on the LUNG1 dataset can achieve AUC values close to chance; neither radiomics nor DL models could provide high performances, achieving AUC values in the range of 0.61-0.67 [9; 11; 13; 33]. Starting from these previous works, we aim to explore whether a GP-based approach can improve the accuracy of survival prediction in NSCLC by creating new discriminant features from radiomic ones. The goal is to increase the survival prediction performance in NSCLC patients, which could be used as a support for oncologists to determine the clinical prognosis of patients.