## 5 Conclusions  
This work proposed a GP-based radiomic model for the survival prediction of patients affected by NSCLC. This method constructed new relevant and independent features from original radiomic parameters by maximizing a fitness function that measured the classification performance gain using the output-to-input AUC ratio. This approach was compared with other techniques traditionally used in radiomic feature selection and GP-based methods.  
The experimental results revealed that the proposed method increased survival prediction performance, even using challenging data like the LUNG1 dataset. Therefore, it could potentially support oncologists in determining the clinical prognosis of patients with lung cancer, with personalized treatment planning and prognosis to improve patients' outcomes. A practical application that could enhance the impact of the proposed work in clinics would be integrating GP-based signatures in a nomogram.  
Future work considers learning GP features directly from the original input images. The idea is to learn subtle features that classical radiomic features can miss. This approach has been tested on natural images with competitive results regarding deep neural networks [55]. On the other hand, our study revealed that less complex models (i.e., shallow trees) achieved better prediction performance. Hence, another research direction is to address feature construction as a multiobjective optimization problem to minimize simultaneously the error rate and the model's complexity. Hence, a Pareto front is obtained, providing different tradeoffs between the two objectives [55]. Finally, GP-based radiomics will be evaluated on other oncological applications, such as breast or prostate cancer, to test and claim the wider generalizability of the proposed approach.  
Conceptualization, E.S. and W.G.-F.; methodology, E.S. and W.G.-F.; software, E.S. and W.G.-F.; validation, E.S. and W.G.-F.; formal analysis, E.S. and W.G.-F.; investigation, E.S. and W.G.-F.; data curation, E.S.; writing--original draft preparation, E.S. and W.G.-F.; writing--review and editing, E.S., W.G.-F. and G.R.; visualization, E.S. and W.G.-F.; supervision, G.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Mexican Agency for International Development Cooperation (AMEXCID) and the Italian Ministry of Foreign Affairs and International Cooperation (MAECI) to support the project \"Development and optimization of machine learning methods for the construction of radiomic predictive models from oncological images\" under the Executive Programme for Scientific and Technological Cooperation 2025-2027.  
Not applicable.  
The data sources used in this article are publicly available at [[https://www.cancerimagingarchive.net/collection/nsclc-radiomics/](https://www.cancerimagingarchive.net/collection/nsclc-radiomics/)]([https://www.cancerimagingarchive.net/collection/nsclc-radiomics/](https://www.cancerimagingarchive.net/collection/nsclc-radiomics/)) (accessed on 6 August 2024) and [[https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/](https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/)]([https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/](https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/)) (accessed on 25 June 2024). The MATLAB source codes of the proposed approach are available at [[https://github.com/wgomezf/GP-radiomics](https://github.com/wgomezf/GP-radiomics)]([https://github.com/wgomezf/GP-radiomics](https://github.com/wgomezf/GP-radiomics)) (accessed on 4 August 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AUCArea under the ROC curve  
CNNConvolutional neural network  
CTComputer tomography  
DLDeep learning  
GAGenetic algorithm  
GLCMGray-level co-occurrence matrix  
GLDMGray-level dependence matrix