## 5 Conclusions  
The obtained results showed that the subset of the radiomic features analyzed in this study can be successfully used to predict overall survival in cervical cancer patients. In particular, the Cox model trained with the CT radiomic features selected by the GA achieved a significant result on test data, with a CI score of 0.707. Moreover, the addition of recurrence as an extra feature further improved the predictive power of the model (+7%). The difference between the performance of the CT and PET Cox models confirmed the low efficiency of PET features in catching small tumor structure details due to the low resolution of the data. This was also confirmed by the type of features selected by the GA for the two feature sets. For CT, seven of the nine selected features were textural features, while only two textural features were selected for PET.  
The scores achieved by the RSF models highlight the importance of the feature selection task. These ensemble models, trained using all the available features, performed substantially worse than the Cox CT model.  
The proposed GA was significantly better in feature selection than the widely used LASSO regression. The integration of GA with the Cox proportional hazard model allowed the selection of meaningful predictive features, addressing the problem of dimensionality reduction required by radiomic applications. Furthermore, the GA feature selection technique developed in this study can be adapted to different types of models and different metrics with minimum effort.  
Further improvements can be achieved, including a feature importance criterion in the proposed pipeline, repeating the feature extraction procedure several times. The developed pipeline may also be employed to predict the probability of recurrence outcome, after the definition of an inclusion criterion for the subjects that experienced recurrence more than once. Finally, the model may be further improved by including other clinical variables and investigating their relationships with the radiomic features already found.  
The results obtained by the proposed fully automated pipeline proved its suitability for usage in clinical practice, proving the effectiveness of radiomic analysis in CT/PET imaging. However, the results presented in this work must be validated by a multicenter study with a larger cohort of patients.  
**Author Contributions:** Conceptualization, E.G., D.R. and G.C. ([PERSON]); data curation, S.S., S.F., C.N. and L.S.; formal analysis, G.C. ([PERSON]), N.C., C.S., D.D. and A.M.; methodology, G.C. ([PERSON]), N.C., E.G., C.S., D.D., A.M. and L.S.; project administration, L.S. and G.C. ([PERSON]); software, G.C. ([PERSON]) and N.C.; supervision, L.S. and G.C. ([PERSON]); validation, S.S., S.F. and C.N.; writing--original draft, G.C. ([PERSON]), N.C.; E.G., D.D. and A.M.; writing--review and editing, S.S., C.S., S.F., D.R., C.N., L.S. and G.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board (or Ethics Committee) of IRCCS University Hospital di Bologna (protocol code n\({}^{\circ}\) CE 322/2019/Oss/AUOBo).  
**Informed Consent Statement:** This study was an observational, retrospective, single-center study that was approved by our local institution review board. Informed consent was waived by the institutional review board owing to the retrospective nature of the study.  
**Data Availability Statement:** The data collected in loco are available on request due to restrictions on privacy (European GDPR).  
**Acknowledgments:** The study was partially supported by AIRC IG 20809 (2017) PI: [PERSON] and by AIRC IG 26216 (2021) PI: [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.