## 1 Introduction  
Radiomics refers to the analysis of quantitative features extracted from medical images, including computerized tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). Radiomic features can be used to build models that provide valuable diagnostic, prognostic, and predictive information [1; 2]. In the last few years, radiomic features were shown to be promising predictors in several cancer-related studies [3], providing quantitative measures of tumor characteristics that would otherwise be inaccessible. [PERSON] et al. [4] showed that MRI radiomic features can be used to predict lymph node metastasis in early stage cervical cancer. A study by [PERSON] et al. [5] on cervical cancer patients revealed that it is possible to build machine-learning models using PET radiomic features to classify early stage and advanced-stage tumors. [PERSON] et al. [6] showed that some of the radiomic features extracted from PET/CT and MRI were significant predictors of recurrence in patients with cervical cancer, with higher prognostic power than usual clinical variables. Other studies have also investigated the correlation between PET/CT radiomic features and cervical cancer histological types [7; 8].  
Cervical cancer (CC) is the fourth-most frequently diagnosed cancer and the fourth-most common cause of death from cancer in women, with an estimated 604,000 new cases and 342,000 deaths worldwide in 2020 [9].  
Concurrent chemoradiation is the standard treatment option in locally advanced CC (LACC), allowing a local control of the disease in 70-80% of patients, with 66% and 58% reaching 5-year overall survival (OS) and disease-free survival (DFS) rates, respectively [10; 11].  
\({}^{18}\)F-FDG PET/CT has a well-established role in the management of patients with cervical cancer, especially in staging, treatment response, and recurrence [12; 13]. However, the prognostic role of conducting quantitative image analysis on these images to determine the OS of LACC patients is still under investigation and requires additional studies [14].  
The aim of this work was to use radiomic features extracted from PET/CT images to predict the risk of death in 85 cervical cancer patients. The patients were retrospectively selected for the study. The patients were followed for a time span ranging from a minimum of 6 to a maximum of 105 months. We developed and compared different feature selection methods to select the most informative variables for training a predictive model. Feature selection is a crucial step in radiomic analyses to address the common imbalances between the number of patients and the features in medical applications, known as ill-posed problems. We used the survival and associated survival times as labels for our survival models. The recurrence score is a clinical feature, i.e., a feature not quantified by radiomic analysis but included as an extra feature for our models and estimated at the time of collection of the PET/CT images. Two types of models were employed for prediction: the Cox proportional hazards model [15] and the random survival forest [16]. We selected these two types of models because they are standard tools used in survival analysis and allowed us to compare the behavior of linear and ensemble models, preserving easy explainability of the results. We aimed the models to predict the risk associated with the patients: a higher risk corresponded to a lower predicted survival time. We also developed alternative versions of the final models using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance.