## 2 Materials and Methods  
In this retrospective observational study, we analyzed patients with locally advanced cervical cancer who were followed in a tertiary center from 2010 to 2018. All patients of our cohort underwent a pre-treatment MRI and, consequently, a pretreatment radiologic stage, according to FIGO 2018 [7], was established. All patients had either IB2, IB3, IIA1, IIA2, IIB, or IIIC1 stage (ordinal variable). They also received neoadjuvant chemotherapy and a subsequent post-treatment MRI. The treatment response was assessed by variation in tumor size according to Response Evaluation Criteria In Solid Tumors (RECIST v. 1.1, ordinal variable) [22]. In case of complete response (CR), partial response (PR) or stable disease (SD), the patients underwent radical hysterectomy with pelvic and bamboo-aortic lymphadenectomy. Radical hysterectomy type was C1. All surgery cases were performed by open surgery. Demographic features (age), clinical characteristics (Body Mass Index (BMI), parity, menopause, regime of neoadjuvant therapy and number of cycles) and progression free survival (PFS) at 24-month were collected at either treatment baseline and 24-month follow-up. Furthermore, we recorded data about MRI examinations as well as information about post-surgery histopathology (histotypes, grading, lymph node involvement). In pre and post-treatment MRI we recorded the largest diameter of lesion, the presence/absence of either lymph node involvement, fornix infiltration, parametrium infiltration, vescico-vaginal septum infiltration and recto-vaginal septum infiltration. In total, the original database included n. 92 patients and n. 24 variables. Proper feature selection was used to determine an attribute core set (see \"Attributes Selection\" paragraph for further details). This study followed STARD guidelines [23] and the TRIPOD statement [24]. The ML algorithms were aimed at forecasting PFS at 24-month follow up. Student's \(t\)-test for paired samples or [PERSON] matched-pair signed-rank test were used as appropriate to identify difference among continuous variables between different observation periods. [PERSON]'s test was used to identify the difference among dummy variables between different observation periods. The significance level at \(\alpha=0.05\) was used. The attribute core set used to train the algorithms was determined using a recursive feature elimination (RFE) wrapper based on a decision tree algorithm with extreme gradient boosting (XGBoost) [25]; in brief, this algorithm automatically selects among all the recorded attributes (n. 23) the best number of features upon their importance for predictions of the given outcome (PFS at 24 months). Feature selection may contrast overfitting problems and improves classification performance. RFE elimination method is one of the commonly used feature selection methods for small samples problems [26; 27; 28] (For further details about RFE see Supplementary Materials). The whole analysis was implemented in a Python 3.6 environment using scikit-learn (ver.0.22.1) and XGBoost (ver. 1.1.0) libraries [25; 29]. After z-score normalization, we ran a Bayesian ridge conditional imputation [30] for missing data. The latter method has proven to be the more accurate method of imputation for obstetrics and gynecology datasets [31] (see Supplementary Materials for further details). Three different classifiers, either linear and non-linear, were trained and validated with 10-fold cross-validation using the attribute core set retrieved by the RFE for predicting 24-month PFS. While logistic regression (LR) had been almost always the algorithm of choice to find independent predictors in multivariate models, it must be noticed that the study hypotheses were usually based on the unreal assumption that the association between the prognostic factors and clinical outcomes is direct and isolated. On the contrary, LR is not suitable for the modeling of non-independent variables.