## 4 Discussion  
The pillar of survival analyses in oncologic research had historically been Cox proportional hazard regression model, being a surrogate for estimating treatment effectiveness and safety. This model is based on an assumption of linear association. However, many clinicopathologic features exhibit a nonlinear association in medicine [39]. Conversely in the area of cervical cancer research, ML can be used for supporting the study of human papillomavirus-related disease, evaluating either cervical cytology, colposcopy and genomic analysis [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. However, there are only a few studies that have examined oncologic outcome [52]. This is the first study that wants to analyze the accuracy of a ML modeling to predict the response to neoadjuvant chemotherapy in patients with locally advanced cervical cancer. [PERSON] et al. [53] studied predictors of clinical outcome in patients with locally advanced cervical cancer treated with radical hysterectomy followed neoadjuvant chemotherapy using traditional statistics. This study stated that an optimal pathological response was the most relevant predictor for disease-free survival (DFS) and overall survival (OS). The involvement of the parameters and/or margins of surgical resection was the other independent predictor; vice versa, the lymph node status and the involvement of the lymphovascular spaces correlated with DFS and OS. Figure 1: Receiver operating characteristics curve for Random Forest (box (**A**)), Logistic Regression (box (**B**)) and K-nearest neighbors (box (**C**)) models. Figure 2: Calibration diagnostics for RFF model. 24-month PFS roughly happened with an observed relative frequency consistent with the forecast value, showing good calibration. A study of [PERSON] et al. established prognostic value of pathological response to neoadjuvant chemotherapy in 204 patients affected with stage IB2--IIA cervical squamous cell cancer. Clinical response and FIGO stage are variables statistically associated with DFS. Patient age, histological grade and chemotherapy regimen result not associated with DFS. An optimal pathological response to neoadjuvant chemotherapy has been shown to be associated with improved long-term outcome. In this study tumor regression results to be an independent prognostic factor for survival performing the multivariate analysis. Moreover, in patients without extra-cervical deposits, an association between survival rate and chemotherapy response grade was shown, while this was not true in patients with extra-cervical deposits. This implies that when the tumor is confined to the cervix, residual viable tumor cells has an impact on prognosis. Although this is not true when tumor cells have spread outside the cervix [54]. The role of extra-cervical deposits (vaginal disease, nodal metastasis, parametrial involvement) in determining the prognosis of cervical cancer patients after NACT has been reported in previous studies. [PERSON] et al. demonstrated that pelvic lymph node metastasis was the only histopathologically independent prognostic factor (\(p\) = 0.0029) [55]. [PERSON] et al. showed that lymph node metastases and involved parametria were the only two independent factors for survival [56]. From all the recorded variables in our cohort, the automated attribute selection algorithm selected the presence/absence of fornix infiltration at pre-treatment MRI as well as presence/absence of either parametrium invasion and lymph nodes involvement at post-radical surgery histopathology as the attribute core set to be used in ML models training and validation. The presence/absence of either parametrium invasion and lymph nodes involvement at post-radical surgery histopathology had been already evaluated as predictors in previous studies [53; 55; 56]. On the other hand, the presence/absence of fornix infiltration at pre-treatment MRI was not considered as a classical predictor of response to neoadjuvant chemotherapy.