## 1 Introduction  
Cervical cancer (CC) represents one of the most common and severe female cancers. More than 500,000 new cases per year are diagnosed in the world and despite the significant therapeutic improvements achieved in the recent years, the number of deaths per year ranges from 250,000 to 350,000 [1; 2; 3]. The clinical management of CC involves different specialists, with the aim of tailoring the treatment on the basis of the tumor staging [4].  
According to the Federation Internationale de Gynecologie et d'Obstetrique (FIGO) classification, stage IB2-IVA patients are considered as locally advanced cervical cancer (LACC). The standard of care for these patients is represented by neoadjuvant chemoradiotherapy (nCRT) followed by brachytherapy boost, with the primary aim of organ preservation [5].  
Despite the good results in terms of overall survival and local control achieved through this approach, alternative approaches have been investigated in the scientific community to further reduce the rate of treatment failure [6; 7].  
In this context, some experiences have reported the advantage to administer neo-adjuvant external beam radiotherapy with concomitant chemotherapy followed by radical surgery, aiming to remove the residual tumor foci resistant to the previous treatment and reporting favorable rates of local control and disease-free survival, with acceptable acute and long-term toxicity profiles [8; 9; 10].  
The response to nCRT followed by surgery is quantified on the basis of the histological evaluation of the surgical specimen: Pathological complete response (pCR) is achieved when no residual tumor foci are present.  
Clinical experiences reported pCR rates of about 50% for LACC patients undergoing nCRT, also observing that the pCR status is associated with higher rates of long-term and disease-free survival [11; 12; 13; 14].  
Regardless of the clinical approach adopted, there is growing interest towards the identification of treatment response predictors, to move towards a more personalized clinical approach, avoiding unnecessary surgical procedures in patients with high probability of pCR or modifying the clinical management in the case of poor responders.  
Different models have been proposed in literature to predict treatment response in CC, some of them including only clinical data, others combining also image parameters extracted from magnetic resonance (MR) or computed tomography (CT) images acquired at disease staging [15; 16; 17; 18].  
Magnetic resonance imaging (MRI) is considered the elective imaging modality for diagnosis and staging of CC and the recent development of hybrid MRI-radiotherapy systems, combining a linear accelerator with an on-board MR scanner, may represent a significant step-forward in the clinical management of this disease, as demonstrated by some preliminary experiences [19; 20; 21].  
The availability of daily MR images leads to the possibility to define image-based predictive models able to study the patient treatment sensitivity by means of the analysis of the MR images acquired during the course of treatment.  
In this context, a radiobiological parameter able to predict the treatment response in the locally advanced rectal cancer has been recently proposed and validated, modelling the tumor shrinkage during the first weeks of nCRT treatment through a statistical approach. This parameter, known as early regression index (ERI\({}_{\text{TCP}}\)), combining the tumor volume measured on the MR images acquired at simulation and at mid-therapy, is able to identify the patients who will have complete response with high AUC value on low and high field MR images [22; 23].  
Aim of this hypothesis generating study is to evaluate the applicability of this parameter in the context of response of nCRT in CC, quantifying its performance in predicting pCR by using MR images acquired using a low field MR-guided radiotherapy unit [24].