A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer  
[PERSON]  
1 Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari \"[PERSON]\", 70124 Bari, Italy; [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (E.C. ); [EMAIL_ADDRESS] (G.C.) 2 SSD Radiodigasonica Senpolgica, IRCCS Istituto Tumori \"Giovanni [PERSON]\", 70124 Bari, Italy; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari \"[PERSON]\", 70124 Bari, Italy; [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (E.C. ); [EMAIL_ADDRESS] (G.C.) [PERSON]  
3 Department of Emergency and Organ Transplantations, Rheumatology Unit, University of Bari \"[PERSON]\", 70124 Bari, Italy; [EMAIL_ADDRESS] (G.C.) [PERSON]  
1 Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari \"[PERSON]\", 70124 Bari, Italy; [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (E.C. ); [EMAIL_ADDRESS] (G.C.) [PERSON]  
1 Department of Biomedical Sciences and Human Oncology, Obstetrics and Gynecology Unit, University of Bari \"[PERSON]\", 70124 Bari, Italy; d.laforgia@oncologico. The most common histopathologic type of cervical cancer is squamous cell carcinoma, accounting for more than 80% of the cervical malignancies. The others histotypes are adenocarcinoma (up to 15%) and adenosquamous carcinoma (less than 5%) [3]. Uncommon histopathologic types are small cell or neuroendocrine, serouspapillary and clear cell. Non squamous presentations are associated with the worst prognosis [4; 5]. The most recent revision of the International Federation of Gynecology and Obstetrics (FIGO) staging system was announced in 2018 introducing the role of the imaging as a source of staging information [6; 7]. For pretreatment local staging, pelvic magnetic resonance imaging (MRI) and/or transvaginal ultrasound are the gold standard examinations. This evaluations are useful to define pelvic tumor extent, allowing accurate assessment of either tumor size, stromal invasion depth, and parametrial invasion. MRI examination is a valuable imaging method in the diagnostic work-up of macroscopically visible cervical cancers (stage \(\geq\) IB) and represents a tool for monitoring the cervical tumor response to chemotherapy [8; 9; 10]. According to 2018 FIGO Staging System [7], in early stage forms (IA, IB1, IB2, IB3, and IIA) treatment typically consists of surgery as chemoradiation makes patients susceptible to more unpredictable long-term side effects and menopause, despite equally effective; patients may undergo surgery alone if no risk factors requiring adjuvant radiation treatment are identified [7] Conversely, in locally advanced cervical cancer (FIGO stage \(\geq\) IIB), definitive management with concomitant chemoradiation is the preferred treatment [2; 11]. In patients with stage IB2, IB3, IIA, or IIB, the choice of neoadjuvant chemotherapy followed by radical hysterectomy can improve disease control and reduce toxicity [12; 13]. Additionally, several studies report that patients undergoing radical surgery after neoadjuvant chemotherapy may lead to improved survival outcomes compared with those on radiotherapy [14; 15; 16]. In several fields of science, machine learning (ML) is emerging as a promising tool for the implementation of complex multi-parametric decision algorithms [17]. In this regard, a ML approach is a potential gamechanger. In fact, in addition to detecting linear patterns in analyzed data, it can unravel complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods, merging them to output a forecast or a probability for a given outcome [18]. ML is a step towards precision medicine, leading to the improvement of patient profiling and treatment personalization.