Valentina Lancellotta

Melanoma is an extremely aggressive tumor and is considered to be an extremely immunogenic tumor because compared to other cancers it usually presents a well-expressed lymphoid infiltration. The aim of this paper is to perform a multidisciplinary comprehensive review of the evidence available about the combination of radiotherapy and immunotherapy for melanoma. Radiation, in fact, can increase tumor antigens visibility and promote priming of T cells but can also exert immunosuppressive action on tumor microenvironment. Combining radiotherapy with immunotherapy provides an opportunity to increase immunostimulatory potential of radiation. We therefore provide the latest clinical evidence about radiobiological rationale, radiotherapy techniques, timing, and role both in advanced and systemic disease (with a special focus on ocular melanoma and brain, liver, and bone metastases) with a particular attention also in geriatric patients. The combination of immunotherapy and radiotherapy seems to be a safe therapeutic option, supported by a clear biological rationale, even though the available data confirm that radiotherapy is employed more for metastatic than for non-metastatic disease. Such a combination shows promising results in terms of survival outcomes; however, further studies, hopefully prospective, are needed to confirm such evidence.

PURPOSE: Clinical data collecting is expensive in terms of time and human resources. Data can be collected in different ways; therefore, performing multicentric research based on previously stored data is often difficult. The primary objective of the ENT COBRA (COnsortium for BRachytherapy data Analysis) ontology is to define a specific terminological system to standardized data collection for head and neck (H&N) cancer patients treated with interventional radiotherapy. MATERIAL AND METHODS: ENT-COBRA is a consortium for standardized data collection for H&N patients treated with interventional radiotherapy. It is linked to H&N and Skin GEC-ESTRO Working Group and includes 11 centers from 6 countries. Its ontology was firstly defined by a multicentric working group, then evaluated by the consortium followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert. RESULTS: Two hundred and forty variables were defined on 13 input forms. There are 3 levels, each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the consortium and technical commission; an ad-hoc software architecture ("broker") remaps the data present in already existing storage systems of the various centers according to the shared terminology system. The first data sharing was successfully performed using COBRA software and the ENT COBRA Ontology, automatically collecting data directly from 3 different hospital databases (Lübeck, Navarra, and Rome) in November 2017. CONCLUSIONS: The COBRA Ontology is a good response to the multi-dimensional criticalities of data collection, retrieval, and usability. It allows to create a software for large multicentric databases with implementation of specific remapping functions wherever necessary. This approach is well-received by all involved parties, primarily because it does not change a single center's storing technologies, procedures, and habits.

Background and Objectives: Cervical cancer is a leading cause of mortality among women. Chemo-radiation followed by interventional radiotherapy (IRT) is the standard of care for stage IB–IVA FIGO. Several studies have shown that image-guided adaptive IRT resulted in excellent local and pelvic control, but it is associated with vaginal toxicity and intercourse problems. The purpose of this review is to evaluate the dysfunctions of the sexual sphere in patients with cervical cancer undergoing different cervix cancer treatments. Materials and Methods: We performed a comprehensive literature search using Pub med, Scopus and Cochrane to identify all the full articles evaluating the dysfunctions of the sexual sphere. ClinicalTrials.gov was searched for ongoing or recently completed trials, and PROSPERO was searched for ongoing or recently completed systematic reviews. Results: One thousand three hundred fifty-six women included in five studies published from 2016 to 2022 were analyzed. The median age was 50 years (range 46–56 years). The median follow-up was 12 months (range 0–60). Cervical cancer diagnosis and treatment (radiotherapy, chemotherapy and surgery) negatively affected sexual intercourse. Sexual symptoms such as fibrosis, strictures, decreased elasticity and depth and mucosal atrophy promote sexual dysfunction by causing frigidity, lack of lubrication, arousal, orgasm and libido and dyspareunia. Conclusions: Physical, physiological and social factors all contribute to the modification of the sexual sphere. Cervical cancer survivors who were irradiated have lower sexual and vaginal function than the normal population. Although there are cures for reducing discomfort, effective communication about sexual dysfunctions following treatment is essential.

PURPOSE: Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy). ARTIFICIAL INTELLIGENCE IN INTERVENTIONAL RADIATION THERAPY: AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians' behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery. CONCLUSIONS: AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient's assistance in IRT.