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Objectives: The primary aims of this multicenter, prospective observational study were to investigate spiritual well-being, resilience, and psychosocial distress in an Italian sample of glioblastoma patients undergoing radiochemotherapy. The secondary aim was to explore the influence of demographic, clinical, and psychological characteristics on survival., Methods: The assessment was conducted only once, within the first week of radiochemotherapy treatment. Spiritual well-being was evaluated by the Functional Assessment of Chronic Illness Therapy-Spiritual Well-being (FACIT-Sp-12), and religious/spiritual beliefs and practices were evaluated by the System of Belief Inventory. Resilience was evaluated by the Connor-Davidson Resilience Scale (CD-RISC). Psychosocial distress was evaluated the by Distress Thermometer and Hospital Anxiety Depression Scale. We conducted an univariable analysis of overall survival (OS) using data from the most recent follow-up available, considering demographic and clinical variables that could influence survival. Follow-up was defined as either the time of death or the latest follow-up visit recorded., Results: We recruited 104 patients, and the median follow-up time was 18.3 months. "Distressed" patients had lower scores than "not distressed" patients on the FACIT-Sp-12 and CD-RISC. While OS was not significant according to the FACIT-Sp-12 threshold, the Kaplan-Meier log-rank test was 0.05 according to the CD-RISC threshold. Among demographic variables, age showed significant associations with OS ( p = 0.011). Resilience showed significant associations with OS ( p = 0.025)., Significance of Results: Data showed that high spiritual well-being was associated with high resilience and an absence of psychosocial distress in our sample of glioblastoma patients undergoing radiochemotherapy. Patients with greater resilience survived longer than those with lesser resilience. Profiling spiritual well-being and resilience in glioblastoma patients undergoing radiochemotherapy can be seen as a resource to identify novel characteristics to improve clinical take-in-charge of glioblastoma patients.

Objective: SARS-CoV-2 disease (COVID-19) has become a pandemic disease, determining a public health emergency. The use of artificial intelligence in identifying easily available biomarkers capable of predicting the risk for severe disease may be helpful in guiding clinical decisions. The aim of the study was to investigate the ability of interleukin (IL)-6, troponin I, and D-dimer to identify patients with COVID-19 at risk for intensive care unit (ICU)-admission and death by using a machine-learning predictive model., Patients and Methods: Data on demographic characteristics, underlying comorbidities, symptoms, physical and radiological findings, and laboratory tests have been retrospectively collected from electronic medical records of patients admitted to Policlinico A. Gemelli Foundation from March 1, 2020, to September 15, 2020, by using artificial intelligence techniques., Results: From an initial cohort of 425 patients, 146 met the inclusion criteria and were enrolled in the study. The in-hospital mortality rate was 15%, and the ICU admission rate was 41%. Patients who died had higher troponin I (p-value<0.01) and IL-6 values (p-value=0.04), compared to those who survived. Patients admitted to ICU had higher levels of troponin I (p-value<0.01) and IL-6 (p-value<0.01), compared to those not admitted to ICU. Threshold values to predict in-hospital mortality and ICU admission have been identified. IL-6 levels higher than 15.133 ng/L have been associated with a 22.91% risk of in-hospital mortality, and IL-6 levels higher than 25.65 ng/L have been associated with a 56.16% risk of ICU admission. Troponin I levels higher than 12 ng/L have been associated with a 26.76% risk of in-hospital mortality and troponin I levels higher than 12 ng/L have been associated with a 52.11% risk of ICU admission., Conclusions: Levels of IL-6 and troponin I are associated with poor COVID-19 outcomes. Cut-off values capable of predicting in-hospital mortality and ICU admission have been identified. Building a predictive model using a machine-learning approach may be helpful in supporting clinical decisions in a more precise and personalized way.

Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19., (© 2023. The Author(s).)

In the era of evidence-based medicine, several clinical guidelines were developed, supporting cancer management from diagnosis to treatment and aiming to optimize patient care and hospital resources. Nevertheless, individual patient characteristics and organizational factors may lead to deviations from these standard recommendations during clinical practice. In this context, process mining in healthcare constitutes a valid tool to evaluate conformance of real treatment pathways, extracted from hospital data warehouses as event log, to standard clinical guidelines, translated into computer-interpretable formats. In this study we translate the European Society of Medical Oncology guidelines for rectal cancer treatment into a computer-interpretable format using Pseudo-Workflow formalism (PWF), a language already employed in pMineR software library for Process Mining in Healthcare. We investigate the adherence of a real-world cohort of rectal cancer patients treated at Fondazione Policlinico Universitario A. Gemelli IRCCS, data associated with cancer diagnosis and treatment are extracted from hospital databases in 453 patients diagnosed with rectal cancer. PWF enables the easy implementation of guidelines in a computer-interpretable format and visualizations that can improve understandability and interpretability of physicians. Results of the conformance checking analysis on our cohort identify a subgroup of patients receiving a long course treatment that deviates from guidelines due to a moderate increase in radiotherapy dose and an addition of oxaliplatin during chemotherapy treatment. This study demonstrates the importance of PWF to evaluate clinical guidelines adherence and to identify reasons of deviations during a treatment process in a real-world and multidisciplinary setting., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Savino, Chiloiro, Masciocchi, Capocchiano, Lenkowicz, Gottardelli, Gambacorta, Valentini and Damiani.)

Background/aim: Non-melanoma skin cancers (NMSC) are the most common neoplasms worldwide and their incidence has been proven to increase in recent years and their treatment should aim at cancer cure as well as cosmetic and functional results. The aim of the study was to report the results of our mono-institutional series of high-dose-rate radiotherapy (cHDR-RT) in NMSC, based on a homogenous technique and two different treatment schedules., Patients and Methods: All patients affected by NMSC who were consecutively evaluated and treated at our Interventional Oncology Center from October 2018 to August 2020, were included. Patients underwent cHDR-RT using flap applicators and remotely afterloaded Ir-192 sources., Results: Overall, 51 patients were treated for a total of 67 lesions. Local control (LC) and disease-specific survival (DSS) were 94.0% and 100%, respectively. Grade 1, grade 2, grade 3 and grade 4 acute toxicity rates were 24.6%, 3.5%, 3.5%, and 0.0%, respectively. The cosmetic results were graded as excellent/good, fair, and poor in 73.7%, 19.3%, and 7.0%., Conclusion: cHDR-RT of NMSC is an effective alternative to surgery due to excellent outcomes both in terms of local control and aesthetic results especially in the face., (Copyright © 2021 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Conservative approach has emerged as an option for the management of rectal cancer (RC) patients with a near or complete clinical response after neoadjuvant chemoradiotherapy (nCRT). The aim of this study is to assess the impact of the conservative approach by comparing patients' survival outcomes and quality of life with those who had surgical resection., Methods: A single-institution and retrospective study including RC patients who reached a near complete or complete clinical response after nCRT from January 2010 to September 2019. Conservative approaches included local excision or watch and wait strategy; surgery approaches included anterior resection or abdominal-perineal resection.Local regrowth (LR), overall survival, disease free survival, metastasis free survival and colostomy free survival were evaluated through Kaplan-Meier curves and compared trough log-rank tests. Quality of life was measured by the following validated questionnaires: EORTC QLC30, EORTC QLQ - CR29 and Fecal Incontinence Quality of Life scale., Results: Overall 157 patients were analyzed: 105 (66,9%) underwent radical surgery and 52 (33,1%) had a conservative approach. With a median follow-up of 51 months, 2 patients in the surgical group had a local recurrence and 8 in the conservative group had a LR, respectively. Distance metastasis occurred in 7 and 1 patients of surgical and conservative group, respectively. No differences were detected in terms of survival outcomes except for colostomy free survival (p: 0,01). The conservative group showed better intestinal (p < 0.01) and sexual (p: 0,04) function and emotional status (p: 0,02)., Conclusions: Conservative approach seems to be safe in terms of survival outcomes with a significant advantage on quality of life in RC patients who achieved clinical complete response after nCRT., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.)

Background: During the 2016 Assisi Think Tank Meeting (ATTM) on breast cancer, the panel of experts proposed developing a validated system, based on rapid learning health care (RLHC) principles, to standardize inter-center data collection and promote personalized treatments for breast cancer. Material and Methods: The seven-step Breast LArge DatabasE (BLADE) project included data collection, analysis, application, and evaluation on a data-sharing platform. The multidisciplinary team developed a consensus-based ontology of validated variables with over 80% agreement. This English-language ontology constituted a breast cancer library with seven knowledge domains: baseline, primary systemic therapy, surgery, adjuvant systemic therapies, radiation therapy, follow-up, and toxicity. The library was uploaded to the BLADE domain. The safety of data encryption and preservation was tested according to General Data Protection Regulation (GDPR) guidelines on data from 15 clinical charts. The system was validated on 64 patients who had undergone post-mastectomy radiation therapy. In October 2018, the BLADE system was approved by the Ethical Committee of Fondazione Policlinico Gemelli IRCCS, Rome, Italy (Protocol No. 0043996/18). Results: From June 2016 to July 2019, the multidisciplinary team completed the work plan. An ontology of 218 validated variables was uploaded to the BLADE domain. The GDPR safety test confirmed encryption and data preservation (on 5000 random cases). All validation benchmarks were met. Conclusion: BLADE is a support system for follow-up and assessment of breast cancer care. To successfully develop and validate it as the first standardized data collection system, multidisciplinary collaboration was crucial in selecting its ontology and knowledge domains. BLADE is suitable for multi-center uploading of retrospective and prospective clinical data, as it ensures anonymity and data privacy.

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer., Materials and Methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs)., Results: Data were classified into "Not organized, not 'ontologized' data", "Organized, not 'ontologized' data", and "Organized and 'ontologized' data". Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available., Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system.

Purpose: The purpose of this study was to explore the use of fluorescein angiography (FA) images in a convolutional neural network (CNN) in the management of retinopathy of prematurity (ROP)., Methods: The dataset involved a total of 835 FA images of 149 eyes (90 patients), where each eye was associated with a binary outcome (57 "untreated" eyes and 92 "treated"; 308 "untreated" images, 527 "treated"). The resolution of the images was 1600 and 1200 px in 20% of cases, whereas the remaining 80% had a resolution of 640 and 480 px. All the images were resized to 640 and 480 px before training and no other preprocessing was applied. A CNN with four convolutional layers was trained on 90% of the images ( n = 752) randomly chosen. The accuracy of the prediction was assessed on the remaining 10% of images ( n = 83). Keras version 2.2.0 for R with Tensorflow backend version 1.11.0 was used for the analysis., Results: The validation accuracy after 100 epochs was 0.88, whereas training accuracy was 0.97. The receiver operating characteristic (ROC) presented an area under the curve (AUC) of 0.91., Conclusions: Our study showed, we believe for the first time, the applicability of artificial intelligence (CNN) technology in the ROP management driven by FA. Further studies are needed to exploit different fields of applications of this technology., Translational Relevance: This algorithm is the basis for a system that could be applied to both ROP as well as experimental oxygen induced retinopathy., Competing Interests: Disclosure: D. Lepore, None; M.H. Ji, None; M.M. Pagliara, None; J. Lenkowicz, None; N.D. Capocchiano, None; L. Tagliaferri, None; L. Boldrini, None; V. Valentini, None; A. Damiani, None, (Copyright 2020 The Authors.)

Background: In recent years, novel radiation therapy techniques have moved clinical practice toward tailored medicine. An essential role is played by the decision support system, which requires a standardization of data collection. The Aim of the Prediction Models In Stereotactic External radiotherapy (PRE.M.I.S.E.) project is the implementation of systems that analyze heterogeneous datasets. This article presents the project design, focusing on brain stereotactic radiotherapy (SRT)., Materials & Methods: First, raw ontology was defined by exploiting semiformal languages (block and entity relationship diagrams) and the natural language; then, it was transposed in a Case Report Form, creating a storage system., Results: More than 130 brain SRT's variables were selected. The dedicated software Beyond Ontology Awareness (BOA-Web) was set and data collection is ongoing., Conclusion: The PRE.M.I.S.E. project provides standardized data collection for a specific radiation therapy technique, such as SRT. Future aims are: including other centers and validating an extracranial SRT ontology., Competing Interests: Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in, or financial conflict, with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript., (© 2020 Silvia Longo.)

Purpose: The primary objective of the SKIN-COBRA (Consortium for Brachytherapy data Analysis) ontology is to define a specific terminological system to standardize data collection for non-melanoma skin cancer patients treated with brachytherapy (BT, interventional radiotherapy). Through ontological characterization of information, it is possible to find, isolate, organize, and integrate its meaning., Material and Methods: SKIN-COBRA is a standardized data collection consortium for non-melanoma skin patients treated with BT, including 8 cancer centers. Its ontology was firstly defined by a multicentric and multidisciplinary working group and 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 ninety variables were defined in 10 input forms. There are 3 levels, with 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 technical commission and consortium, and an ad-hoc software system was defined to be implemented in the SKIN-COBRA consortium., Conclusions: Large databases are natural extension of traditional statistical approaches, a valuable and increasingly necessary tool for modern healthcare system. Future analysis of the collected multinational and multicenter data will show whether the use of the system can produce high-quality evidence to support multidisciplinary management of non-melanoma skin cancer and utilizing this information for personalized treatment decisions., Competing Interests: The authors report no conflict of interest., (Copyright © 2020 Termedia.)