Identification of early predictors of clinical outcomes of COVID-19 outbreak in an Italian single center using a machine-learning approach

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 lev- els of troponin I (p-value<0.01) and IL-6 (p-val- ue<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 as- sociated 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-hospi- tal mortality and ICU admission have been iden- tified. Building a predictive model using a ma- chine-learning approach may be helpful in supporting clinical decisions in a more precise and personalized way

Generator breast datamart\u2014the novel breast cancer data discovery system for research and monitoring: Preliminary results and future perspectives

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 \u201cNot organized, not \u2018ontologized\u2019 data\u201d, \u201cOrganized, not \u2018ontologized\u2019 data\u201d, and \u201cOrganized and \u2018ontologized\u2019 data\u201d. 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

The assisi think tank meeting breast large database for standardized data collection in breast cancer\u2014attm.Blade

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, followup, 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

EP-1937 Distributed AUC algorithm: a privacypreserving approach to measure the performance of Cox models:a privacy-preserving approach to measure the performance of Cox models

Recent years have brought both a notable rise in theability to efficiently harvest vast amounts of information,and a concurrent effort in preserving and actuallyenforcing the privacy of patients and their related data,as evidenced by the European GDPR. In these conditions,the Distributed Learning Ecosystem has shown greatpotential in allowing researchers to pool the huge amountsof sensitive data need to develop and validate predictionmodels in a privacy preserving way and with an eyetowards personalized medicine.The aim of this abstract is to propose a privacy-preservingstrategy for measuring the performance of CoxProportional Hazard (PH) model

Convolutional neural network based on fluorescein angiography images for retinopathy of prematurity management

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

A new standardized data collection system for brain stereotactic external radiotherapy: The PRE.M.I.S.E project

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. Lay abstract Radiotherapy moves clinical practice toward tailored medicine, where a decision support system is essential. The Prediction Models In Stereotactic External radiotherapy (PRE.M.I.S.E) project aims to implement a system that can analyze heterogeneous datasets. This article presents the project design for brain stereotactic radiotherapy (SRT). First, a raw ontology, which is a classification system where uniform and nonambiguous definitions represent each variable and all their relationships, was defined by exploiting semiformal and natural language. It was then it was transposed in a case report form, setting a storage system. More than 130 brain SRT's variables were selected. The dedicated software BOA-Web (Beyond Ontology Awareness) was set. PRE.M.I.S.E. provides standardized data collection for SRT. Future aims are: including other centers and validating an extracranial SRT ontology

Non-melanoma skin cancer treated by contact high-dose-rate radiotherapy (brachytherapy): A mono-institutional series and literature review

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

Could the conservative approach be considered safe in the treatment of locally advanced rectal cancer in case of a clinical near-complete or complete response? A retrospective analysis

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