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Kekatpure A, Kekatpure A, Deshpande S, Srivastava S. Development of a diagnostic support system for distal humerus fracture using artificial intelligence. INTERNATIONAL ORTHOPAEDICS 2024; 48:1303-1311. [PMID: 38499714 DOI: 10.1007/s00264-024-06125-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/18/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE AI has shown promise in automating and improving various tasks, including medical image analysis. Distal humerus fractures are a critical clinical concern that requires early diagnosis and treatment to avoid complications. The standard diagnostic method involves X-ray imaging, but subtle fractures can be missed, leading to delayed or incorrect diagnoses. Deep learning, a subset of artificial intelligence, has demonstrated the ability to automate medical image analysis tasks, potentially improving fracture identification accuracy and reducing the need for additional and cost-intensive imaging modalities (Schwarz et al. 2023). This study aims to develop a deep learning-based diagnostic support system for distal humerus fractures using conventional X-ray images. The primary objective of this study is to determine whether deep learning can provide reliable image-based fracture detection recommendations for distal humerus fractures. METHODS Between March 2017 and March 2022, our tertiary hospital's PACS data were evaluated for conventional radiography images of the anteroposterior (AP) and lateral elbow for suspected traumatic distal humerus fractures. The data set consisted of 4931 images of patients seven years and older, after excluding paediatric images below seven years due to the absence of ossification centres. Two senior orthopaedic surgeons with 12 + years of experience reviewed and labelled the images as fractured or normal. The data set was split into training sets (79.88%) and validation tests (20.1%). Image pre-processing was performed by cropping the images to 224 × 224 pixels around the capitellum, and the deep learning algorithm architecture used was ResNet18. RESULTS The deep learning model demonstrated an accuracy of 69.14% in the validation test set, with a specificity of 95.89% and a positive predictive value (PPV) of 99.47%. However, the sensitivity was 61.49%, indicating that the model had a relatively high false negative rate. ROC analysis showed an AUC of 0.787 when deep learning AI was the reference and an AUC of 0.580 when the most senior orthopaedic surgeon was the reference. The performance of the model was compared with that of other orthopaedic surgeons of varying experience levels, showing varying levels of diagnostic precision. CONCLUSION The developed deep learning-based diagnostic support system shows potential for accurately diagnosing distal humerus fractures using AP and lateral elbow radiographs. The model's specificity and PPV indicate its ability to mark out occult lesions and has a high false positive rate. Further research and validation are necessary to improve the sensitivity and diagnostic accuracy of the model for practical clinical implementation.
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Rosselló-Jiménez D, Docampo S, Collado Y, Cuadra-Llopart L, Riba F, Llonch-Masriera M. Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey. Eur Geriatr Med 2024:10.1007/s41999-024-00970-7. [PMID: 38615289 DOI: 10.1007/s41999-024-00970-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/04/2024] [Indexed: 04/15/2024]
Abstract
PURPOSE The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains. METHODS An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each. RESULTS 130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77). CONCLUSION Scores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.
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Affiliation(s)
- Daniel Rosselló-Jiménez
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain.
| | - S Docampo
- Geriatric Medicine Department, Hospital Santa Creu, Tortosa, Tortosa, Tarragona, Spain
| | - Y Collado
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain
| | - L Cuadra-Llopart
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
- ACTIUM Functional Anatomy Group, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - F Riba
- Geriatric Medicine Department, Hospital Santa Creu, Tortosa, Tortosa, Tarragona, Spain
| | - M Llonch-Masriera
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Perri A, Sbordone A, Patti ML, Nobile S, Tirone C, Giordano L, Tana M, D'Andrea V, Priolo F, Serrao F, Riccardi R, Prontera G, Lenkowicz J, Boldrini L, Vento G. The future of neonatal lung ultrasound: Validation of an artificial intelligence model for interpreting lung scans. A multicentre prospective diagnostic study. Pediatr Pulmonol 2023; 58:2610-2618. [PMID: 37417801 DOI: 10.1002/ppul.26563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/28/2023] [Accepted: 06/10/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is a promising field in the neonatal field. We focused on lung ultrasound (LU), a useful tool for the neonatologist. Our aim was to train a neural network to create a model able to interpret LU. METHODS Our multicentric, prospective study included newborns with gestational age (GA) ≥ 33 + 0 weeks with early tachypnea/dyspnea/oxygen requirements. For each baby, three LU were performed: within 3 h of life (T0), at 4-6 h of life (T1), and in the absence of respiratory support (T2). Each scan was processed to extract the region of interest used to train a neural network to classify it according to the LU score (LUS). We assessed sensitivity, specificity, positive and negative predictive value of the AI model's scores in predicting the need for respiratory assistance with nasal continuous positive airway pressure and for surfactant, compared to an already studied and established LUS. RESULTS We enrolled 62 newborns (GA = 36 ± 2 weeks). In the prediction of the need for CPAP, we found a cutoff of 6 (at T0) and 5 (at T1) for both the neonatal lung ultrasound score (nLUS) and AI score (AUROC 0.88 for T0 AI model, 0.80 for T1 AI model). For the outcome "need for surfactant therapy", results in terms of area under receiver operator characteristic (AUROC) are 0.84 for T0 AI model and 0.89 for T1 AI model. In the prediction of surfactant therapy, we found a cutoff of 9 for both scores at T0, at T1 the nLUS cutoff was 6, while the AI's one was 5. Classification accuracy was good both at the image and class levels. CONCLUSIONS This is, to our knowledge, the first attempt to use an AI model to interpret early neonatal LUS and can be extremely useful for neonatologists in the clinical setting.
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Affiliation(s)
- Alessandro Perri
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
- Department of Woman and Child Health Sciences, Child Health Area, Catholic University of Sacred Heart Seat of Rome, Rome, Lazio, Italy
| | - Annamaria Sbordone
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Maria Letizia Patti
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Stefano Nobile
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Chiara Tirone
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Lucia Giordano
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Milena Tana
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Vito D'Andrea
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Francesca Priolo
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Francesca Serrao
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Riccardo Riccardi
- Neonatal Intensive Care Unit, "San Giovanni Calibita Fatebenefratelli" Hospital, Isola Tiberina, Rome, Italy
| | - Giorgia Prontera
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
| | - Jacopo Lenkowicz
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCSS, Rome, Italy
| | - Luca Boldrini
- Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCSS, Rome, Italy
| | - Giovanni Vento
- Department of Woman and Child Health Sciences, Child Health Area, University Hospital Agostino Gemelli, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Lazio, Italy
- Department of Woman and Child Health Sciences, Child Health Area, Catholic University of Sacred Heart Seat of Rome, Rome, Lazio, Italy
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Casà C, Corvari B, Cellini F, Cornacchione P, D'Aviero A, Reina S, Di Franco S, Salvati A, Colloca GF, Cesario A, Patarnello S, Balducci M, Morganti AG, Valentini V, Gambacorta MA, Tagliaferri L. KIT 1 (Keep in Touch) Project-Televisits for Cancer Patients during Italian Lockdown for COVID-19 Pandemic: The Real-World Experience of Establishing a Telemedicine System. Healthcare (Basel) 2023; 11:1950. [PMID: 37444784 DOI: 10.3390/healthcare11131950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/09/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
To evaluate the adoption of an integrated eHealth platform for televisit/monitoring/consultation during the COVID-19 pandemic. METHODS During the lockdown imposed by the Italian government during the COVID19 pandemic spread, a dedicated multi-professional working group was set up in the Radiation Oncology Department with the primary aim of reducing patients' exposure to COVID-19 by adopting de-centralized/remote consultation methodologies. Each patient's clinical history was screened before the visit to assess if a traditional clinical visit would be recommended or if a remote evaluation was to be preferred. Real world data (RWD) in the form of patient-reported outcomes (PROMs) and patient reported experiences (PREMs) were collected from patients who underwent televisit/teleconsultation through the eHealth platform. RESULTS During the lockdown period (from 8 March to 4 May 2020) a total of 1956 visits were managed. A total of 983 (50.26%) of these visits were performed via email (to apply for and to upload of documents) and phone call management; 31 visits (1.58%) were performed using the eHealth system. Substantially, all patients found the eHealth platform useful and user-friendly, consistently indicating that this type of service would also be useful after the pandemic. CONCLUSIONS The rapid implementation of an eHealth system was feasible and well-accepted by the patients during the pandemic. However, we believe that further evidence is to be generated to further support large-scale adoption.
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Affiliation(s)
- Calogero Casà
- Fatebenefratelli Isola Tiberina-Gemelli Isola, Via di Ponte Quattro Capi 39, 00186 Rome, Italy
| | - Barbara Corvari
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Francesco Cellini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Patrizia Cornacchione
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Andrea D'Aviero
- Mater Olbia Hospital, SS 125 Orientale Sarda, 07026 Olbia, Italy
| | - Sara Reina
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Silvia Di Franco
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Alessandra Salvati
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Rome, Italy
| | | | - Alfredo Cesario
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Stefano Patarnello
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Mario Balducci
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Alessio Giuseppe Morganti
- Department of Experimental, Diagnostic and Specialty Medicine, Alma Mater Studiorum University of Bologna, Via Zamboni 33, 40126 Bologna, Italy
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Giuseppe Massarenti 9, 40138 Bologna, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Luca Tagliaferri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Rome, Italy
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Papachristou N, Kartsidis P, Anagnostopoulou A, Marshall-McKenna R, Kotronoulas G, Collantes G, Valdivieso B, Santaballa A, Conde-Moreno AJ, Domenech JR, Kokoroskos E, Papachristou P, Sountoulides P, Levva S, Avgitidou K, Tychala C, Bakogiannis C, Stafylas P, Ramon ZV, Serrano A, Tavares V, Fernandez-Luque L, Hors-Fraile S, Billis A, Bamidis PD. A Smart Digital Health Platform to Enable Monitoring of Quality of Life and Frailty in Older Patients with Cancer: A Mixed-Methods, Feasibility Study Protocol. Semin Oncol Nurs 2023; 39:151437. [PMID: 37149438 DOI: 10.1016/j.soncn.2023.151437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators. DATA SOURCES This will be a mixed-methods exploratory project, involving four study sites in Greece, Spain, Sweden, and the United Kingdom. The quantitative component of LifeChamps (single-group, pre-post feasibility study) will integrate digital technologies, home-based motion sensors, self-administered questionnaires, and the electronic health record to (1) enable multimodal, real-world data collection, (2) provide patients with a coaching mobile app interface, and (3) equip healthcare professionals with an interactive, patient-monitoring dashboard. The qualitative component will determine end-user usability and acceptability via end-of-study surveys and interviews. CONCLUSION The first patient was enrolled in the study in January 2023. Recruitment will be ongoing until the project finishes before the end of 2023. IMPLICATIONS FOR NURSING PRACTICE LifeChamps provides a comprehensive digital health platform to enable continuous monitoring of frailty indicators and health-related quality of life determinants in geriatric cancer care. Real-world data collection will generate "big data" sets to enable development of predictive algorithms to enable patient risk classification, identification of patients in need for a comprehensive geriatric assessment, and subsequently personalized care.
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Affiliation(s)
- Nikolaos Papachristou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Panagiotis Kartsidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alexandra Anagnostopoulou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Grigorios Kotronoulas
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, United Kingdom
| | | | | | - Ana Santaballa
- University and Polytechnic La Fe Hospital of Valencia, Valencia, Spain
| | | | | | | | - Panagiotis Papachristou
- Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden; Department of Neurobiology, Care Science and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
| | - Petros Sountoulides
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sophia Levva
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kelly Avgitidou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece; Healthink (Medical Research & Innovation, PC), Thessaloniki, Greece
| | - Christiana Tychala
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece; Healthink (Medical Research & Innovation, PC), Thessaloniki, Greece
| | - Costas Bakogiannis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panos Stafylas
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece; Healthink (Medical Research & Innovation, PC), Thessaloniki, Greece
| | | | | | | | | | | | - Antonios Billis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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7
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Viswanath M, Clinch D, Ceresoli M, Dhesi J, D’Oria M, De Simone B, Podda M, Di Saverio S, Coccolini F, Sartelli M, Catena F, Moore E, Rangar D, Biffl WL, Damaskos D. Perceptions and practices surrounding the perioperative management of frail emergency surgery patients: a WSES-endorsed cross-sectional qualitative survey. World J Emerg Surg 2023; 18:7. [PMID: 36653865 PMCID: PMC9850554 DOI: 10.1186/s13017-022-00471-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/25/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Frailty is associated with poor post-operative outcomes in emergency surgical patients. Shared multidisciplinary models have been developed to provide a holistic, reactive model of care to improve outcomes for older people living with frailty. We aimed to describe current perioperative practices, and surgeons' awareness and perception of perioperative frailty management, and barriers to its implementation. METHODS A qualitative cross-sectional survey was sent via the World Society of Emergency Surgery e-letter to their members. Responses were analysed using descriptive statistics and reported by themes: risk scoring systems, frailty awareness and assessment and barriers to implementation. RESULT Of 168/1000 respondents, 38% were aware of the terms "Perioperative medicine for older people undergoing surgery" (POPS) and Comprehensive Geriatric Assessment (CGA). 66.6% of respondents assessed perioperative risk, with 45.2% using the American Society of Anaesthesiologists Physical Status Classification System (ASA-PS). 77.8% of respondents mostly agreed or agreed with the statement that they routinely conducted medical comorbidity management, and pain and falls risk assessment during emergency surgical admissions. Although 98.2% of respondents agreed that frailty was important, only 2.4% performed CGA and 1.2% used a specific frailty screening tool. Clinical frailty score was the most commonly used tool by those who did. Screening was usually conducted by surgical trainees. Key barriers included a lack of knowledge about frailty assessment, a lack of clarity on who should be responsible for frailty screening, and a lack of trained staff. CONCLUSIONS Our study highlights the ubiquitous lack of awareness regarding frailty assessment and the POPS model of care. More training and clear guidelines on frailty scoring, alongside support by multidisciplinary teams, may reduce the burden on surgical trainees, potentially improving rates of appropriate frailty assessment and management of the frailty syndrome in emergency surgical patients.
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Affiliation(s)
| | - Darja Clinch
- grid.418716.d0000 0001 0709 1919Registrar in General Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Marco Ceresoli
- grid.7563.70000 0001 2174 1754General and Emergency Surgery, School of Medicine and Surgery, Milano-Bicocca University, Monza, Italy
| | - Jugdeep Dhesi
- grid.420545.20000 0004 0489 3985Department of Ageing and Health, Guy’s and St Thomas NHS Foundation Trust, London, UK
| | - Mario D’Oria
- grid.460062.60000000459364044Division of Vascular and Endovascular Surgery, Cardiovascular Departments, University Hospital of Trieste, ASUGI, Trieste, Italy
| | - Belinda De Simone
- Unit of Digestive and Bariatric Surgery, Clinique Saint Louis, Poissy, Île-de-France France
| | - Mauro Podda
- grid.7763.50000 0004 1755 3242Emergency Surgery Unit, Department of Surgical Science, University of Cagliari, Cagliari, Italy
| | - Salomone Di Saverio
- Hospital of San Benedetto del Tronto, AV5 ASUR Marche, San Benedetto del Tronto, Italy
| | - Federico Coccolini
- grid.144189.10000 0004 1756 8209Emergency and Trauma Surgery Department, Pisa University Hospital, Pisa, Italy
| | | | - Fausto Catena
- grid.414682.d0000 0004 1758 8744General and Emergency Surgery Dept, Bufalini Hospital, Cesena, Italy
| | - Ernest Moore
- grid.239638.50000 0001 0369 638XDenver Health System-Denver Health Medical Center, Denver, USA
| | - Deepa Rangar
- grid.418716.d0000 0001 0709 1919Medicine of the Elderly, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Walter L. Biffl
- grid.415402.60000 0004 0449 3295Scripps Memorial Hospital La Jolla, La Jolla, CA USA
| | - Dimitrios Damaskos
- grid.418716.d0000 0001 0709 1919Department of General Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
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8
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Cesario A, D’Oria M, Simone I, Patarnello S, Valentini V, Scambia G. Open Innovation as the Catalyst in the Personalized Medicine to Personalized Digital Medicine Transition. J Pers Med 2022; 12:jpm12091500. [PMID: 36143285 PMCID: PMC9505138 DOI: 10.3390/jpm12091500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.
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Affiliation(s)
- Alfredo Cesario
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
- Gemelli Generator, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
| | - Marika D’Oria
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Correspondence:
| | - Irene Simone
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
| | - Stefano Patarnello
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
- Gemelli Generator, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
| | - Vincenzo Valentini
- Gemelli Digital Medicine & Health, 00182 Rome, Italy
- Gemelli Generator, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
- Department of Diagnostic Imaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
| | - Giovanni Scambia
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00182 Rome, Italy
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9
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Martyushev-Poklad A, Yankevich D, Petrova M. Improving the Effectiveness of Healthcare: Diagnosis-Centered Care Vs. Person-Centered Health Promotion, a Long Forgotten New Model. Front Public Health 2022; 10:819096. [PMID: 35651862 PMCID: PMC9149093 DOI: 10.3389/fpubh.2022.819096] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 04/12/2022] [Indexed: 11/29/2022] Open
Abstract
Performance of healthcare can be measured as its ability to restore and preserve health with acceptable costs for the society. Under the current prevalence of chronic disease, medical care (the major content of healthcare) underperforms in all key indicators: clinical effectiveness, benefit/risk ratio of interventions, cost/benefit ratio, and general population health. In Russia key performance indicators (KPI) of healthcare do not allow effective decision-making; a similar situation is seen worldwide: most KPIs are either focused on the process (not results) of medical care, or depend on efforts out of control of healthcare decision-makers. The key root factors limiting clinical effectiveness and cost-effectiveness of healthcare are reactive diagnosis-centered organizational model of care and the underlying biomedical paradigm, generally inadequate in chronic diseases. They make healthcare intervene too late, use less effective prevention and treatment instruments, and be in a state of resource scarcity. In Russia there is also a lack of interdisciplinary and interagency cooperation essential for health preservation and promotion. Performance of healthcare system in overcoming the chronic disease epidemic can be improved through supplementing the current ‘reactive’ organizational model with preventive person-centered model based on the biopsychosocial paradigm. Enabling patients for early lifestyle-based interventions, the core P4 medicine approach, should prevail in managing chronic disease. Communication and information technologies should allow fast scaling up of the best person-centered practices.
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10
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Gu YF, Lin FP, Epstein RJ. How aging of the global population is changing oncology. Ecancermedicalscience 2022; 15:ed119. [PMID: 35211208 PMCID: PMC8816510 DOI: 10.3332/ecancer.2021.ed119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Indexed: 11/24/2022] Open
Abstract
Population aging is causing a demographic redistribution with implications for the future of healthcare. How will this affect oncology? First, there will be an overall rise in cancer affecting older adults, even though age-specific cancer incidences continue to fall due to better prevention. Second, there will be a wider spectrum of health functionality in this expanding cohort of older adults, with differences between “physiologically older” and “physiologically younger” patients becoming more important for optimal treatment selection. Third, greater teamwork with supportive care, geriatric, mental health and rehabilitation experts will come to enrich oncologic decision-making by making it less formulaic than it is at present. Success in this transition to a more nuanced professional mindset will depend in part on the development of user-friendly computational tools that can integrate a complex mix of quantitative and qualitative inputs from evidence-based medicine, functional and cognitive assessments, and the personal priorities of older adults.
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Affiliation(s)
- Yan Fei Gu
- New Hope Cancer Center, United Family Hospitals, 9 Jiangtai W Rd, Chaoyang, Beijing 100015, China
| | - Frank P Lin
- Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, Sydney 2010, Australia.,NH&MRC Clinical Trials Centre, 92 Parramatta Rd, Camperdown, Sydney 2050, Australia
| | - Richard J Epstein
- New Hope Cancer Center, United Family Hospitals, 9 Jiangtai W Rd, Chaoyang, Beijing 100015, China.,Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, Sydney 2010, Australia.,UNSW Clinical School, St Vincent's Hospital, 390 Victoria St, Darlinghurst, Sydney 2010, Australia.,https://orcid.org/0000-0002-4640-0195
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11
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Perju-Dumbrava L, Barsan M, Leucuta DC, Popa LC, Pop C, Tohanean N, Popa SL. Artificial intelligence applications and robotic systems in Parkinson's disease (Review). Exp Ther Med 2022; 23:153. [PMID: 35069834 PMCID: PMC8753978 DOI: 10.3892/etm.2021.11076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/05/2021] [Indexed: 11/11/2022] Open
Abstract
Parkinson's disease (PD) is the second most frequent neurodegenerative disorder following Alzheimer's disease. Advanced stages of PD, 4 or 5 of the Hoehn and Yahr Scale, are characterized by severe motor complications, limited mobility without assistance, risk of falling, and non-motor complications. The aim of this review was to provide a practical overview on specific artificial intelligence (AI) systems for the management of advanced stages of PD, as well as relevant technological limitations. The authors conducted a systematic search on PubMed and EMBASE with predefined keywords searching for studies published until December 2020. Full articles that satisfied the inclusion criteria were included in the systematic review. To minimize results bias, the reference list was manually searched for pertinent articles to identify any additional relevant missed publications. Exclusion criteria included the following: Other stages of PD than 4 and 5 of the Hoehn and Yahr Scale, case reports, reviews, practice guidelines, commentaries, opinions, letters, editorials, short surveys, articles in press, conference abstracts, conference papers, and abstracts published without a full article. The search identified 21 studies analyzing AI-based applications and robotic systems used for the management of advanced stages of PD, out of which 6 articles analyzed AI-based applications for autonomous management of pharmacologic therapy, 5 articles analyzed home-based telemedicine systems and 10 articles analysed robot-assisted gait training systems. The authors identified significant evidence demonstrating that current AI-based technologies are feasible for automatic management of patients with advanced stages of PD. Improving the quality of care and reducing the cost for patients and healthcare systems are the most important advantages.
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Affiliation(s)
- Lacramioara Perju-Dumbrava
- Department of Neurology, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Maria Barsan
- Department of Occupational Health, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Daniel Corneliu Leucuta
- Department of Medical Informatics and Biostatistics, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Luminita C. Popa
- Department of Neurology, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Cristina Pop
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Nicoleta Tohanean
- Department of Neurology, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Stefan L. Popa
- Second Medical Department, ‘Iuliu Hațieganu’ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
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12
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Oliva A, Grassi S, Vetrugno G, Rossi R, Della Morte G, Pinchi V, Caputo M. Management of Medico-Legal Risks in Digital Health Era: A Scoping Review. Front Med (Lausanne) 2022; 8:821756. [PMID: 35087854 PMCID: PMC8787306 DOI: 10.3389/fmed.2021.821756] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/20/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and processing health data is essential for the new diagnostic and decisional technologies but, at the same time, represents a risk for privacy protection. This scoping review is aimed at underlying the medico-legal and ethical implications of the main artificial intelligence applications to healthcare, also focusing on the issues of the COVID-19 era. Starting from a summary of the United States (US) and European Union (EU) regulatory frameworks, the current medico-legal and ethical challenges are discussed in general terms before focusing on the specific issues regarding informed consent, medical malpractice/cognitive biases, automation and interconnectedness of medical devices, diagnostic algorithms and telemedicine. We aim at underlying that education of physicians on the management of this (new) kind of clinical risks can enhance compliance with regulations and avoid legal risks for the healthcare professionals and institutions.
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Affiliation(s)
- Antonio Oliva
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Simone Grassi
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuseppe Vetrugno
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy.,Risk Management Unit, Fondazione Policlinico A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Riccardo Rossi
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gabriele Della Morte
- International Law, Institute of International Studies, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Vilma Pinchi
- Department of Health Sciences, Section of Forensic Medical Sciences, University of Florence, Florence, Italy
| | - Matteo Caputo
- Criminal Law, Department of Juridical Science, Università Cattolica del Sacro Cuore, Milan, Italy
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13
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Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
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14
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Bizzarri N, Nero C, Sillano F, Ciccarone F, D’Oria M, Cesario A, Fragomeni SM, Testa AC, Fanfani F, Ferrandina G, Lorusso D, Fagotti A, Scambia G. Building a Personalized Medicine Infrastructure for Gynecological Oncology Patients in a High-Volume Hospital. J Pers Med 2021; 12:jpm12010003. [PMID: 35055317 PMCID: PMC8778422 DOI: 10.3390/jpm12010003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/10/2021] [Accepted: 12/16/2021] [Indexed: 12/13/2022] Open
Abstract
Gynecological cancers require complex intervention since patients have specific needs to be addressed. Centralization to high-volume centers improves the oncological outcomes of patients with gynecological cancers. Research in gynecological oncology is increasing thanks to modern technologies, from the comprehensive molecular characterization of tumors and individual pathophenotypes. Ongoing studies are focusing on personalizing therapies by integrating information across genomics, proteomics, and metabolomics with the genetic makeup and immune system of the patient. Hence, several challenges must be faced to provide holistic benefit to the patient. Personalized approaches should also recognize the unmet needs of each patient to successfully deliver the promise of personalized care, in a multidisciplinary effort. This may provide the greatest opportunity to improve patients' outcomes. Starting from a narrative review on gynecological oncology patients' needs, this article focuses on the experience of building a research and care infrastructure for personalized patient management.
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Affiliation(s)
- Nicolò Bizzarri
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
| | - Camilla Nero
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (M.D.); (A.C.)
- Correspondence:
| | - Francesca Sillano
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
| | - Francesca Ciccarone
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (M.D.); (A.C.)
| | - Marika D’Oria
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (M.D.); (A.C.)
| | - Alfredo Cesario
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (M.D.); (A.C.)
| | - Simona Maria Fragomeni
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
| | - Antonia Carla Testa
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Francesco Fanfani
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Gabriella Ferrandina
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Domenica Lorusso
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (M.D.); (A.C.)
| | - Anna Fagotti
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Giovanni Scambia
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (N.B.); (F.S.); (F.C.); (S.M.F.); (A.C.T.); (F.F.); (G.F.); (D.L.); (A.F.); (G.S.)
- Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (M.D.); (A.C.)
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15
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Damiani A, Masciocchi C, Lenkowicz J, Capocchiano ND, Boldrini L, Tagliaferri L, Cesario A, Sergi P, Marchetti A, Luraschi A, Patarnello S, Valentini V. Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.768266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.
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16
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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