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Choudhury A, Janssen E, Bongers BC, van Meeteren NLU, Dekker A, van Soest J. Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train. BMC Med Inform Decis Mak 2024; 24:121. [PMID: 38724966 PMCID: PMC11080148 DOI: 10.1186/s12911-024-02526-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVE Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric 'Proof of Principle' study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. MATERIALS AND METHODS Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. RESULTS ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. DISCUSSION AND CONCLUSION PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures.
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Affiliation(s)
- Ananya Choudhury
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
- Clinical Data Science Group, Faculty of Health Medicine and Life Sciences, Maastricht University Medical Center+, Paul-Henri Spaaklaan 1, Maastricht, 6229 GT, Netherlands.
| | - Esther Janssen
- Department of Orthopaedics, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Orthopaedic Surgery, VieCuri Medical Center, Venlo, The Netherlands
| | - Bart C Bongers
- Department of Nutrition and Movement Sciences, Faculty of Health, Medicine and Life Sciences, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, the Netherlands
- Department of Epidemiology, Faculty of Health, Medicine and Life Sciences, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands
| | - Nico L U van Meeteren
- Top Sector Life Sciences and Health (Health∼Holland), the Hague, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Topcare, Leiden, the Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Brightlands Institute for Smart Society (BISS), Faculty of Science and Engineering (FSE), Maastricht University, Heerlen, the Netherlands
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Valentini V, Alfieri S, Coco C, D'Ugo D, Crucitti A, Pacelli F, Persiani R, Sofo L, Picciocchi A, Doglietto GB, Barbaro B, Vecchio FM, Ricci R, Damiani A, Savino MC, Boldrini L, Cellini F, Meldolesi E, Romano A, Chiloiro G, Gambacorta MA. Four steps in the evolution of rectal cancer managements through 40 years of clinical practice: Pioneering, standardization, challenges and personalization. Radiother Oncol 2024; 194:110190. [PMID: 38438019 DOI: 10.1016/j.radonc.2024.110190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024]
Affiliation(s)
- Vincenzo Valentini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Sergio Alfieri
- Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Claudio Coco
- U.O.C. Chirurgia Generale 2, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Domenico D'Ugo
- Unità di chirurgia generale, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | - Fabio Pacelli
- Unità chirurgica del peritoneo e del retroperitoneo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Roberto Persiani
- Unità di chirurgia generale, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luigi Sofo
- Divisione di Chirurgia Addominale, Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Aurelio Picciocchi
- Dipartimento di Chirurgia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giovanni Battista Doglietto
- Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Brunella Barbaro
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Fabio Maria Vecchio
- Dipartimento di Patologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Riccardo Ricci
- Dipartimento di Patologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Andrea Damiani
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Chiara Savino
- Gemelli Generator Real World Data Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Cellini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Elisa Meldolesi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
| | - Maria Antonietta Gambacorta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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Savino M, Chiloiro G, Masciocchi C, Capocchiano ND, Lenkowicz J, Gottardelli B, Gambacorta MA, Valentini V, Damiani A. A process mining approach for clinical guidelines compliance: real-world application in rectal cancer. Front Oncol 2023; 13:1090076. [PMID: 37265796 PMCID: PMC10231435 DOI: 10.3389/fonc.2023.1090076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 06/03/2023] Open
Abstract
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.
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Affiliation(s)
- Mariachiara Savino
- Diagnostica per immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuditta Chiloiro
- Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Carlotta Masciocchi
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nikola Dino Capocchiano
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Benedetta Gottardelli
- Diagnostica per immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Maria Antonietta Gambacorta
- Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Andrea Damiani
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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Miccichè F, Chiloiro G, Longo S, Autorino R, Massaccesi M, Lenkowicz J, Bonomo P, Desideri I, Belgioia L, Bacigalupo A, D’Angelo E, Bertolini F, Merlotti A, Denaro N, Franco P, Bussu F, Paludetti G, Ricardi U, Valentini V. Development of a prognostic model of overall survival in oropharyngeal cancer from real-world data: PRO.M.E.THE.O. ACTA OTORHINOLARYNGOLOGICA ITALICA 2022; 42:205-214. [PMID: 35396587 PMCID: PMC9330744 DOI: 10.14639/0392-100x-n1672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/11/2021] [Indexed: 11/23/2022]
Abstract
Objective The PRO.M.E.THE.O. study (PredictiOn Models in Ent cancer for anti-EGFR based THErapy Optimization) aimed to develop a predictive model (PM) of overall survival (OS) for patients with locally advanced oropharyngeal cancer (LAOC) treated with radiotherapy (RT) and cetuximab (Cet) from an Italian dataset. Methods We enrolled patients with LAOC from 6 centres treated with RT-Cet. Clinical and treatment variables were collected. Patients were randomly divided into training (TS) (80%) and validation (VS) (20%) sets. A binary logistic regression model was used on the TS with stepwise feature selection and then on VS. Timepoints of 2, 3 and 5 years were considered. The area under the curve (AUC) of receiver operating characteristic of 2, 3 and 5 year and confusion matrix statistics at 5-threshold were used as performance criteria. Results Overall, 218 patients were enrolled and 174 (79.8%) were analysed. Age at diagnosis, gender, ECOG performance, clinical stage, dose to high-risk volume, overall treatment time and day of RT interruption were considered in the final PMs. The PMs were developed and represented by nomograms with AUC of 0.75, 0.73 and 0.73 for TS and 0.713, 0.713, 0.775 for VS at 2, 3 and 5 years, respectively. Conclusions PRO.M.E.THE.O. allows the creation of a PM for OS in patients with LAOC treated with RT-Cet.
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Murri R, Masciocchi C, Lenkowicz J, Fantoni M, Damiani A, Marchetti A, Sergi PDA, Arcuri G, Cesario A, Patarnello S, Antonelli M, Bellantone R, Bernabei R, Boccia S, Calabresi P, Cambieri A, Cauda R, Colosimo C, Crea F, De Maria R, De Stefano V, Franceschi F, Gasbarrini A, Landolfi R, Parolini O, Richeldi L, Sanguinetti M, Urbani A, Zega M, Scambia G, Valentini V. A real-time integrated framework to support clinical decision making for covid-19 patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106655. [PMID: 35158181 PMCID: PMC8800500 DOI: 10.1016/j.cmpb.2022.106655] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 01/15/2022] [Accepted: 01/20/2022] [Indexed: 05/08/2023]
Abstract
BACKGROUND The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.
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Affiliation(s)
- Rita Murri
- Dipartimento di Scienze di Laboratorio e Infettivologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Sicurezza e Bioetica, Sezione Malattie Infettive, Università Cattolica S. Cuore, Roma, Italy.
| | | | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Massimo Fantoni
- Dipartimento di Scienze di Laboratorio e Infettivologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Sicurezza e Bioetica, Sezione Malattie Infettive, Università Cattolica S. Cuore, Roma, Italy
| | - Andrea Damiani
- Istituto di Radiologia, Università Cattolica Sacro Cuore, Roma, Italy
| | - Antonio Marchetti
- Datawarehouse, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | | | - Giovanni Arcuri
- Unità Operativa Complessa Tecnologie Sanitarie, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Alfredo Cesario
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | | | - Massimo Antonelli
- Dipartimento di Scienze dell'Emergenza, Anestesiologiche e della Rianimazione, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Biotecnologiche di base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Rocco Bellantone
- Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Medicina e chirurgia traslazionale, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Roberto Bernabei
- Dipartimento di Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Geriatriche ed Ortopediche, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Stefania Boccia
- Dipartimento di Scienze della Salute della Donna e del Bambino e Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di scienza della vita e sanità pubblica, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Paolo Calabresi
- Dipartimento di Scienze dell'Invecchiamento, Neurologiche, Ortopediche e della Testa-collo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Neuroscienze, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Andrea Cambieri
- Direzione Sanitaria Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Roberto Cauda
- Dipartimento di Scienze di Laboratorio e Infettivologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Sicurezza e Bioetica, Sezione Malattie Infettive, Università Cattolica S. Cuore, Roma, Italy
| | - Cesare Colosimo
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Filippo Crea
- Dipartimento di Scienze Cardiovascolari, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Cardiovascolari e Pneumologiche, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Ruggero De Maria
- Dipartimento di Medicina e chirurgia traslazionale, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Valerio De Stefano
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Francesco Franceschi
- Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Medicina e chirurgia traslazionale, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Antonio Gasbarrini
- Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Medicina e chirurgia traslazionale, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Raffaele Landolfi
- Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Medicina e chirurgia traslazionale, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Ornella Parolini
- Dipartimento di scienza della vita e sanità pubblica, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Luca Richeldi
- Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Cardiovascolari e Pneumologiche, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Maurizio Sanguinetti
- Dipartimento di Scienze di Laboratorio e Infettivologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Biotecnologiche di base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Andrea Urbani
- Dipartimento di Scienze di Laboratorio e Infettivologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Biotecnologiche di base, Cliniche Intensivologiche e Perioperatorie, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Maurizio Zega
- Servizio Infermieristico, Tecnico e Riabilitativo Aziendale, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Giovanni Scambia
- Dipartimento di Scienze della Salute della Donna e del Bambino e Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di scienza della vita e sanità pubblica, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Vincenzo Valentini
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Roma, Italy
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The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer-ATTM.BLADE. J Pers Med 2021; 11:jpm11020143. [PMID: 33669549 PMCID: PMC7926376 DOI: 10.3390/jpm11020143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/05/2021] [Accepted: 02/08/2021] [Indexed: 01/09/2023] Open
Abstract
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.
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A new standardized data collection system for brain stereotactic external radiotherapy: the PRE.M.I.S.E project. Future Sci OA 2020; 6:FSO596. [PMID: 32802398 PMCID: PMC7421993 DOI: 10.2144/fsoa-2020-0015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
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. 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.
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Walsh S, de Jong EEC, van Timmeren JE, Ibrahim A, Compter I, Peerlings J, Sanduleanu S, Refaee T, Keek S, Larue RTHM, van Wijk Y, Even AJG, Jochems A, Barakat MS, Leijenaar RTH, Lambin P. Decision Support Systems in Oncology. JCO Clin Cancer Inform 2020; 3:1-9. [PMID: 30730766 PMCID: PMC6873918 DOI: 10.1200/cci.18.00001] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.
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Affiliation(s)
- Seán Walsh
- Maastricht University, Maastricht, the Netherlands
| | | | | | | | - Inge Compter
- Maastricht University, Maastricht, the Netherlands
| | | | | | | | - Simon Keek
- Maastricht University, Maastricht, the Netherlands
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9
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Kairn T, Crowe SB. Retrospective analysis of breast radiotherapy treatment plans: Curating the 'non-curated'. J Med Imaging Radiat Oncol 2019; 63:517-529. [PMID: 31081603 DOI: 10.1111/1754-9485.12892] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 03/24/2019] [Indexed: 11/29/2022]
Abstract
INTRODUCTION This paper provides a demonstration of how non-curated data can be retrospectively cleaned, so that existing repositories of radiotherapy treatment planning data can be used to complete bulk retrospective analyses of dosimetric trends and other plan characteristics. METHODS A non curated archive of 1137 radiotherapy treatment plans accumulated over a 12-month period, from five radiotherapy centres operated by one institution, was used to investigate and demonstrate a process of clinical data cleansing, by: identifying and translating inconsistent structure names; correcting inconsistent lung contouring; excluding plans for treatments other than breast tangents and plans without identifiable PTV, lung and heart structures; and identifying but not excluding plans that deviated from the local planning protocol. PTV, heart and lung dose-volume metrics were evaluated, in addition to a sample of personnel and linac load indicators. RESULTS Data cleansing reduced the number of treatment plans in the sample by 35.7%. Inconsistent structure names were successfully identified and translated (e.g. 35 different names for lung). Automatically separating whole lung structures into left and right lung structures allowed the effect of contralateral and ipsilateral lung dose to be evaluated, while introducing some small uncertainties, compared to manual contouring. PTV doses were indicative of prescription doses. Breast treatment work was unevenly distributed between oncologists and between metropolitan and regional centres. CONCLUSION This paper exemplifies the data cleansing and data analysis steps that may be completed using existing treatment planning data, to provide individual radiation oncology departments with access to information on their own patient populations. Clearly, the well-planned and systematic recording of new, high quality data is the preferred solution, but the retrospective curation of non-curated data may be a useful interim solution, for radiation oncology departments where the systems for recording of new data have yet to be designed and agreed.
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Affiliation(s)
- Tanya Kairn
- Genesis Cancer Care, Auchenflower, Queensland, Australia.,Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Scott B Crowe
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.,Cancer Care Services, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
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10
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Tagliaferri L, Budrukkar A, Lenkowicz J, Cambeiro M, Bussu F, Guinot JL, Hildebrandt G, Johansson B, Meyer JE, Niehoff P, Rovirosa A, Takácsi-Nagy Z, Boldrini L, Dinapoli N, Lanzotti V, Damiani A, Gatta R, Fionda B, Lancellotta V, Soror T, Monge RM, Valentini V, Kovács G. ENT COBRA ONTOLOGY: the covariates classification system proposed by the Head & Neck and Skin GEC-ESTRO Working Group for interdisciplinary standardized data collection in head and neck patient cohorts treated with interventional radiotherapy (brachytherapy). J Contemp Brachytherapy 2018; 10:260-266. [PMID: 30038647 PMCID: PMC6052377 DOI: 10.5114/jcb.2018.76982] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 06/18/2018] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Clinical data collecting is expensive in terms of time and human resources. Data can be collected in different ways; therefore, performing multicentric research based on previously stored data is often difficult. The primary objective of the ENT COBRA (COnsortium for BRachytherapy data Analysis) ontology is to define a specific terminological system to standardized data collection for head and neck (H&N) cancer patients treated with interventional radiotherapy. MATERIAL AND METHODS ENT-COBRA is a consortium for standardized data collection for H&N patients treated with interventional radiotherapy. It is linked to H&N and Skin GEC-ESTRO Working Group and includes 11 centers from 6 countries. Its ontology was firstly defined by a multicentric working group, then evaluated by the consortium followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert. RESULTS Two hundred and forty variables were defined on 13 input forms. There are 3 levels, each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the consortium and technical commission; an ad-hoc software architecture ("broker") remaps the data present in already existing storage systems of the various centers according to the shared terminology system. The first data sharing was successfully performed using COBRA software and the ENT COBRA Ontology, automatically collecting data directly from 3 different hospital databases (Lübeck, Navarra, and Rome) in November 2017. CONCLUSIONS The COBRA Ontology is a good response to the multi-dimensional criticalities of data collection, retrieval, and usability. It allows to create a software for large multicentric databases with implementation of specific remapping functions wherever necessary. This approach is well-received by all involved parties, primarily because it does not change a single center's storing technologies, procedures, and habits.
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Affiliation(s)
- Luca Tagliaferri
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | | | - Jacopo Lenkowicz
- Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | | | - Francesco Bussu
- Head of the Otolaryngology Division, Azienda Ospedaliero-Universitaria di Sassari, Italy
| | - Jose Luis Guinot
- Department of Radiation Oncology, Fundacion Instituto Valenciano de Oncologia, Valencia, Spain
| | - Guido Hildebrandt
- University Hospital Radiotherapy Department, University of Rostock, Germany
| | - Bengt Johansson
- Department of Oncology, Orebro University Hospital and Orebro University, Sweden
| | - Jens E. Meyer
- Head & Neck Surgery Department, AK St. George Hospital, Hamburg, Germany
| | - Peter Niehoff
- Department of Radiotherapy, Sana Hospital Offenbach, Offenbach, Germany
| | | | | | - Luca Boldrini
- Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Nicola Dinapoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Vito Lanzotti
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Andrea Damiani
- Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Roberto Gatta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Bruno Fionda
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Valentina Lancellotta
- Radiation Oncology Section, Department of Surgical and Biomedical Science, University of Perugia and Perugia General Hospital, Perugia, Italy
| | - Tamer Soror
- Interdisciplinary Brachytherapy Unit, University of Lübeck – University Hospital S-H, Campus Lübeck, Germany
| | | | - Vincenzo Valentini
- Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | - György Kovács
- Interdisciplinary Brachytherapy Unit, University of Lübeck – University Hospital S-H, Campus Lübeck, Germany
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Bibault JE, Zapletal E, Rance B, Giraud P, Burgun A. Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology. PLoS One 2018; 13:e0191263. [PMID: 29351341 PMCID: PMC5774757 DOI: 10.1371/journal.pone.0191263] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 01/01/2018] [Indexed: 12/25/2022] Open
Abstract
Purpose Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant amount of patients with a high level of clinical details, multicenter studies are necessary. A challenge in creating high quality Big Data studies involving several treatment centers is the lack of semantic interoperability between data sources. We present the ontology we developed to address this issue. Methods Radiation Oncology anatomical and target volumes were categorized in anatomical and treatment planning classes. International delineation guidelines specific to radiation oncology were used for lymph nodes areas and target volumes. Hierarchical classes were created to generate The Radiation Oncology Structures (ROS) Ontology. The ROS was then applied to the data from our institution. Results Four hundred and seventeen classes were created with a maximum of 14 children classes (average = 5). The ontology was then converted into a Web Ontology Language (.owl) format and made available online on Bioportal and GitHub under an Apache 2.0 License. We extracted all structures delineated in our department since the opening in 2001. 20,758 structures were exported from our “record-and-verify” system, demonstrating a significant heterogeneity within a single center. All structures were matched to the ROS ontology before integration into our clinical data warehouse (CDW). Conclusion In this study we describe a new ontology, specific to radiation oncology, that reports all anatomical and treatment planning structures that can be delineated. This ontology will be used to integrate dosimetric data in the Assistance Publique—Hôpitaux de Paris CDW that stores data from 6.5 million patients (as of February 2017).
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Affiliation(s)
- Jean-Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), Paris Descartes University, Paris Sorbonne Cité, Paris, France
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France
- * E-mail:
| | - Eric Zapletal
- Biomedical Informatics and Public Health Department, Georges Pompidou European Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Bastien Rance
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France
- Biomedical Informatics and Public Health Department, Georges Pompidou European Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Philippe Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Anita Burgun
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France
- Biomedical Informatics and Public Health Department, Georges Pompidou European Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), Paris Descartes University, Paris Sorbonne Cité, Paris, France
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Abstract
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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13
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van Soest J, Meldolesi E, van Stiphout R, Gatta R, Damiani A, Valentini V, Lambin P, Dekker A. Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility. Med Phys 2017. [PMID: 28639302 DOI: 10.1002/mp.12423] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Multiple models have been developed to predict pathologic complete response (pCR) in locally advanced rectal cancer patients. Unfortunately, validation of these models normally omit the implications of cohort differences on prediction model performance. In this work, we will perform a prospective validation of three pCR models, including information whether this validation will target transferability or reproducibility (cohort differences) of the given models. METHODS We applied a novel methodology, the cohort differences model, to predict whether a patient belongs to the training or to the validation cohort. If the cohort differences model performs well, it would suggest a large difference in cohort characteristics meaning we would validate the transferability of the model rather than reproducibility. We tested our method in a prospective validation of three existing models for pCR prediction in 154 patients. RESULTS Our results showed a large difference between training and validation cohort for one of the three tested models [Area under the Receiver Operating Curve (AUC) cohort differences model: 0.85], signaling the validation leans towards transferability. Two out of three models had a lower AUC for validation (0.66 and 0.58), one model showed a higher AUC in the validation cohort (0.70). DISCUSSION We have successfully applied a new methodology in the validation of three prediction models, which allows us to indicate if a validation targeted transferability (large differences between training/validation cohort) or reproducibility (small cohort differences).
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Affiliation(s)
- Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6062, NA, the Netherlands
| | - Elisa Meldolesi
- Department of Radiotherapy, Sacred Heart University Hospital, Rome, 00168, Italy
| | - Ruud van Stiphout
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6062, NA, the Netherlands
| | - Roberto Gatta
- Department of Radiotherapy, Sacred Heart University Hospital, Rome, 00168, Italy
| | - Andrea Damiani
- Department of Radiotherapy, Sacred Heart University Hospital, Rome, 00168, Italy
| | - Vincenzo Valentini
- Department of Radiotherapy, Sacred Heart University Hospital, Rome, 00168, Italy
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6062, NA, the Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6062, NA, the Netherlands
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14
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Alitto AR, Gatta R, Vanneste B, Vallati M, Meldolesi E, Damiani A, Lanzotti V, Mattiucci GC, Frascino V, Masciocchi C, Catucci F, Dekker A, Lambin P, Valentini V, Mantini G. PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncol 2017; 13:2171-2181. [PMID: 28758431 DOI: 10.2217/fon-2017-0142] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
AIM Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. MATERIALS & METHODS The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org . RESULTS A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. CONCLUSION The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.
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Affiliation(s)
- A R Alitto
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - R Gatta
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - Bgl Vanneste
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - M Vallati
- School of Computing & Engineering, University of Huddersfield, Huddersfield, UK
| | - E Meldolesi
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - A Damiani
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - V Lanzotti
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - G C Mattiucci
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - V Frascino
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - C Masciocchi
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - F Catucci
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - A Dekker
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - P Lambin
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - V Valentini
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
| | - G Mantini
- Radiation Oncology Area, Gemelli-ART, Catholic University of the Sacred Heart, Rome, Italy
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15
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Deist TM, Jochems A, van Soest J, Nalbantov G, Oberije C, Walsh S, Eble M, Bulens P, Coucke P, Dries W, Dekker A, Lambin P. Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT. Clin Transl Radiat Oncol 2017; 4:24-31. [PMID: 29594204 PMCID: PMC5833935 DOI: 10.1016/j.ctro.2016.12.004] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 12/11/2016] [Accepted: 12/15/2016] [Indexed: 12/31/2022] Open
Abstract
Developed and implemented IT infrastructure in 5 radiation clinics across 3 countries. Proof-of-principle for ‘big data’ infrastructure and distributed learning studies. General framework to execute learning algorithms on distributed data.
Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future ‘big data’ infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade ⩾2. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.
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Affiliation(s)
- Timo M Deist
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Minderbroedersberg 4-6, Maastricht, The Netherlands
| | - A Jochems
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Minderbroedersberg 4-6, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Minderbroedersberg 4-6, Maastricht, The Netherlands
| | - Georgi Nalbantov
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands
| | - Seán Walsh
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands
| | - Michael Eble
- Klinik für Strahlentherapie (University Clinic Aachen), Pauwelsstraße 30, Aachen, Germany
| | - Paul Bulens
- Department of Radiation Oncology (Jessa Hospital), Stadsomvaart 11, Hasselt, The Netherlands
| | - Philippe Coucke
- Departement de Physique Medicale (CHU de Liège), Bâtiment B 35, Liège, Belgium
| | - Wim Dries
- Catharina Hospital Eindhoven, Michelangelolaan 2, Eindhoven, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Minderbroedersberg 4-6, Maastricht, The Netherlands
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16
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Lambin P, Zindler J, Vanneste BGL, De Voorde LV, Eekers D, Compter I, Panth KM, Peerlings J, Larue RTHM, Deist TM, Jochems A, Lustberg T, van Soest J, de Jong EEC, Even AJG, Reymen B, Rekers N, van Gisbergen M, Roelofs E, Carvalho S, Leijenaar RTH, Zegers CML, Jacobs M, van Timmeren J, Brouwers P, Lal JA, Dubois L, Yaromina A, Van Limbergen EJ, Berbee M, van Elmpt W, Oberije C, Ramaekers B, Dekker A, Boersma LJ, Hoebers F, Smits KM, Berlanga AJ, Walsh S. Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev 2017; 109:131-153. [PMID: 26774327 DOI: 10.1016/j.addr.2016.01.006] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 12/08/2015] [Accepted: 01/06/2016] [Indexed: 12/12/2022]
Abstract
A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
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Affiliation(s)
- Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Jaap Zindler
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ben G L Vanneste
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lien Van De Voorde
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kranthi Marella Panth
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jurgen Peerlings
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ruben T H M Larue
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Timo M Deist
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evelyn E C de Jong
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Aniek J G Even
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicolle Rekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marike van Gisbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sara Carvalho
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Catharina M L Zegers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maria Jacobs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Janita van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Patricia Brouwers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jonathan A Lal
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ludwig Dubois
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ala Yaromina
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evert Jan Van Limbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maaike Berbee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bram Ramaekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Liesbeth J Boersma
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kim M Smits
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Adriana J Berlanga
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sean Walsh
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Yahya N, Ebert MA, Bulsara M, Kennedy A, Joseph DJ, Denham JW. Independent external validation of predictive models for urinary dysfunction following external beam radiotherapy of the prostate: Issues in model development and reporting. Radiother Oncol 2016; 120:339-45. [DOI: 10.1016/j.radonc.2016.05.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 05/11/2016] [Accepted: 05/15/2016] [Indexed: 12/20/2022]
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18
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Possible contribution of IMRT in postoperative radiochemotherapy for rectal cancer: analysis on 1798 patients by prediction model. Oncotarget 2016; 7:46536-46544. [PMID: 27340785 PMCID: PMC5216815 DOI: 10.18632/oncotarget.10228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 06/01/2016] [Indexed: 01/04/2023] Open
Abstract
The evidence for adjuvant therapy in locally advanced rectal cancer after TME surgery is sparse. The aim of this study was to identify predicting factors of overall survival (OS) in these patients and combine them into a nomogram for individualized treatment. 1798 patients with pathologically staged II/III rectal adenocarcinoma treated by radical TME surgery from a single center's database were reviewed. The nomogram was derived by Cox proportional hazards regression. Its performance was assessed by concordance index and calibration curve in internal validation with bootstrapping. Pooled Cox model analysis identified age, sex, grade of histology, pathological T and N stage, residual tumor, concurrent radiochemotherapy (RTCT), adjuvant chemotherapy cycles (CT), radiotherapy (RT) unexpected interruption days and intensity-modulated radiation therapy (IMRT) as significant covariates for 5-year OS (P<0.05). Postoperative RTCT, CT and IMRT all improved OS. The proposed model can predict 5-year OS with a C-index of 0.7105. IMRT significantly benefited OS in multivariate analysis (p=0.0441).In conclusion, our nomogram can predict 5-year OS after TME surgery for locally advanced rectal cancer with simple and effective advantage. This model may provide not only baseline OS estimate but also a tool for candidates selecting of adjuvant treatment in prospective studies.
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Nyholm T, Olsson C, Agrup M, Björk P, Björk-Eriksson T, Gagliardi G, Grinaker H, Gunnlaugsson A, Gustafsson A, Gustafsson M, Johansson B, Johnsson S, Karlsson M, Kristensen I, Nilsson P, Nyström L, Onjukka E, Reizenstein J, Skönevik J, Söderström K, Valdman A, Zackrisson B, Montelius A. A national approach for automated collection of standardized and population-based radiation therapy data in Sweden. Radiother Oncol 2016; 119:344-50. [DOI: 10.1016/j.radonc.2016.04.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 03/30/2016] [Accepted: 04/02/2016] [Indexed: 10/21/2022]
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20
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Cheng Q, Roelofs E, Ramaekers BLT, Eekers D, van Soest J, Lustberg T, Hendriks T, Hoebers F, van der Laan HP, Korevaar EW, Dekker A, Langendijk JA, Lambin P. Development and evaluation of an online three-level proton vs photon decision support prototype for head and neck cancer - Comparison of dose, toxicity and cost-effectiveness. Radiother Oncol 2016; 118:281-5. [PMID: 26924342 DOI: 10.1016/j.radonc.2015.12.029] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 12/01/2015] [Accepted: 12/05/2015] [Indexed: 12/25/2022]
Abstract
To quantitatively assess the effectiveness of proton therapy for individual patients, we developed a prototype for an online platform for proton decision support (PRODECIS) comparing photon and proton treatments on dose metric, toxicity and cost-effectiveness levels. An evaluation was performed with 23 head and neck cancer datasets.
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Affiliation(s)
- Qing Cheng
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Bram L T Ramaekers
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Tim Hendriks
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Hans Paul van der Laan
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Erik W Korevaar
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands.
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21
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Meldolesi E, van Soest J, Damiani A, Dekker A, Alitto AR, Campitelli M, Dinapoli N, Gatta R, Gambacorta MA, Lanzotti V, Lambin P, Valentini V. Standardized data collection to build prediction models in oncology: a prototype for rectal cancer. Future Oncol 2015; 12:119-36. [PMID: 26674745 DOI: 10.2217/fon.15.295] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.
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Affiliation(s)
- Elisa Meldolesi
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Andrea Damiani
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | - Nicola Dinapoli
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Roberto Gatta
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | | | - Vito Lanzotti
- Radiotherapy Department, Sacred Heart University, Rome, Italy
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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22
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Lambin P, Zindler J, Vanneste B, van de Voorde L, Jacobs M, Eekers D, Peerlings J, Reymen B, Larue RTHM, Deist TM, de Jong EEC, Even AJG, Berlanga AJ, Roelofs E, Cheng Q, Carvalho S, Leijenaar RTH, Zegers CML, van Limbergen E, Berbee M, van Elmpt W, Oberije C, Houben R, Dekker A, Boersma L, Verhaegen F, Bosmans G, Hoebers F, Smits K, Walsh S. Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine. Acta Oncol 2015; 54:1289-300. [PMID: 26395528 DOI: 10.3109/0284186x.2015.1062136] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided. AIM The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm. DISCUSSION Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.
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Affiliation(s)
- Philippe Lambin
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Jaap Zindler
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Ben Vanneste
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Lien van de Voorde
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Maria Jacobs
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Daniëlle Eekers
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Jurgen Peerlings
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Bart Reymen
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Ruben T H M Larue
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Timo M Deist
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Evelyn E C de Jong
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Aniek J G Even
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Adriana J Berlanga
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Erik Roelofs
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Qing Cheng
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Sara Carvalho
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Ralph T H Leijenaar
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Catharina M L Zegers
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Evert van Limbergen
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Maaike Berbee
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Wouter van Elmpt
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Cary Oberije
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Ruud Houben
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Andre Dekker
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Liesbeth Boersma
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Frank Verhaegen
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Geert Bosmans
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Frank Hoebers
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Kim Smits
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
| | - Sean Walsh
- a Department of Radiation Oncology (MAASTRO) , GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre , Maastricht , The Netherlands
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23
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Ree AH, Redalen KR. Personalized radiotherapy: concepts, biomarkers and trial design. Br J Radiol 2015; 88:20150009. [PMID: 25989697 DOI: 10.1259/bjr.20150009] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
In the past decade, and pointing onwards to the immediate future, clinical radiotherapy has undergone considerable developments, essentially including technological advances to sculpt radiation delivery, the demonstration of the benefit of adding concomitant cytotoxic agents to radiotherapy for a range of tumour types and, intriguingly, the increasing integration of targeted therapeutics for biological optimization of radiation effects. Recent molecular and imaging insights into radiobiology will provide a unique opportunity for rational patient treatment, enabling the parallel design of next-generation trials that formally examine the therapeutic outcome of adding targeted drugs to radiation, together with the critically important assessment of radiation volume and dose-limiting treatment toxicities. In considering the use of systemic agents with presumed radiosensitizing activity, this may also include the identification of molecular, metabolic and imaging markers of treatment response and tolerability, and will need particular attention on patient eligibility. In addition to providing an overview of clinical biomarker studies relevant for personalized radiotherapy, this communication will highlight principles in addressing clinical evaluation of combined-modality-targeted therapeutics and radiation. The increasing number of translational studies that bridge large-scale omics sciences with quality-assured phenomics end points-given the imperative development of open-source data repositories to allow investigators the access to the complex data sets-will enable radiation oncology to continue to position itself with the highest level of evidence within existing clinical practice.
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Affiliation(s)
- A H Ree
- 1 Department of Oncology, Akershus University Hospital, Lørenskog, Norway.,2 Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - K R Redalen
- 1 Department of Oncology, Akershus University Hospital, Lørenskog, Norway
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24
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Information science ontologies and metaphysics: In regard to an umbrella protocol for standardized data collection in rectal cancer by Meldolesi et al. Radiother Oncol 2015; 114:131. [DOI: 10.1016/j.radonc.2014.08.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 08/31/2014] [Indexed: 11/19/2022]
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25
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Guren MG, Undseth C, Rekstad BL, Brændengen M, Dueland S, Spindler KLG, Glynne-Jones R, Tveit KM. Reirradiation of locally recurrent rectal cancer: A systematic review. Radiother Oncol 2014; 113:151-7. [DOI: 10.1016/j.radonc.2014.11.021] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 10/10/2014] [Accepted: 11/15/2014] [Indexed: 10/24/2022]
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26
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Van De Voorde L, Larue RT, Pijls M, Buijsen J, Troost EG, Berbée M, Sosef M, van Elmpt W, Schraepen MC, Vanneste B, Oellers M, Lambin P. A qualitative synthesis of the evidence behind elective lymph node irradiation in oesophageal cancer. Radiother Oncol 2014; 113:166-74. [DOI: 10.1016/j.radonc.2014.11.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Revised: 10/10/2014] [Accepted: 11/09/2014] [Indexed: 12/21/2022]
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Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets. Radiother Oncol 2014; 113:303-9. [PMID: 25458128 PMCID: PMC4648243 DOI: 10.1016/j.radonc.2014.10.001] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Revised: 10/01/2014] [Accepted: 10/02/2014] [Indexed: 12/25/2022]
Abstract
Disconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within “Big Data”. Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology.
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28
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Meldolesi E, van Soest J, Alitto AR, Autorino R, Dinapoli N, Dekker A, Gambacorta MA, Gatta R, Tagliaferri L, Damiani A, Valentini V. VATE: VAlidation of high TEchnology based on large database analysis by learning machine. COLORECTAL CANCER 2014. [DOI: 10.2217/crc.14.34] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
SUMMARY The interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine new technologies with outcomes related to rectal cancer in terms of tumor control and normal tissue sparing. Using automated computer bots and the knowledge for screening data it is possible to identify the factors that can mostly influence those outcomes. Population-based observational studies resulting from the linkage of different datasets will be conducted in order to develop predictive models that allow physicians to share decision with patients into a wider concept of tailored treatment.
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Affiliation(s)
- Elisa Meldolesi
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO) GROW School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Anna Rita Alitto
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
| | - Rosa Autorino
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
| | - Nicola Dinapoli
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO) GROW School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - Roberto Gatta
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
| | - Luca Tagliaferri
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
| | - Andrea Damiani
- Department of Radiation Oncology, Sacred Heart University, Rome, Italy
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