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The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE

Background: During the 2016 Assisi Think Tank Meeting (ATTM) on breast cancer, the panel of experts proposed developing a validated system, based on rapid learning health care (RLHC) principles, to standardize inter-center data collection and promote personalized treatments for breast cancer. Materi...

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Autores principales: Marazzi, Fabio, Masiello, Valeria, Masciocchi, Carlotta, Merluzzi, Mara, Saldi, Simonetta, Belli, Paolo, Boldrini, Luca, Capocchiano, Nikola Dino, Di Leone, Alba, Magno, Stefano, Meldolesi, Elisa, Moschella, Francesca, Mulé, Antonino, Smaniotto, Daniela, Terribile, Daniela Andreina, Tagliaferri, Luca, Franceschini, Gianluca, Gambacorta, Maria Antonietta, Masetti, Riccardo, Valentini, Vincenzo, Poortmans, Philip M. P., Aristei, Cynthia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926376/
https://www.ncbi.nlm.nih.gov/pubmed/33669549
http://dx.doi.org/10.3390/jpm11020143
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author Marazzi, Fabio
Masiello, Valeria
Masciocchi, Carlotta
Merluzzi, Mara
Saldi, Simonetta
Belli, Paolo
Boldrini, Luca
Capocchiano, Nikola Dino
Di Leone, Alba
Magno, Stefano
Meldolesi, Elisa
Moschella, Francesca
Mulé, Antonino
Smaniotto, Daniela
Terribile, Daniela Andreina
Tagliaferri, Luca
Franceschini, Gianluca
Gambacorta, Maria Antonietta
Masetti, Riccardo
Valentini, Vincenzo
Poortmans, Philip M. P.
Aristei, Cynthia
author_facet Marazzi, Fabio
Masiello, Valeria
Masciocchi, Carlotta
Merluzzi, Mara
Saldi, Simonetta
Belli, Paolo
Boldrini, Luca
Capocchiano, Nikola Dino
Di Leone, Alba
Magno, Stefano
Meldolesi, Elisa
Moschella, Francesca
Mulé, Antonino
Smaniotto, Daniela
Terribile, Daniela Andreina
Tagliaferri, Luca
Franceschini, Gianluca
Gambacorta, Maria Antonietta
Masetti, Riccardo
Valentini, Vincenzo
Poortmans, Philip M. P.
Aristei, Cynthia
author_sort Marazzi, Fabio
collection PubMed
description 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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spelling pubmed-79263762021-03-04 The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE Marazzi, Fabio Masiello, Valeria Masciocchi, Carlotta Merluzzi, Mara Saldi, Simonetta Belli, Paolo Boldrini, Luca Capocchiano, Nikola Dino Di Leone, Alba Magno, Stefano Meldolesi, Elisa Moschella, Francesca Mulé, Antonino Smaniotto, Daniela Terribile, Daniela Andreina Tagliaferri, Luca Franceschini, Gianluca Gambacorta, Maria Antonietta Masetti, Riccardo Valentini, Vincenzo Poortmans, Philip M. P. Aristei, Cynthia J Pers Med Article 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. MDPI 2021-02-19 /pmc/articles/PMC7926376/ /pubmed/33669549 http://dx.doi.org/10.3390/jpm11020143 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marazzi, Fabio
Masiello, Valeria
Masciocchi, Carlotta
Merluzzi, Mara
Saldi, Simonetta
Belli, Paolo
Boldrini, Luca
Capocchiano, Nikola Dino
Di Leone, Alba
Magno, Stefano
Meldolesi, Elisa
Moschella, Francesca
Mulé, Antonino
Smaniotto, Daniela
Terribile, Daniela Andreina
Tagliaferri, Luca
Franceschini, Gianluca
Gambacorta, Maria Antonietta
Masetti, Riccardo
Valentini, Vincenzo
Poortmans, Philip M. P.
Aristei, Cynthia
The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
title The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
title_full The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
title_fullStr The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
title_full_unstemmed The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
title_short The Assisi Think Tank Meeting Breast Large Database for Standardized Data Collection in Breast Cancer—ATTM.BLADE
title_sort assisi think tank meeting breast large database for standardized data collection in breast cancer—attm.blade
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926376/
https://www.ncbi.nlm.nih.gov/pubmed/33669549
http://dx.doi.org/10.3390/jpm11020143
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