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

Abstract

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.

Patterns of Care for Breast Radiotherapy in Italy: Breast IRRadiATA (Italian Repository of Radiotherapy dATA) Feasibility Study

“…It also aims at a universal enhancement of quality of care based on the standardization of procedures and reporting across countries. Some attempts have already been made in this direction, as reported in the literature, such as data collection tools for oncology [ 28 ] and radiotherapy [ 29 ]. All of them explore the possibility of storing data in Data Warehouses, large repositories, collected by an institution or a structure, managed by a Database Management System (DMS), controlling the organization, storage and retrieval of data for many different and complex needs.…”

“…All of them explore the possibility of storing data in Data Warehouses, large repositories, collected by an institution or a structure, managed by a Database Management System (DMS), controlling the organization, storage and retrieval of data for many different and complex needs. Marazzi and colleagues [ 29 ] proposed a seven-step Breast LArge DatabasE (BLADE) project, including data collection, analysis and evaluation on a data-sharing platform. It focused on developing a large database of encrypted data available from clinical records and approved by a multidisciplinary team with the aim of standardizing a data collection system.…”

Innovations in the Integrated Management of Breast Cancer

“…Research itself is a viable candidate for the coming high-tech revolution: today, protocol development can be promoted, patient enrollment can be enhanced by a patient-trial matching made possible by the growing diffusion of electronic health records, and patient parameters and adherence to trials can be monitored in real-time by a variety of wearable devices. This Issue witnesses the transformation, thanks to the contribution of authors active in the field of real-world data: Cesario et al describe the development of a digital research assistant that manages patient enrollment in trials with the employment of an artificial intelligence algorithm [17], while Marazzi et al exploit text mining to successfully extract data from heterogeneous sources and to generate clinical evidence [18,19].…”