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.
Highlights
Breast cancer, one of the main causes of women’s mortality, is characterized by highly complex presentation patterns [1]
Can either be automatically assigned or manually set, and refers to the ordering of the various questions inside the case report form (CRF), with SECTION_NAME and SECTION_LABEL working as visual dividers when the questions are displayed in the interface, with the former being the name to be used in the UI code, and the latter being the name to be displayed
In the 2016 Assisi Think Tank Meeting (ATTM) [13], attention focused on developing such a system from the potentially large database that was available from clinical records, in radiation oncology centers, but in many other specialty units that are dedicated to the diagnosis and treatment of breast cancer
Summary
One of the main causes of women’s mortality, is characterized by highly complex presentation patterns [1]. Hypothesis-based tailored treatments that are adapted to each individual patient’s specific features are being explored in an approach termed personalized medicine. Semantic approaches include patient stratification and treatment tailoring. In the former, individual patients are grouped into subpopulations according to the probability that a specific drug or treatment regimen will be of benefit, whereas in the latter, the individual patient’s status is used as the rationale for treatment choice [6,7]. Principles, to standardize inter-center data collection and promote personalized treatments for breast cancer. English-language ontology constituted a breast cancer library with seven knowledge domains: baseline, primary systemic therapy, surgery, adjuvant systemic therapies, radiation therapy, followup, and toxicity.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
