Data managers: A survey of the European Society of Breast Cancer Specialists in certified multi-disciplinary breast centers

Treatment and outcomes in breast cancer patients: A cross section study from the EUSOMA breast centre network

INTRODUCTION

The present study was designed to describe tumour features and treatments for patients with breast cancer. It also aimed at assessing the risk of distant metastases in relation to biological profiles, disease stages and treatment.

METHODS

Data were analysed from 81,882 patients in the EUSOMA database (disease stages at diagnosis 0-IV; median age 61 years; range 20-100 years). All patients were treated between January 2016 and December 2021 in 53 Breast Centres within the EUSOMA certification process in 13 European countries. Cases were classified as HR+ /HER2-, HR+ /HER2 + , HR-/HER2 + or HR-/HER2- and data were analysed accordingly.

RESULTS

Univariable and multivariable analyses for distant metastases were conducted on a subset of 38,119 cases with information on whether or not they had developed them. Potential determinants included sub-group type, Ki67 value, disease stage, adjuvant systemic therapies and post-operative radiation therapy. In multivariable analysis, the HR-/HER2 + and HR-/HER2- sub-groups were associated with a higher risk of distant metastases than HR+ /HER2-. Ki67 > 20 % and advanced stage disease also carried a high risk. Radiation therapy emerged as a protective factor against distant metastases.

CONCLUSIONS

Present results show a large patient database offers an information stream that can be applied to reduce uncertainties in clinical practice. Database parameters need to be updated dynamically for outcome monitoring. Molecular prognostic factors, gene-expression signatures, tumour-infiltrating lymphocytes and circulating tumoral DNA should be added.

Success Factors and Barriers in Combining Personalized Medicine and Patient Centered Care in Breast Cancer. Results from a Systematic Review and Proposal of Conceptual Framework

Breast Cancer (BC) is the leading cause of death due to cancer in women. Ensuring equitable, quality-assured and effective care has increased the complexity of BC management. This systematic review reports on the state-of-the art of available literature investigating the enactment of personalized treatment and patient-centered care models in BC clinical practice, building a framework for the delivery of personalized BC care within a Patient-Centered model. Databases were searched for articles (from the inception to December 2020) reporting on Patient-Centered or Personalized Medicine BC management models, assessing success factors or limits. Out of 1885 records, 25 studies were included in our analysis. The main success factors include clearly defined roles and responsibilities within a multi-professional collaboration, appropriate training programs and adequate communication strategies and adopting a universal genomic language to improve patients' involvement in the decision-making process. Among detected barriers, delays in the use of genetic testing were linked to the lack of public reimbursement schemes and of clear indications in timing and appropriateness. Overall, both care approaches are complementary and necessary to effectively improve BC patient management. Our framework attempts to bridge the gap in assigning a central role played by shared decision-making, still scarcely investigated in literature.

GENERATOR Breast DataMart-The Novel Breast Cancer Data Discovery System for Research and Monitoring: Preliminary Results and Future Perspectives

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into “Not organized, not ‘ontologized’ data”, “Organized, not ‘ontologized’ data”, and “Organized and ‘ontologized’ data”. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system.