Presentation (CTI)

OBJECTIVES: Breast cancer varies in presentation and progression patterns that influence treatment decisions and patient outcomes. Understanding real-world disease characteristics is important for improving care. This study uses a UK breast cancer dataset to analyze disease characteristics and recurrence rates.
METHODS: We used the UK Flatiron Health Research Database, which integrates structured and unstructured data curated through technology-enabled abstraction. Key clinical variables, including histology, biomarker status, and recurrence status were extracted. Descriptive statistics were used to summarize patient characteristics.
RESULTS: A total of 2844 patients were included in the analysis, with a median age at diagnosis of 63 years (interquartile range 52-73). Most patients were female (99.4%) and of White ethnicity (92.3%), with smaller proportions of Asian (3.6%) and Black (2.0%) patients. At initial diagnosis, 47.5% of patients had Stage I disease, 31.6% had Stage II, 11.6% had Stage III, and 9.3% had Stage IV (metastatic at diagnosis). The most common histological subtype was invasive ductal carcinoma (71.8%), followed by invasive lobular carcinoma (13.1%). Among patients with recorded tumor grade, 54.8% had grade 2 tumors, while 28.1% had grade 3. 20% of patients had a recorded date of metastatic diagnosis. Tumor laterality was nearly evenly distributed between left (50.4%) and right (49.6%) sides, with 0.7% having unknown or undocumented laterality. While those with HR+ disease represented 80% of the total cohort, they represented fewer (71%) of those who ever developed metastatic disease.
CONCLUSIONS: These findings offer valuable insights into the real-world presentation of breast cancer in the UK and patient characteristics. The use of an EHR-derived dataset enables a more comprehensive understanding of disease trajectories beyond what is typically captured in clinical trials.