Edinburgh Cancer Informatics

Data and technology for cancer care

Breast Cancer

Pathological response as prognostic indicator for recurrence and survival in early triple negative breast cancer (eTNBC) – use case for multi-centre RWD to support patient access

Innovative new medicines used in conjunction with neoadjuvant chemotherapy (NACT) may lead to a higher pathological complete response (pCR), and therefore a better chance of survival for patients diagnosed with eTNBC cancer. The Edinburgh Cancer Informatics, in partnership with DATA-CAN, ran a retrospective, longitudinal cohort study using routinely collected data to better describe differences in […]

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Patient Characteristics, Treatment Patterns and Long-term Outcomes from a Real-World Population of Early Breast Cancer Patients at High Risk of Recurrence in Scotland

In an effort to better describe patients affected with node-positive HR+, HER2− early breast cancer, the Edinburgh Cancer Informatics conducted a retrospective study looking at the demographic and clinical characteristics of these patients in South-East Scotland, in a real-world setting. Long term outcomes and treatment profiles were also reported, along with healthcare utilisation.  Such audits are […]

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NHS PREDICT – R Package

Data Scientist Giovanni Tramonti has produced an implementation of the NHS Predict decision tool for batch processing. Available on the R CRAN. This is useful for producing a patient-level mortality risk estimate from breast cancer staging information. https://cran.r-project.org/web/packages/nhs.predict/index.html

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Treatment sequencing in metastatic breast cancer

Edinburgh Cancer Centre Oncology Specialty Registrar Ashley Horne @AshHorne1983 used data visualisation to demonstrate the power of Scottish prescribing data for complex treatment sequencing in metastatic breast cancer at the 2019 European Society of Medical Oncology annual conference.  Full poster:   ESMO_2019_Optimized

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