Danny Platform® in a RWE Study on Triple-Negative Breast Cancer

Triple-negative breast cancer in Bulgaria: epidemiological data and treatment patterns based on real-world evidence and patient registries




Evaluation of epidemiological data and treatment patterns for patients with triple-negative breast cancer in Bulgaria; its compliance with international therapeutic guidelines and recommendations; with the use of analytics software for processing Big Data sets.


Through Sqilline’s analytics platform – Danny Platform® and the embedded algorithms for machine learning (ML) and natural language processing (NLP) was possible to perform the one-year (2019) real-life retrospective study of the cohort of patients with triple-negative breast cancer, patho-histologically proven.

Demographic characteristics, data on biomarkers, TNM stage, therapeutic regime, line, and changes in treatment were chronologically analyzed for all patients.


  • 6 880 patients with breast cancer (BC), from 8 leading oncology hospitals in Bulgaria
  • The average age for all women with BC was 60
  • 234 (3,4%) were diagnosed with TNBC with the majority assigned on chemotherapy
  • Treatment of TNBC patients is fully in line with recommendations from ESMO and NCCN
  • Only 7% of patients were on 3rd, 4th, or 5th line, which may be due to the fact they were not eligible for 1st or 2nd line of therapy (due to contradictions or lack of treatment effect) or due to mortality
  • A change in therapy was observed in 25 patients, mainly to neoadjuvant therapy, where only 5 patients were complicated by the addition of new or more agents, and for the others was alleviated


  • The study confirmed dynamic patient registers are of great importance in performing real-world studies of treatment patterns
  • There is a lack of nationally organized screening programs for older women
  • In the era of digital innovations and value-based healthcare substantial part of the medical information is still unstructured. The extraction and systematization of medical records are of great importance for the improvement of diagnosis, treatment, and survival predictions
  • Electronic population registers can contribute to the value allocation of resources and cost in healthcare


Link to full publication: https://doi.org/10.1080/13102818.2021.1903338

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