World Cancer Day: Real-World Findings on Recurrence in Breast Cancer

World Cancer Day Breast Cancer

World Cancer Day is a global initiative aimed to raise awareness against cancer and its risk factors. It is observed every year on 4th February. The year 2020 marks the midway point of the 3-year campaign ‘I am and I will’ help to fight cancer. It encourages the power of collective and individual actions to reduce the impact of cancer.


There is a need to work together to reduce premature deaths from cancer and improve cancer control by overcoming barriers to early detection, screening, diagnosis, and regular follow up. Early diagnosis can significantly reduce the impact of cancer and the cost of treatment. Screenings for breast cancer need to be encouraged in women starting from the early 20s as signs and symptoms of breast cancer can vary from person to person and may not express until it’s too late. Regular follow up can help in the monitoring of early symptoms of recurrence and their management.

Recurrent Breast Cancer

Recurrent breast cancer is defined as return of the cancer after successful treatment. It represents a major clinical manifestation and is the main cause of breast cancer-related deaths. Recurrence is based on many factors, including the size and type of cancer, stage of diagnosis and how it was treated. The risk of finding new cancers, such as ovarian cancer, may also be high in patients with greater chances of cancer recurrence.


Similarly, breast cancer can recur at local, regional and distant locations. In local recurrence, cancer returns to the same location where it was initially present. In regional recurrence, cancer is detected near the original location. In distant recurrence, breast cancer spreads to other parts of the body and is also called metastatic breast cancer.

Overall, more than 40% of the cases relapse across stages of breast cancer

  • Almost 40% of women diagnosed with early-stage breast cancer relapsed. Relapse rates were higher in the first 5 years following initial treatment. The highest recurrence rates were in the first and second years after initial diagnosis and treatment.
  • Distant recurrence was most prevalent (in 74% of the patients), followed by local (18%) and contralateral/opposite breast (4%) recurrence. Both distance and local recurrence were present in 4% of the patients.
  • Sites of recurrence were bone (26%), lung (21%), liver (20%), breast (20%), and brain (13%).
  • Larger and lymph-node positive tumors were associated with greater chances of early recurrence.
  • Patients with hormone receptor-positive breast cancer remained at risk of recurrence throughout their entire life.

cancer ratio

Way Forward: A New Model for Understanding Tumor Recurrence

An important challenge for doctors and the healthcare community is to accurately predict breast cancer recurrence. Currently, there are no acceptable models that can accurately predict the causes and risk factors of tumor recurrence. This highlights an urgent need to unravel the complex relationships between molecular, genetic and cellular components leading to recurrence.

Recurrence of breast cancer cannot be understood entirely through clinical observations and lab investigations alone. By digitalizing clinical and medical records in electronic formats, doctors can learn from the years of clinical practice and experience of other doctors and use it as a second opinion, in addition to treating and increasing survival times of the patients.

This enormous and complex volumes of data generated through medical activities can then be analyzed with the use of new technologies like AI and machine learning. This can be done by utilizing the latest data mining technologies to provide valuable information to doctors and help them make informed, real-time, and patient-centric clinical decisions and follow-up care.

The wealth of literature accumulated from medical records can also be used in research to understand patterns of cancer recurrence and for figuring out effective treatments and best practices. The more detailed and diverse the data is, the more accurate will be the prediction modeling.

(Disclaimer: The writer is Akansh Khurana, Co-Founder, and CEO, THB.)

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