Symptoms, causes and prevention and how AI advances its detection

With an age-adjusted mortality rate of 12.7 per 100,000 women and a cancer incidence rate of 25.8 per 100,000 women, breast cancer is the most common cancer among Indian women. However, breast cancer can be effectively treated if caught early. The majority of the time, localised cancer (cancer that hasn’t spread beyond your breast) can be treated before it spreads. Treatment becomes more challenging as the cancer spreads. 


  • A new lump or mass is the most typical sign of breast cancer, yet most breast lumps are benign. A painless, hard mass with jagged edges is more likely to be cancer. Other indications of breast cancer include: 
  • New Breast Swelling, either Complete or Partial (even if no lump is felt)
  • Skin dimples (sometimes looking like an orange peel)
  • Pain in the breast, nipple, or newly inverted nipple (turning inward)
  • Skin that is red, dry, flaky, or thickened on the nipple or breast
  • Nipple erupting (other than breast milk)
  • Swollen lymph nodes in the area of the collarbone or under the arm may indicate the spread of breast cancer even before the primary tumour in the breast becomes noticeable.


  • The risk of breast cancer has been linked to hormonal, behavioural, and environmental factors, according to research. However, it is unclear why some people with risk factors never get cancer while others with risk factors do. It’s likely that a complicated relationship between your environment and genetic composition leads to breast cancer.
  • Breast cancer that is inherited – A family’s genetic mutations that have been passed down through the generations are responsible for 5 to 10% of breast cancer cases. The most well-known ones are the BRCA1 and BRCA2 genes, which both dramatically raise the risk of developing breast and ovarian cancer.
  • Increasing age
  • Personal and family history of Breast cancer
  • Radiation exposure as a child
  • Beginning of menstruation before age 12
  • Menopause at an older age
  • Having never been pregnant or giving birth to their first child after age 30
  • Postmenopausal hormone therapy
  • Indulgence to alcohol.


Screening and early detection of breast cancer helps in lowering breast cancer related mortality rates. Mammogram and ultrasonogram have assisted in reducing breast cancer-related deaths by providing early detection when cancer is still treatable. Supplemental screening with MRI in women with dense breast tissue increased the sensitivity of cancer.

Artificial Intelligence (AI) & Breast Cancer Detection:

AI is a general term that describes any human-like behaviour that a machine or system demonstrates. In the simplest type of AI, computers are taught to “imitate” human behaviour utilising a wealth of data from prior instances of the same activity.Imaging modalities such as mammogram, USG, MRI, histogical images have been exploited by researchers to automate the task of breast cancer detection. AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up. 

The artificial Intelligence system could assist doctors in reducing benign biopsy rate also without missing highly malignant tumors. Radiologists frequently missed low suspicion lesions in breast tissue that was heterogeneously and highly dense, but the artificial intelligence system was able to find them. The implementation of an artificial intelligence system may decrease the reading time required by beginning radiologists to evaluate possible lesions and increase inter-rater reliability and sensitivity. Doctors also use imaging tests to get important prognostic information about cancer, such as how fast it is growing, whether it has spread, and whether it is likely to come back after treatment. This information can help doctors choose the most appropriate treatment for their patients.

AI can play an important role for treatment planning of Breast Cancer. For e.g. it is humanly impossible for a doctor to remember all the research data or new drugs and newer targeted therapy or newer molecules to treat the patient. But, now there are computers and systems which remember all the studies and trials and when we feed the exact demographical data of the patient to the system for example age, sex and the size of the tumor, the AI can give us the algorithm by which we can treat the patient, like next generation sequencing. AI can help doctors plan the exact medical treatment of cancer.

The effectiveness of multidisciplinarity and how clinical skills collaborate with AI will decide how to create the road for applying AI in clinical care for the benefit of breast cancer patients will depend on how we adapt to a quickly changing future.



Views expressed above are the author’s own.


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