Breast cancer is one of the most common cancer in women worldwide and the second leading cause of cancer death in females. Despite this statistic, from the 1980s to 2020, the number of women who have died from breast cancer has dropped by 40%. Many countries have been successfully reduced breast cancer mortality by 2-4% annually. But let us say that a steady reduction of 2.5% mortality annually continues till 2040; we would be able to avoid 2.5 million deaths due to breast cancer. And we owe this steady decline in breast cancer mortality to the introduction of advanced technology.
Technology has made our lives easier. But in the medical field, it has significantly contributed to the prevention, diagnosis, and treatment of a disease. Consequently, tech has increased the survival rate of people worldwide. This fact is particularly applicable to cancer. The use of technology in screening tools and various treatment modalities have increased curability from cancer; notably for breast cancer.
Of course, the increase in awareness and the importance of self-examination have resulted in the earlier detection of breast cancer. However, it is the implementation of regular screening that lead to earlier diagnosis and hence prompt treatment.
THE STANDARD TECH USED IN SCREENING AND DIAGNOSING BREAST CANCER
Ultrasound
An ultrasound uses high-frequency sound waves to produce a computer image of the internal structures of a breast. It is commonly the first step in the assessment of breast tissue changes. For women under 30, an ultrasound will be sufficient to determine whether these changes are innocuous or not. For women over 30, other imaging techniques may accompany the ultrasound. Since it does not use radiation, an ultrasound is also beneficial for women who cannot be exposed to radiation (such as during pregnancy). Moreover, the ultrasound also assists during a biopsy of a suspicious breast lump.
Mammogram
A mammogram is the only screening tool known to reduce deaths due to breast cancer through early detection. For this reason, it is considered the gold standard imaging technique for breast cancer diagnosis. After the age of 40, every woman undergoes a mammogram as a screening exam for breast cancer. The schedule for it varies according to the risk factors of every woman. A mammogram uses X-ray radiation to produce a 2D image of the breast tissue, thus detecting any minor changes. It is more sensitive than ultrasound and will be able to pick up the earliest breast cancer changes. Based on the characteristics of a lump, a mammogram (just like any other imaging modality) can classify it as benign or malignant. However, for a definitive diagnosis, a biopsy is necessary; to examine the abnormal breast tissue at a microscopic level.
Magnetic Resonance Imaging (MRI)
MRI uses radio waves and strong magnets to produce an image of the internal structure of a breast. It is often used as a screening tool for women at high risk of developing breast cancer (for instance, strong family history). In women with breast cancer, an MRI will help in determining the extent of breast cancer. However, not every woman will undergo an MRI as it is reserved for specific cases.
Digital breast tomosynthesis (DBT)
DBT is an advanced form of mammography. It uses low doses of radiation and a series of two-dimensional images to produce a 3D image of the breast. Consequently, it allows the doctor to visualize the breast tissue in greater detail, specifically dense breast tissue.
Although the mammogram can pick up minor changes in breast tissue, it cannot do so in dense breast tissue. On the other hand, an ultrasound is favorable for detecting changes in dense breast tissue, but it may miss the small details. Hence, a DBT may become the new gold standard imaging technique for breast cancer screening.
Additional advantages of DBT are:
- Detects more invasive cancers
- Clearer images reduce the rate of false-positive readings (a reading that identifies normal tissue as abnormal)
- Ensures that fewer women need to come back for another imaging test
- May be able to reduce long-term anxiety while awaiting test results
ARTIFICIAL INTELLIGENCE IN BREAST CANCER MANAGEMENT
Despite the prevalence of imaging techniques for screening and diagnosing breast cancer, inaccuracies may arise while interpreting these images. Human error is unavoidable. And when a clinician inadvertently misinterprets a diagnostic image, the rate of false-positive and false-negative results increases. Fortunately, we can rely on artificial intelligence (AI) systems to reduce such errors.
Research demonstrates that applying an AI system in a clinical setting reduced the rate of false-positive and false-negative readings in breast cancer diagnosis. Moreover, this system outperformed all human readers and reduced the workload of a second reader.
However, the role of an AI system is beyond that of computer-aided detection of breast cancer. In addition to enhancing a clinicians diagnostic expertise, AI systems can:
- Assess prognosis
- Evaluate response to therapy
- Risk assessment
- An accurate distinction of tumor size
- Aids in genetic analysis of breast cancer such as characteristics of the cancer phenotypes and clinical implications
- It integrates multiple data streams into multidisciplinary applications leading to a personalized medical approach for every individual.
All these attributes of an AI system are valuable in breast cancer screening, diagnosis, and treatment.
THE BOTTOM LINE
Other newer screening techniques are also being experimented such as contrast-enhanced mammography or optical imaging tests. But these experimental imaging tests have a long ways to go before being approved for regular screening tests. In the meantime, the above standard screening and diagnostic tests are efficient and expose one to less radiation.
Treatment for breast cancer has also advanced in the previous years; it has improved curability, thus reducing mortality. However, breast cancer therapy has its side effects and can reduce the quality of life. Research shows that globally women have lost more years to disability due to breast cancer than any other type of cancer. For this reason, there is an emphasis on screening tests for breast cancer. After all, prevention is better than cure.
SPREAD THE WORD
During Breast Cancer Awareness Month, it is critical to just raise awareness. If breast cancer is detected and treated early, the majority of people can survive it.
Discuss risk factors and lifestyle modifications with others. Encourage your loved ones to have frequent physical examinations, even during COVID-19 lockdowns! Time is precious and cancer doesn’t wait.
Reach out to a friend or family member who is dealing with breast cancer and provide your support. Participate in local activities and races to raise money and build awareness.
One of the most frequent malignancies in women is breast cancer. Take efforts to keep healthy in October and every month.
Everything you do is significant.
REFERENCES
- Institute of Medicine (US) and National Research Council (US) Committee on the Early Detection of Breast Cancer; Patlak M, Nass SJ, Henderson IC, et al., editors. Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer: A Non-Technical Summary. Washington (DC): National Academies Press (US); 2001. BREAST CANCER DETECTION TECHNOLOGIES IN DEVELOPMENT. Available from: https://www.ncbi.nlm.nih.gov/books/NBK223396/
- Breast Cancer Screening with Digital Breast Tomosynthesis: The Initial Benefits Continue Over Time, Patrice Taourel, Radiology 2020 295:3, 540-541
- McKinney, S.M., Sieniek, M., Godbole, V. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020). https://doi.org/10.1038/s41586-019-1799-6
- Hickman, S.E., Baxter, G.C. & Gilbert, F.J. Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. Br J Cancer 125, 15–22 (2021). https://doi.org/10.1038/s41416-021-01333-w
- Sheth, D. and Giger, M.L. (2020), Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging, 51: 1310-1324. https://doi.org/10.1002/jmri.26878