In the United States, October is celebrated as Breast Cancer Awareness Month. Breast cancer usually encompasses a broad spectrum of more than 100 diseases with distinct clinical and histopathological features . Currently, breast cancer is one of the most common cancers in women in several countries and the second most deadly after lung cancer. In the United States, the average risk of a woman developing breast cancer at some point in her life is about 13% [1, 2]. 15% of diagnosed women die as a result . According to the results of a study produced by the International Agency for Research on Cancer (IARC) in 185 countries, there is a frequency of 2.3 million new cases and mortality of about 6.9%. Although great strides have been made to increase awareness of breast cancer, complexities arise in who is the most affected by prognosis.
Regarding the risk factors associated with breast cancer, there is a shred of numerous evidence that demonstrated the relevance of reproductive and non-reproductive factors all o them related to socioeconomic status and healthcare access, such as younger age at menarche (first menses or period), older age at menopause, lower number of children, as well as less exposure to breastfeeding. Respecting the non-reproducible factors, it is observed that overweight postmenopausal women, increased alcohol consumption, low level of physical activity, smoking, and unbalanced diet contributes to approximately 4% of the increase in diagnostic cancer cases in 2020 . Women based in less developed nations are also at risk: while less than 1 in 5 women are under 50 years old at the time of their diagnosis, while in less developed countries, it is noted that more than half of breast cancer occurs in women under 50 years of age . These confounding factors are especially deadly for Black women. This is in part because on average, Black women have denser breast tissue than White women in addition to the factors previously mentioned . 1 in 5 Black women with breast cancer receiving triple-negative breast cancer screenings, and an early age of first diagnosis, breast cancer poses a higher threat to Black women . Men are also susceptible to breast cancer, aleit at lower rates. Male breast cancer accounts for less than one percent of all breast cancer diagnoses, or about 1 in 833 men. Of men that develop breast cancer, they are more likely to have more advanced cancers than women .
One of the main strategies to prevent breast cancer is early diagnosis through mammograms. Mammography basically consists of using X-rays specially directed at breast tissues allowing the identification of masses and other anatomical abnormalities . Early diagnosis of this type of cancer results in better clinical management and, consequently, in increased survival and quality of life for these women . In this context, recent studies have shown that artificial intelligence and machine learning have promoted significant advances in the diagnosis and prognosis of breast cancer, studies such as those carried out by Osareh et al. [5,6] show that the use of intelligence can help to understand signals and patterns in mammograms while machine learning can support the development of models which consider histopathological and laboratory patterns, helping, in turn, the AI models provided a diagnosis and prognosis considering the clinical context, ruling out possible confounding factors [5,6].
Despite notable and expressive advances, there are still considerable gaps regarding the wide use of this combination of AI with machine learning for the diagnosis and prognosis of breast cancer, such as the heterogeneous characteristics of this cancer, which requires a broad integration of the diverse of the genetic, liver, medical, clinical and laboratory imaging data from several centers and with representative populations. In addition to another fundamentally relevant challenge, the use of these tools is related to patient’s access to these resources .
 Azamjah N, Soltan-Zadeh Y, Zayeri F. Global Trend of Breast Cancer Mortality Rate: A 25-Year Study. Asian Pac J Cancer Prev. 2019 Jul 1;20(7):2015-2020. Doi: 0.31557/APJCP.2019.20.7.2015.
 A. Osareh, B. Shadgar, and R. Markham, “A computational intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 535–545, 2009.
 A.Osareh, B.Shadgar,“Microarray Data Analysis For Cancer classification,” in 2010 5th international symposium on health informatics and bioinformatics, pp. 125–132, IEEE, 2010
 Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, Zaguia A, Koundal S, Belay A. Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures. Biomed Res Int. 2022 Apr 18;2022:9605439. doi: 10.1155/2022/9605439.