Mammogram: Breast Cancer Diagnostics

Cancers have had a significant burden on the global population in recent times. The emerging trend demands that societies and authorities design and implement deliberate measures to counter the problem. For instance, lung cancer has been the leading cause of death from cancer at 11.6% global prevalence rate, rivalling female breast cancer with a prevalence rate of 11.5% (Bray et al. 394). The increasing morbidity and mortality from cancer follow the backdrop of other non-communicable diseases such as coronary heart diseases and stroke. Global socio-economic developments mirror this worrying trend of increased prevalence of malignancies.

Reproductive cancers pose a significant threat to the quality of life of the female population. Female breast cancer, the second most common malignancy, is a significant public health challenge (Akram et al. 1). Moreover, breast cancer diagnosis most often occurs at late stages due to late clinical detection and insufficient knowledge on breast self-examination. Stakeholders have championed screening as the practical approach for the early detection and treatment of breast cancer. A breast cancer diagnosis at early stages prevents invasive medical or surgical interventions, contributing to the overall refinement of breast cancer outcomes. Mammography functions for both screening and diagnosis of breast cancer. However, the procedure has limitations, notwithstanding the benefits. Therefore, it is essential to describe the appropriateness of mammogram for general females.

Screening and diagnostic tests should possess an acceptable degree of sensitivity and specificity. Additionally, there must be a significant prevalence of a disease in the general population to necessitate testing individuals with no signs and symptoms of the disease. Mammogram involves radiologic procedures to detect breast abnormalities suggestive of breast malignancy (Akram et al. 1). However, concerns exist in the efficacy of the test to screen the general female population. Ohuchi et al. established that the sensitivity of mammograms to detect breast cancer was 71% for women aged 40-49, 85% for women aged 50-59, and 86% for women aged 60-69, denoting the proportion of those with breast cancer that mammography was able to detect (341). Additionally, the specificity of the mammogram was 87%, indicating the proportion of those without breast cancer that the test was able to identify (Ohuchi et al. 341). Therefore, the sensitivity and specificity represented the accuracy of mammogram in breast cancer screening.

The validity of the screening test is essential for prompt diagnosis and management of breast cancer. Consequently, the screening test’s accuracy can be described as the degree to which the categorization of the positive or negative results predicts the presence or absence of the disease. This method is represented as predictive value positive or the Baye’s theorem (Udayakumar et al. 342). The predictive value positive represents the percentage of individuals that the test identifies as positive, and standard diagnostic procedures confirm the results as positive.

P(D) represents the prevalence rate, P(+~D) is the sensitivity, P(Dc) is the false positive, while P(+Dc) is the false positive rate. An increase in the prevalence rate would lead to an increase in the predictive value positive. Therefore, prevalence rate, sensitivity, and specificity affect the predictive value positively.

Studies have established that the prevalence rate of breast cancer is low in general females below 40 years. Therefore, the predictive value positive of a breast mammogram will be very low, notwithstanding the test’s sensitivity and specificity. The findings will denote significantly low numbers of detected breast cancer cases among the population of under 40-year-old females. Consequently, the investment will not be cost-effective in combating the scourge of increasing cancer cases.

Works Cited

Akram, Muhammad et al. “Awareness and Current Knowledge of Breast Cancer.” Biological Research vol. 50, 2017, pp 33. Web.

Bray, Freddie, et al. “Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.” CA: A Cancer Journal for Clinicians Vol. 68.6, 2018, pp 394-424. Web.

Ohuchi, Noriaki, et al. “Sensitivity and Specificity of Mammography and Adjunctive Ultrasonography to Screen for Breast Cancer in the Japan Strategic Anti-Cancer Randomized Trial (J-START): A Randomised Controlled Trial.” The Lancet Vol. 387, 2016, pp 341-348. Web.

Udayakumar, E., S. Santhi, and P. Vetrivelan. “An Investigation of Bayes Algorithm and Neural Networks for Identifying the Breast Cancer.” Indian Journal of Medical and Paediatric Oncology: Official Journal of Indian Society of Medical & Paediatric Oncology Vol. 38.3, 2017, pp 340. Web.

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