Abstract PO-003: Early detection of breast cancer during the Covid-19 pandemic using artificial intelligence neural networks

Abstract

Abstract Currently, as governments across the world attempt to halt the spread of COVID-19, many of their efforts are being hampered by shortages of crucial testing kits. Even in many developed countries, the health system has come to the point of standstill due to the increasing demand for intensive care units. One study reveals that 40,000 women die in a year from breast cancer, which is one woman every 13 minutes dying from this disease every day. Breast cancer remains the number one form of cancer that women are diagnosed with around the world. Any hospital in the world will have a multidisciplinary team of experts including radiologists, oncologists, surgeons, and other respiratory physicians and consultants. This multidisciplinary team uses a wide variety of data taken from CT scans, x-rays, PET scans, biopsies, clinical examination, imaging mammography, ultrasound (sonography), pathology biopsy, and fine-needle aspiration to identify whether a patient has cancer. Individuals with cancer, particularly those who are receiving systemic anticancer treatments, have been postulated to be at increased risk of mortality from COVID-19. Therefore, it is the need of an hour to implement an early detection of cancer that will prevent patients from COVID-19 and due treatment will be provided. Breast cancer that is detected early is far easier to cure, but at the same time it is difficult to detect. An accurate breast cancer staging is an essential task performed by pathologists (experts who examine the laboratory samples) to inform clinical management for the appropriate treatment. For instance, evaluation of the extent of cancer spread by histopathologic analysis or microscopic examination is an important part of breast cancer staging, and the process involves the decision-making of human behavior with time-consuming and costly sample examination and a period of more than one month to have breast cancer type, stage, etc., to decide the appropriate therapy to patients by the time it can affect the results of therapeutic treatments and survival of patients. The application of artificial intelligence (AI) machine learning technology with deep learning algorithms to whole-slide pathology images can potentially improve the diagnostic accuracy of breast cancer at a very early stage. I have developed a simple machine-learning model using the Python programming language and neural network, inspired by the structure of the human brain, with 12 attributes, out of which 10 real-valued features from each cell nucleus obtained from the Wisconsin diagnostic breast cancer dataset available in public domain for the education capacity that explains about the stage of breast cancer M (Malignant) and B (Benign). This model predicts the presence of breast cancer with an accuracy of 98.8% at a very early stage. The continuous development of technology in the medical field will save countless lives and the overall quality of human life will continue to improve over time. Citation Format: Subash Kumar. Early detection of breast cancer during the Covid-19 pandemic using artificial intelligence neural networks [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-003.