Abstract A088: Utilizing Machine Learning Techniques to Investigate Mammograms for Breast Cancer Detection

Abstract

Purpose: The prevalence of artificial intelligence (AI) in breast cancer screening and diagnosis has substantially increased over the past 5 years. AI can be used to assist in the interpretation of mammograms and other breast imaging modalities, as well as predict patient outcomes. This study aims to quantify AI's diagnostic accuracy and its ability to detect breast cancer from a large database of mammograms. The implementation of AI in breast cancer screening holds immense potential to not only enhance the accuracy of mammogram interpretation but also improve the overall efficiency of radiologists and healthcare providers. Furthermore, we aim to contribute to the advancement of the rapidly growing field of machine learning in medicine, particularly in breast cancer care. Our hope is that by applying this technology, we can improve patient outcomes and increase accessibility to breast cancer screening and early detection. Methods: A deep neural network (DNN) model was built on Tensorflow utilizing Radiological Society of North America Screening Mammography Breast Cancer Detection dataset, consisting of normal mammograms and abnormal mammograms with tumors. The model's efficacy was assessed using Area under the curve (AUC), precision, and recall, with images randomly split into training (80%), validation (10%), and test sets (10%). Four consecutive training sessions each lasting 2 hours and 13 minutes using 8,877 images consisting of 4,621 breast cancer and 4,456 normal breast mammogram images was done. Results: The model attained an AUC of 0.926, Specificity of 95%, Sensitivity of 73% and Accuracy of 84%. Conclusion: This DNN model was created for diagnosing breast cancer from mammograms, with higher AUC than the current standard. This study highlights the potential of AI to revolutionize breast cancer screening, prevention, and diagnosis, which will ultimately improve patient outcomes and increase accessibility for patients. The use of AI in detecting breast cancer on mammograms can not only provide support to radiologists but also improve their efficacy and accuracy, reducing the burden of high volume images. Additionally, the use of AI in detection and screening can address the barriers that come with physician shortages, especially in underserved areas. Extending AI’s reach to these areas of limited access can improve early detection and screening which tends to be lacking in under-resourced areas, thus addressing the gap in health equity worldwide. While this study highlights encouraging data at the intersection of AI and medicine, we acknowledge that AI’s role in medicine is not to replace but rather enhance the expertise of radiologists. Lastly, we hope that our developed tool can alleviate the workload of radiologists and increase efficiency in high-pressure settings such as emergency departments and rural areas, ultimately leading to improved patient care.

Citation Format: Parsa Riazi Esfahani, Maya M Maalouf, Akshay J Reddy, Prashant Chawla. Utilizing Machine Learning Techniques to Investigate Mammograms for Breast Cancer Detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A088.