Florentino Saenz Rios1, Giri Movva1, Hari Movva2, Quan D Nguyen1. 1. Department of Radiology, University of Texas Medical Branch, Galveston, USA. 2. School of Medicine, University of Texas Rio Grande Valley, Edinburg, USA.
Abstract
INTRODUCTION: This study intends to show that the current widely used computer-aided detection (CAD) may be helpful, but it is not an adequate replacement for the human input required to interpret mammograms accurately. However, this is not to discredit CAD's ability but to further encourage the adoption of artificial intelligence-based algorithms into the toolset of radiologists. METHODS: This study will use Hologic (Marlborough, MA, USA) and General Electric (Boston, MA, USA) CAD read images provided by patients found to be Breast Imaging Reporting and Data System (BI-RADS) 6 from 2019 to 2020. In addition, patient information will be pulled from our institution's emergency medical record to confirm the findings seen in the pathologist report and the radiology read. RESULTS: Data from a total of 24 female breast cancer patients from January 31st 2019 to April 31st 2020, was gathered from our institution's emergency medical record with restrictions in patient numbers due to coronavirus disease 2019 (COVID-19). Within our patient population, CAD imaging was shown to be statistically significant in misidentifying breast cancer, while radiologist interpretation still proves to be the most effective tool. CONCLUSION: Despite a low sample size due to COVID-19, this study found that CAD did have significant difficulty in differentiating benign vs. malignant lesions. CAD should not be ignored, but it is not specific enough. Although CAD often marks cancer, it also marks several areas that are not cancer. CAD is currently best used as an additional tool for the radiologist.
INTRODUCTION: This study intends to show that the current widely used computer-aided detection (CAD) may be helpful, but it is not an adequate replacement for the human input required to interpret mammograms accurately. However, this is not to discredit CAD's ability but to further encourage the adoption of artificial intelligence-based algorithms into the toolset of radiologists. METHODS: This study will use Hologic (Marlborough, MA, USA) and General Electric (Boston, MA, USA) CAD read images provided by patients found to be Breast Imaging Reporting and Data System (BI-RADS) 6 from 2019 to 2020. In addition, patient information will be pulled from our institution's emergency medical record to confirm the findings seen in the pathologist report and the radiology read. RESULTS: Data from a total of 24 female breast cancerpatients from January 31st 2019 to April 31st 2020, was gathered from our institution's emergency medical record with restrictions in patient numbers due to coronavirus disease 2019 (COVID-19). Within our patient population, CAD imaging was shown to be statistically significant in misidentifying breast cancer, while radiologist interpretation still proves to be the most effective tool. CONCLUSION: Despite a low sample size due to COVID-19, this study found that CAD did have significant difficulty in differentiating benign vs. malignant lesions. CAD should not be ignored, but it is not specific enough. Although CAD often marks cancer, it also marks several areas that are not cancer. CAD is currently best used as an additional tool for the radiologist.
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