13 Dec Deep Learning Algorithms Can Detect Spread of Breast Cancer To Lymph Nodes As Well or Better Than Pathologists
MedicalResearch.com: What is the background for this study?
Response: Artificial intelligence (AI) will play a crucial role in health care. Advances in a family of AI popularly known as deep learning have ignited a new wave of algorithms and tools that read medical images for diagnosis. Analysis of digital pathology images is an important application of deep learning but requires evaluation for diagnostic performance.
Accurate breast cancer staging is an essential task performed by the pathologists worldwide to inform clinical management. Assessing the extent of cancer spread by histopathological analysis of sentinel lymph nodes (SLN) is an important part of breast cancer staging. Traditionally, pathologists endure time and labor-intensive processes to assess tissues by reviewing thousands to millions of cells under a microscope. Using computer algorithms to analyze digital pathology images could potentially improve the accuracy and efficiency of pathologists.
In our study, we evaluated the performance of deep learning algorithms at detecting metastases in lymph nodes of patients with breast cancer and compared it to pathologist’s diagnoses in a diagnostic setting.
MedicalResearch.com: What are the main findings?
Response: In the setting of a competition (CAncer MEtastases in LYmph nOdes challeNge (CAMELYON16)), some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; the top-performing deep learning algorithm performed comparable to an expert pathologist interpreting the slides in the absence of time constraints.
MedicalResearch.com: What should clinicians and patients take away from your report?
Response: Deep learning algorithms can detect the spread of cancer to lymph nodes in women with breast cancer as well as or better than pathologists.
Previous studies on diagnostic imaging tasks in which deep learning reached human-level performance mostly used reference standard based on the consensus of human experts reviewing the images. This study, in comparison, generated reference standard using additional lab procedures, yielding an independent reference against which human pathologists could also be compared.
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Response: Further evaluation in a complete clinical setting is required to determine the clinical utility of this approach.
MedicalResearch.com: Thank you for your contribution to the MedicalResearch.com community.
Jeffrey Alan Golden. Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast CancerHelping Artificial Intelligence Be Seen. JAMA. 2017;318(22):2184–2186. doi:10.1001/jama.2017.14580
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