Author Interviews, Breast Cancer, Cancer Research, JAMA, Technology / 13.12.2017
Deep Learning Algorithms Can Detect Spread of Breast Cancer To Lymph Nodes As Well or Better Than Pathologists
MedicalResearch.com Interview with:
Babak Ehteshami Bejnordi
Department of Radiology and Nuclear Medicine
Radboud University medical center, Nijmegen
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.
Babak Ehteshami Bejnordi
Department of Radiology and Nuclear Medicine
Radboud University medical center, Nijmegen
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.






















