Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach
Pham and colleagues set out to address high false positivity of lymph nodes metastasis analysis using deep learning. As characterizing lymph node metastases in breast and lung cancer is of great clinical importance for treatment selection and prognosis, finding a method with high sensitivity and specificity would represent a major advance. Here, the researchers demonstrate a two-step approach with HALO AI where the first deep learning algorithm excludes the lymph germinal centers that are the source of false positivity and the second algorithm detects tumor cells. The researchers demonstrate this method on lung cancer lymph tissue and find a sensitivity ~78% and specificity ~97% and conclude that a two-step approach can successfully be used to detect lung cancer metastases to the lymph nodes with high specificity. Future research may target development of an algorithm or algorithms with increased sensitivity that maintain high specificity.
Hoa Hoang Ngoc Pham, Mitsuru Futakuchi, Andrey Bychkov, Tomoi Furukawa, Kishio Kuroda, Junya Fukuoka
The American Journal of Pathology | First published December 2019| DOI https://doi.org/10.1016/j.ajpath.2019.08.014