Abstract: Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes. Six pathologists reviewed 70 digitized slides from lymph node sections in 2 reader modes, unassisted and assisted, with a washout period between sessions. In the assisted mode, the deep learning algorithm was used to identify and outline regions with high likelihood of containing tumor. Algorithm-assisted pathologists demonstrated higher accuracy than either the algorithm or the pathologist alone. In particular, algorithm assistance significantly increased the sensitivity of detection for micrometastases (91% vs. 83%, P = 0.02). In addition, average review time per image was significantly shorter with assistance than without assistance for both micrometastases (61 vs. 116 s, P = 0.002) and negative images (111 vs. 137 s, P = 0.018). Lastly, pathologists were asked to provide a numeric score regarding the difficulty of each image classification. On the basis of this score, pathologists considered the image review of micrometastases to be significantly easier when interpreted with assistance (P = 0.0005). Utilizing a proof of concept assistant tool, this study demonstrates the potential of a deep learning algorithm to improve pathologist accuracy and efficiency in a digital pathology workflow. Key Words: artificial intelligence, machine learning, digital pathology, breast cancer, computer aided detection (Am J Surg Pathol 2018;42:1636-1646 T he regulatory approval and gradual implementation of whole-slide scanners has enabled the digitization of glass slides for remote consults and archival purposes. 1 Digitization alone, however, does not necessarily improve the consistency or efficiency of a pathologist's primary workflow. In fact, image review on a digital medium can be slightly slower than on glass, especially for pathologists with limited digital pathology experience. 2 However, digital pathology and image analysis tools have already demonstrated potential benefits, including the potential to reduce inter-reader variability in the evaluation of breast cancer HER2 status. 3, 4 Digitization also opens the door for assistive tools based on Artificial Intelligence (AI) to improve efficiency and consistency, decrease fatigue, and increase accuracy. 5 Among AI technologies, deep learning has demonstrated strong performance in many automated image-recognition applications. [6] [7] [8] Recently, several deep learningbased algorithms have been developed for the detection of breast cancer metastases in lymph nodes as well as for other applications in pathology. 9, 10
The highest performance was obtained in using GoogLeNet with 40 times magnification Steiner et al. REF conducted a study utilizing results from deep learning algorithms for the detection of breast cancer metastasis in lymph nodes.
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
The American Journal of Surgical Pathology
Medicine