Abstract
AbstractRapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnostic imaging systems have been explored. Microscopy with ultraviolet surface excitation (MUSE) is an inexpensive, maintenance-free, and rapid imaging technique that yields images like thin-sectioned samples without sectioning. However, pathologists find it nearly impossible to assign diagnostic labels to MUSE images of unfixed specimens; thus, AI for intraoperative diagnosis cannot be trained in a supervised learning manner. In this study, we propose a deep-learning pipeline model for lymph node metastasis detection, in which CycleGAN translate MUSE images of unfixed lymph nodes to formalin-fixed paraffin-embedded (FFPE) sample, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE sample images. Our pipeline yielded an average accuracy of 84.6% when using each of the three deep convolutional neural networks, which is a 18.3% increase over the classification-only model without CycleGAN. The modality translation to FFPE sample images using CycleGAN can be applied to various intraoperative diagnostic imaging systems and eliminate the difficulty for pathologists in labeling new modality images in clinical sites. We anticipate our pipeline to be a starting point for accurate rapid intraoperative diagnostic systems for new imaging modalities, leading to healthcare quality improvement.
Publisher
Springer Science and Business Media LLC
Reference31 articles.
1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).
2. Novis, D. A., Zarbo, R. J. & Saladino, A. J. Interinstitutional comparison of surgical biopsy diagnosis turnaround time: A College of American Pathologists Q-Probes study of 5384 surgical biopsies in 157 small hospitals. Arch. Pathol. Lab. Med. 122, 951–956 (1998).
3. Tamaki, Y. et al. Molecular detection of lymph node metastases in breast cancer patients: Results of a multicenter trial using the one-step nucleic acid amplification assay. Clin. Cancer Res. 15, 2879–2884 (2009).
4. Koizumi, N. et al. Highly sensitive fluorescence detection of metastatic lymph nodes of gastric cancer with photo-oxidation of protoporphyrin IX. Eur. J. Surg. Oncol. 42, 1236–1246 (2016).
5. Matsumoto, T. et al. 5-ALA-assistant automated detection of lymph node metastasis in gastric cancer patients. Gastric Cancer 23, 725–733 (2020).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献