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Original Article
- Deep learning for computer-assisted diagnosis of hereditary diffuse gastric cancer
- Sean A. Rasmussen, Thomas Arnason, Weei-Yuarn Huang
- J Pathol Transl Med. 2021;55(2):118-124. Published online January 22, 2021
- DOI: https://doi.org/10.4132/jptm.2020.12.22
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- 5 Web of Science
- 5 Crossref
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Abstract
PDF - Background
Patients with hereditary diffuse gastric cancer often undergo prophylactic gastrectomy to minimize cancer risk. Because intramucosal poorly cohesive carcinomas in this setting are typically not grossly visible, many pathologists assess the entire gastrectomy specimen microscopically. With 150 or more slides per case, this is a major time burden for pathologists. This study utilizes deep learning methods to analyze digitized slides and detect regions of carcinoma.
Methods
Prophylactic gastrectomy specimens from seven patients with germline CDH1 mutations were analyzed (five for training/validation and two for testing, with a total of 133 tumor foci). All hematoxylin and eosin slides containing cancer foci were digitally scanned, and patches of size 256×256 pixels were randomly extracted from regions of cancer as well as from regions of normal background tissue, resulting in 15,851 images for training/validation and 970 images for testing. A model with DenseNet-169 architecture was trained for 150 epochs, then evaluated on images from the test set. External validation was conducted on 814 images scanned at an outside institution.
Results
On individual patches, the trained model achieved a receiver operating characteristic (ROC) area under the curve (AUC) of 0.9986. This enabled it to maintain a sensitivity of 90% with a false-positive rate of less than 0.1%. On the external validation dataset, the model achieved a similar ROC AUC of 0.9984. On whole slide images, the network detected 100% of tumor foci and correctly eliminated an average of 99.9% of the non-cancer slide area from consideration.
Conclusions
Overall, our model shows encouraging progress towards computer-assisted diagnosis of hereditary diffuse gastric cancer. -
Citations
Citations to this article as recorded by
- Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review
Marianne Linley L. Sy-Janairo, Jose Isagani B. Janairo
Journal of Gastrointestinal Cancer.2024; 55(1): 47. CrossRef - Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace
Guoyang Zhang, Jingjing Song, Zongfeng Feng, Wentao Zhao, Pan Huang, Li Liu, Yang Zhang, Xufeng Su, Yukang Wu, Yi Cao, Zhengrong Li, Zhigang Jie
Frontiers in Oncology.2023;[Epub] CrossRef - Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer
Anqi Wang, Jieli Zhou, Gang Wang, Beibei Zhang, Hongyi Xin, Haiyang Zhou
Asian Journal of Surgery.2023; 46(9): 3568. CrossRef - Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance
Yuanqing Yang, Kai Sun, Yanhua Gao, Kuansong Wang, Gang Yu
Diagnostics.2023; 13(19): 3115. CrossRef - Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry
Sean A. Rasmussen, Valerie J. Taylor, Alexi P. Surette, Penny J. Barnes, Gillian C. Bethune
Applied Immunohistochemistry & Molecular Morphology.2022; 30(10): 668. CrossRef
- Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review
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