Abstract
Purpose
Computer-aided diagnosis systems for polyp characterization are commercially available but cannot recognize subtypes of sessile lesions. This study aimed to develop a computer-aided diagnosis system to characterize polyps using non-magnified white-light endoscopic images.
Methods
A total of 2249 non-magnified white-light images from 1030 lesions including 534 tubular adenomas, 225 sessile serrated adenoma/polyps, and 271 hyperplastic polyps in the proximal colon were consecutively extracted from an image library and divided into training and testing datasets (4:1), based on the date of colonoscopy. Using ResNet-50 networks, we developed a classifier (1) to differentiate adenomas from serrated lesions, and another classifier (2) to differentiate sessile serrated adenoma/polyps from hyperplastic polyps. Diagnostic performance was assessed using the testing dataset. The computer-aided diagnosis system generated a probability score for each image, and a probability score for each lesion was calculated as the weighted mean with a log10-transformation. Two experts (E1, E2) read the identical testing dataset with a probability score.
Results
The area under the curve of classifier (1) for adenomas was equivalent to E1 and superior to E2 (classifier 86%, E1 86%, E2 69%; classifier vs. E2, p < 0.001). In contrast, the area under the curve of classifier (2) for sessile serrated adenoma/polyps was inferior to both experts (classifier 55%, E1 68%, E2 79%; classifier vs. E2, p < 0.001).
Conclusion
The classifier (1) developed using white-light images alone compares favorably with experts in differentiating adenomas from serrated lesions. However, the classifier (2) to identify sessile serrated adenoma/polyps is inferior to experts.
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Acknowledgements
The Statistical Consulting Service at Health Data Science Research Section, Tokyo Metropolitan Institute of Gerontology, and Clinical Research Center at Fukushima Medical University, in particular Dr. Noriko Tanaka, helped with the interpretation of the results from statistical analysis in this study. Dr. Hiroshi Hojo, former professor at Aizu Medical Center Fukushima Medical University helped with the interpretation of the histological assessment.
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Study conception and design: Nemoto, Guo, Hayashi, Utano, Aizawa, Zhu, Togashi. Acquisition of data: Nemoto, Aizawa, Utano, Nakajima, Hayashi, Yamashina, Togashi. Interpretation of data: Guo, Peng, Zhang, Zhu, Nemoto, Togashi. Drafting of the manuscript: Nemoto, Guo, Zhu, Togashi. Critical revision of the manuscript for important intellectual content: Nemoto, Guo, Peng, Zhang, Zhu, Aizawa, Nakajima, Utano, Hayashi, Yamashina, Lefor, Togashi. Statistical analysis: Guo, Zhu, Nemoto, Togashi. Obtained funding: Zhu, Togashi. Administrative, technical, or material support: Zhu, Togashi. Study supervision: Zhu, Togashi. Final manuscript approval: Nemoto, Guo, Peng, Zhang, Aizawa, Nakajima, Utano, Hayashi, Yamashina, Lefor, Zhu,Togashi.
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Nemoto, D., Guo, Z., Peng, B. et al. Computer-aided diagnosis of serrated colorectal lesions using non-magnified white-light endoscopic images. Int J Colorectal Dis 37, 1875–1884 (2022). https://doi.org/10.1007/s00384-022-04210-x
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DOI: https://doi.org/10.1007/s00384-022-04210-x