Abstract
We developed a computer-aided diagnosis (CAD) system based on linked color imaging (LCI) images to predict the histological results of polyps by analyzing the colors of the lesions. A total of 139 images of adenomatous polyps and 69 images of non-adenomatous polyps obtained from our hospital were collected and used to train the CAD system. A test set of LCI images, including both adenomatous and non-adenomatous polyps, was prospectively collected from patients who underwent colonoscopies between Oct and Dec 2017; this test set was used to assess the diagnostic abilities of the CAD system compared to those of human endoscopists (two experts and two novices). The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of this novel CAD system for the training set were 87.0%, 87.1%, 87.0%, 93.1%, and 76.9%, respectively. The test set included 115 adenomatous polyps and 66 non-adenomatous polyps that were prospectively collected. The CAD system identified adenomatous or non-adenomatous polyps in the test set with an accuracy of 78.4%, a sensitivity of 83.3%, a specificity of 70.1%, a PPV of 82.6%, and an NPV of 71.2%. The accuracy of the CAD system was comparable to that of the expert endoscopists (78.4% vs 79.6%; p = 0.517). In addition, the diagnostic accuracy of the novices was significantly lower to the performance of the experts (70.7% vs 79.6%; p = 0.018). A novel CAD system based on LCI could be a rapid and powerful decision-making tool for endoscopists.
Highlights
Colorectal tumor is one of the most common tumors worldwide[1,2]
We developed a novel computer-aided diagnosis (CAD) system that analyzed Linked color imaging (LCI) images of colorectal polyps to predict the histological results of these polyps
The algorithm for training the CAD system was based on a Gaussian mixture model (GMM) that consisted of four steps: (1) training data preparation; (2) parameter learning; (3) classification criteria creation; and (4) diagnostic output
Summary
Colorectal tumor is one of the most common tumors worldwide[1,2]. Adenomatous polyps are considered premalignant lesions of colorectal tumors[3]. Many advanced endoscopic image modalities have been developed to improve the differentiation ability in colorectal lesions, allowing for in vivo histological predictions. Linked color imaging (LCI), a new endoscopy modality, creates clear and bright images by using short wavelength narrow-band laser light This modality can separately enhance the colors of lesions and make red areas appear redder and white areas appear whiter during a colonoscopy[13,14]. We developed a novel CAD system that analyzed LCI images of colorectal polyps to predict the histological results of these polyps. This CAD system was designed to assist endoscopists rapidly classify polyps as adenomatous or non-adenomatous regardless of expert or novice status. The aims of this study were to validate the diagnostic abilities of this CAD system and to compare the performance of the CAD system with that of experts and novice endoscopists in a pilot study
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