Abstract
Differential diagnosis of true gallbladder polyps remains a challenging task. This study aimed to differentiate true polyps in ultrasound images using deep learning, especially gallbladder polyps less than 20 mm in size, where clinical distinction is necessary. A total of 501 patients with gallbladder polyp pathology confirmed through cholecystectomy were enrolled from two tertiary hospitals. Abdominal ultrasound images of gallbladder polyps from these patients were analyzed using an ensemble model combining three convolutional neural network (CNN) models and a 5-fold cross-validation. True polyp diagnosis with the ensemble model that learned only using ultrasonography images achieved an area under receiver operating characteristic curve (AUC) of 0.8960 and accuracy of 83.63%. After adding patient age and polyp size information, the diagnostic performance of the ensemble model improved, with a high specificity of 88.35%, AUC of 0.9082, and accuracy of 87.61%, outperforming the individual CNN models constituting the ensemble model. In the subgroup analysis, the ensemble model showed the best performance with AUC of 0.9131 for polyps larger than 10 mm. Our proposed ensemble model that combines three CNN models classifies gallbladder polyps of less than 20 mm in ultrasonography images with high accuracy and can be useful for avoiding unnecessary cholecystectomy with high specificity.
Highlights
Gallbladder (GB) polyps are tissue growths that protrude from the GB wall into the lumen: they can be classified into pseudopolyps, represented by cholesterol polyps, and true polyps, including adenoma and adenocarcinoma [1,2]
The images used for the deep learning analysis contained 1039 pseudopolyps and 421 true polyps
With a patient age cutoff value of 52 years, accuracy was 62.28%, sensitivity was 77.53%, and specificity was 58.98%. These results show that the pseudo and true polyp groups were not sufficiently divided by polyp size and patient age
Summary
Gallbladder (GB) polyps are tissue growths that protrude from the GB wall into the lumen: they can be classified into pseudopolyps, represented by cholesterol polyps, and true polyps, including adenoma and adenocarcinoma [1,2]. The most commonly used imaging modality for the diagnosis and follow-up of GB polyps is abdominal ultrasound [3]. GB polyps are known to be found in approximately 5% of the patients who undergo abdominal ultrasound, and the number of cases of GB polyps being incidentally detected is increasing with the recent increase in abdominal ultrasound examinations that are being conducted as part of regular health check-ups [4]. It is difficult to differentiate between true and pseudo polyps through pre-operative examinations, including abdominal ultrasound [3,5]. Several studies have investigated the risk factors of neoplastic (true) polyps by combining ultrasound findings and clinical factors [6,7,8]. One of the well-known risk factors of neoplastic GB polyps is the polyp size; typically, 10 mm is used as the cutoff value for cholecystectomy [9]. Wennmacker et al reported that, with a cutoff value of
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