Abstract
Simple SummaryDetection of early esophageal cancer is important to improve patient’s survival, but accurate diagnosis of superficial esophageal neoplasms is difficult even for experienced endoscopists. Computer-aided diagnostic system is believed to be an important method to provide accurate and rapid assistance for endoscopists in diagnosing esophageal neoplasms. We developed a single-shot multibox detector using a convolutional neural network for diagnosing esophageal cancer by using endoscopic images and the aim of our study was to assess the ability of our system. Our system showed good diagnostic performance in detecting as well as differentiating esophageal neoplasms and the accuracy can achieve 90%. Differentiating different histological grades of esophageal neoplasm is usually conducted by magnified endoscopy and we confirm that artificial intelligence system has great potential for helping endoscopists in accurately diagnosing superficial esophageal neoplasms without the necessity of magnified endoscopy and experienced endoscopists.Diagnosis of early esophageal neoplasia, including dysplasia and superficial cancer, is a great challenge for endoscopists. Recently, the application of artificial intelligence (AI) using deep learning in the endoscopic field has made significant advancements in diagnosing gastrointestinal cancers. In the present study, we constructed a single-shot multibox detector using a convolutional neural network for diagnosing different histological grades of esophageal neoplasms and evaluated the diagnostic accuracy of this computer-aided system. A total of 936 endoscopic images were used as training images, and these images included 498 white-light imaging (WLI) and 438 narrow-band imaging (NBI) images. The esophageal neoplasms were divided into three classifications: squamous low-grade dysplasia, squamous high-grade dysplasia, and squamous cell carcinoma, based on pathological diagnosis. This AI system analyzed 264 test images in 10 s, and the sensitivity, specificity, and diagnostic accuracy of this system in detecting esophageal neoplasms were 96.2%, 70.4%, and 90.9%, respectively. The accuracy of this AI system in differentiating the histological grade of esophageal neoplasms was 92%. Our system showed better accuracy in diagnosing NBI (95%) than WLI (89%) images. Our results showed the great potential of AI systems in identifying esophageal neoplasms as well as differentiating histological grades.
Highlights
Esophageal cancer is a highly aggressive cancer with a poor prognosis, and around508,000 esophageal cancer-related deaths were recorded globally in 2018
The accuracy of single-shot multibox detector (SSD) was similar in diagnosing narrowband imaging (NBI) and white-light imaging (WLI) images (p = 0.61)
positive predictive value (PPV), and F1-score of our SSD in analyzing NBI images of different histological grades of esophageal neoplasms than WLI (Table 4)
Summary
Esophageal cancer is a highly aggressive cancer with a poor prognosis, and around. 508,000 esophageal cancer-related deaths were recorded globally in 2018. The prognosis of esophageal cancer is usually good in its early stages with a 5-year survival rate reaching 80%, but extremely poor in its advanced stages with a 5-year survival rate of less than 20% [2]. Most esophageal cancer is diagnosed at advanced stages because typical symptoms such as dysphagia and odynophagia usually develop during these later stages. Even though image-enhanced endoscopy, such as Lugol’s chromoendoscopy and narrowband imaging (NBI), is recommended in addition to WLI to improve the detection rate of esophageal precancerous lesions as well as superficial cancer, interobserver variation still exists, especially with inexperienced endoscopists [4,5,6] Esophagogastroduodenoscopy (EGD) is the most sensitive examination approach as well as being the gold standard for diagnosis of esophageal cancer and precancerous lesions; the diagnosis of esophageal precancerous lesions and superficial cancer still presents great challenges for endoscopists, as these lesions are overlooked in conventional white-light imaging (WLI) and about 40% of lesions might be missed [3].
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