Artificial intelligence-assisted esophageal cancer management: Now and future

Abstract

Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

Keywords: Artificial intelligence; Computer-aided diagnosis; Deep learning; Esophageal squamous cell cancer; Barrett’s esophagus; Endoscopy

Core Tip: Deep-learning-based artificial intelligence (AI) is a breakthrough technology that has been widely explored in diagnosis, treatment and prediction of esophageal cancer. Recent studies have dealt with limitations of previous researches, including small sample size, selection bias, lack of external validation and algorithm efficiency. Favorable outcomes that are comparable to experienced endoscopists have been achieved with satisfactory robustness, indicating a real-time potential. Future randomized controlled trials are needed to further address these issues concerning AI to provide an ultimate patient-centered satisfaction, in an interpretable, ethical and legal manner.