Esophageal cancer has a dismal prognosis and necessitates a multimodal and multidisciplinary approach from diagnosis to treatment. High-definition white-light endoscopy and histopathological confirmation remain the gold standard for the definitive diagnosis of premalignant and malignant lesions. Artificial intelligence using deep learning (DL) methods for image analysis constitutes a promising adjunct for the clinical endoscopist that could effectively decrease BE overdiagnosis and unnecessary surveillance, while also assisting in the timely detection of dysplastic BE and esophageal cancer. A plethora of studies published during the last five years have consistently reported highly accurate DL algorithms with comparable or superior performance compared to endoscopists. Recent efforts aim to expand DL utilization into further aspects of esophageal neoplasia management including histologic diagnosis, segmentation of gross tumor volume, pretreatment prediction and post-treatment evaluation of patient response to systemic therapy and operative guidance during minimally invasive esophagectomy. Our manuscript serves as an introduction to the growing literature of DL applications for image analysis in the management of esophageal neoplasia, concisely presenting all currently published studies. We also aim to guide the clinician across basic functional principles, evaluation metrics and limitations of DL for image recognition to facilitate the comprehension and critical evaluation of the presented studies.
食管癌预后不良，从诊断到治疗均需采取多模式、多学科的综合策略。高清白光内镜联合组织病理学检查仍是诊断癌前病变及恶性病变的金标准。基于深度学习的人工智能图像分析技术为临床内镜医师提供了极具前景的辅助工具，可有效减少巴雷特食管的过度诊断和不必要监测，同时有助于及时发现异型增生性巴雷特食管及食管癌。近五年来大量研究一致表明，深度学习算法具有高度准确性，其性能与内镜医师相当甚至更优。当前研究正致力于将深度学习技术拓展至食管肿瘤管理的更多领域，包括组织病理诊断、大体肿瘤体积分割、治疗前预测、治疗后疗效评估以及微创食管切除术中的手术引导。本文旨在系统介绍深度学习在食管肿瘤影像分析管理中的应用进展，全面梳理当前已发表的研究成果，同时从图像识别的基本原理、评估指标及局限性等方面为临床医师提供指引，以促进对相关研究的深入理解与批判性评价。
Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer
肿瘤(癌症)患者之家