Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. Although artificial intelligence (AI) has brought a great promise to assist pathologist to screen digitalized whole slide images, existing AI systems are limited in fine-grained cancer subclassifications and have little usability in planning cancer treatment. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance. The AI system is designed to efficiently differentiate multi-classes of GC through multi-scale self-attention mechanism using 2-stage hybrid Vision Transformer (ViT) networks, by mimicking the way how human pathologists understand histology. The AI system demonstrates reliable diagnostic performance by achieving class-average sensitivity of above 0.85 on a total of 1,212 slides from multicentric cohort. Furthermore, AI-assisted pathologists show significantly improved diagnostic sensitivity by 12% in addition to 18% reduced screening time compared to human pathologists. Our results demonstrate that AI-assisted gastric endoscopic screening has a great potential for providing presumptive pathologic opinion and appropriate cancer treatment of gastric cancer in practical clinical settings.

翻译：胃内镜筛查是早期决定胃癌（GC）适当治疗方案、降低相关死亡率的重要手段。尽管人工智能（AI）为辅助病理学家筛选数字化全切片图像带来了巨大希望，但现有AI系统在精细的癌症亚分类方面能力有限，且对制定癌症治疗方案的实用性较低。我们提出了一种实用的AI系统，能够实现胃癌病理学的五分类，这些分类可直接对应一般的胃癌治疗指南。该AI系统通过模仿人类病理学家理解组织学的方式，采用两阶段混合视觉Transformer（ViT）网络，借助多尺度自注意力机制高效区分胃癌的多类别。基于多中心队列共1,212张切片的测试，该系统展现出可靠的诊断性能，类别平均灵敏度超过0.85。此外，与人类病理学家相比，AI辅助病理学家的诊断灵敏度显著提升12%，同时筛查时间减少18%。我们的结果表明，AI辅助胃内镜筛查在实际临床环境中具有提供推定病理意见及胃癌适当治疗方案的巨大潜力。