[Application of transformer in intelligent diagnosis and treatment of peritoneal metastasis: current status and prospects].

Peritoneal metastasis is a common manifestation of advanced malignant tumors. Traditional imaging and cytology have insufficient sensitivity for detecting subcentimeter lesions, whereas Transformer models, leveraging self-attention mechanisms, exhibit unique advantages in global context modeling and multimodal fusion. These models have shown preliminary potential in the fields of imaging, pathological and molecular diagnosis, and prognosis prediction. However, existing studies are generally constrained by single-center, small-sample designs, limited cross-center generalizability, and a lack of prospective validation; model interpretability and the computational complexity of clinical deployment also constitute critical bottlenecks. In the future, adopting the pretraining-fine-tuning paradigm of large-scale medical foundation models, advancing pan-omics fusion and dynamic temporal modeling, and conducting prospective interventional clinical validation will be key pathways to propel Transformers from assistive tools toward core components of intelligent diagnosis and treatment for peritoneal metastasis. This paper summarizes and analyzes currently available research data, reviews the applications of Transformers in three major scenarios-imaging diagnosis, pathological and molecular subtyping, and prognosis prediction-focuses on analyzing translational bottlenecks such as data scarcity, model interpretability, and clinical deployment, and proposes corresponding solutions and future directions.