CELL INTELLIGENCE · 肿瘤细胞治疗研究

BACKGROUND: The objective of this study was to establish a new model for predicting the prognosis of endometrial carcinoma (EC) using tumor-infiltrating lymphocytes (TILs) based on artificial intelligence (AI). METHODS: Patients with EC who were treated between 1989 and 2022 were included in this study. For each patient, one hematoxylin and eosin-stained slide containing the most invasive frontline of the tumor was selected and digitized. The area within a 500 m width span, extending 250 m toward the stroma and tumor from the manually annotated invasive frontline, was automatically annotated. The average number of lymphocytes per area ( m 2 ) in the annotated area was calculated using AI. Patients were classified into the High-TIL and Low-TIL groups, and survival analysis was conducted. Four mismatch repair (MMR)-related proteins were evaluated using immunohistochemical staining. RESULTS: A total of 659 patients were included: 346 (52.5%) in the High-TIL group and 313 (47.5%) in the Low-TIL group. MMR deficiency was observed more frequently in the High-TIL group than in the Low-TIL group (p < 0.01). Progression-free survival (PFS) and overall survival (OS) were better in the High-TIL group than in the Low-TIL group (both p < 0.01). Multivariate analysis revealed that TIL status was a prognostic factor for PFS (hazard ratio [HR] (95% confidence interval [CI]) 0.61 (0.43-0.87); p < 0.01) and OS (HR (95% CI) 0.54 (0.33-0.86); p = 0.01). CONCLUSION: TILs evaluated using AI could accurately and significantly predict the prognosis of EC. Further studies are needed to establish new methods for evaluating TILs in ECs.