Radiomics modeling to predict the tumor immune-microenvironment of mucinous adenocarcinoma of gastric-type cervical cancer

Gastric-type adenocarcinoma (GAS) of the cervix is usually diagnosed at advanced stages and has a poor prognosis due to resistance to standard therapies. Although immune checkpoint inhibitors have improved outcomes in cervical cancer, prognosis and treatment response are strongly influenced by the tumor immune microenvironment (TME). Histopathological assessment of GAS is challenging because it often arises in the upper cervix. This study aimed to predict the TME in GAS using MRI-based radiomics. We enrolled 16 patients with GAS treated at our institution. Tumor-infiltrating lymphocytes (TILs) were evaluated by immunohistochemistry, quantifying them with the Immunoscore. Fourteen patients with usual endocervical adenocarcinoma (UEA) served as controls. A total of 1,309 radiomic features were extracted from the primary tumor and peritumoral region on pre-treatment MRI images. After feature selection, clustering, and regression models were developed to predict the TME in GAS. GAS exhibited significantly lower T-cell infiltration than UEA, particularly in early-stage tumors. The clustering model achieved 87.5% accuracy in Immunoscore classification, and the regression model showed a strong correlation with observed TIL densities (r = 0.93, P < .001). These findings suggest that MRI-based radiomics may serve as a noninvasive biomarker for predicting the TME in GAS.