ArticleOncology letters2026
A nomogram based on immunohistochemistry and lymphocyte-to-monocyte ratio for predicting risk stratification in endometrial carcinoma.
Article in Oncology letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Currently, the determination of the risk level for endometrial cancer (EC) needs to be made after radical surgery, based on factors such as tumour type and differentiation, extent of invasion and clinical stage. If the early diagnosis of high-risk EC can be improved, it will greatly contribute to enhancing patient survival rates. The present study aimed to construct a nomogram to predict high-risk EC by combining immunohistochemical (IHC) and serological indicators, and then evaluating and verifying the value of it. A total of 130 patients with EC admitted to Lianyungang Maternal and Child Health Hospital from December 2018 to May 2026 were included. The training set consisted of 107 cases, while the validation set contained 23 cases. All cases were divided into a high-risk group and a low-risk group on the basis of the postoperative pathological results. The clinical data, IHC staining results and preoperative serological indicators of the two groups were compared. Logistic regression analysis was used to screen the risk factors for high-risk EC. A nomogram model was created using R software and then evaluated through receiver operating characteristic (ROC) curves, calibration curves and external validation. In the training set, univariate analysis revealed that the indicators with statistically significant differences between the two groups were age, nuclear-associated antigen (Ki-67) expression, oestrogen receptor (ER) expression and the lymphocyte-to-monocyte ratio (LMR). Multivariate regression analysis revealed that age, the Ki-67 index and the LMR were independent risk factors for high-risk EC (all P<0.05). The results of the nomogram model and ROC curve analysis indicated that, compared with age, the Ki-67 index and the LMR individually, the combined prediction model constructed from these three factors demonstrated greater diagnostic performance [area under the curve (AUC)=0.904; 95% CI=0.840-0.968; sensitivity: 75.6%; specificity: 93.9%; P<0.05]. The goodness-of-fit test results suggested that the predictive model had a good fit (X
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