The early diagnosis of endometrial carcinoma is critical for improving patient survival and prognosis. However, the diagnostic efficiency of a single examination is often insufficient, because it is easy to cause misdiagnosis and missed diagnoses. Therefore, this study used the classification and regression tree (CART) algorithm to establish and validate a CART model to distinguish endometrial carcinoma from other endometrial lesions. The clinical data of 297 patients treated at Changde Hospital, Xiangya School of Medicine, Central South University between April 2021 and April 2023 for postmenopausal uterine effusion, postmenopausal vaginal bleeding, abnormal uterine bleeding and endometrial thickening were retrospectively analyzed. Among them, there were 203 cases of endometrial carcinoma and 94 cases of endometrial lesions. The pathological results from endometrial biopsy and hysteroscopic curettage were compared. The coincidence rate of endometrial biopsy was 90.34% (187/207) and the AUC, sensitivity, and specificity of the diagnosis of endometrial carcinoma were 0.920, 0.914, and 0.925, respectively. Six serological indicators with diagnostic significance were screened out: carbohydrate antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), human epididymis secretory protein 4 (HE4), vascular endothelial growth factor (VEGF), D-dimer, and absolute neutrophil count (N). The AUC, sensitivity and specificity of the CART model based on the above indicators were 0.949, 0.979, and 0.896, respectively. The CART model is an intuitive and simple tool for the clinical diagnosis of endometrial carcinoma and endometrial lesions.
Keywords: Endometrial biopsy; decision tree; diagnosis; endometrial carcinoma.
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