Xiaojuan Xu1, Nan Li2, Yan Chen1, Han Ouyang1, Xinming Zhao1, Jing Zhou3. 1. Department of Diagnostic Imaging, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 2. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China. 3. Department of Gynecology, The Central Hospital of Karamay, Xinjiang, Uyghur Autonomous Region, China. Electronic address: zhou_gyn@126.com.
Abstract
AIM: Accurate preoperative assessment of ovarian malignancy in endometrial carcinoma helps in determining the decision to preserve the ovaries in individualized treatment. This study adopted decision tree method to evaluate the diagnostic efficiency of pelvic MRI and clinical data of patients for preoperative identification of endometrial carcinoma-combined ovarian malignancy (EC-OM). MATERIAL AND METHODS: This retrospective study included a total of 801 patients, and postoperative pathological examinations identified 58 EC-OM group and 743 endometrial carcinoma cases without ovarian malignancy (EC group). Diagnostic efficiency of pelvic MRI in EC-OM was calculated by comparing the clinical data and imaging features of patients in the two groups. Decision tree analysis was performed to screen out associative indexes and establish a diagnostic model for EC-OM. RESULTS: Pelvic MRI showed that, EC-OM group showed deeper invasion into the myometrium, and higher percentages of patients with cervical or cornual involvement, or metastasis of lymph nodes or peritoneum than EC group (P = 0.00). Preoperative pelvic MRI showed a sensitivity of 51.72% and a specificity of 99.87% when detecting ovarian malignancy in endometrial carcinoma. Decision tree model obtained a sensitivity of 89.66%, with an AUC (area under ROC curve) of 0.949 (95% CI 0.906, 0.993, P < 0.001). CONCLUSION: Decision tree analysis based on pelvic MRI and clinical data of patients showed that the detection rate of ovarian malignancy could be increased for patients with endometrial carcinoma.
AIM: Accurate preoperative assessment of ovarian malignancy in endometrial carcinoma helps in determining the decision to preserve the ovaries in individualized treatment. This study adopted decision tree method to evaluate the diagnostic efficiency of pelvic MRI and clinical data of patients for preoperative identification of endometrial carcinoma-combined ovarian malignancy (EC-OM). MATERIAL AND METHODS: This retrospective study included a total of 801 patients, and postoperative pathological examinations identified 58 EC-OM group and 743 endometrial carcinoma cases without ovarian malignancy (EC group). Diagnostic efficiency of pelvic MRI in EC-OM was calculated by comparing the clinical data and imaging features of patients in the two groups. Decision tree analysis was performed to screen out associative indexes and establish a diagnostic model for EC-OM. RESULTS: Pelvic MRI showed that, EC-OM group showed deeper invasion into the myometrium, and higher percentages of patients with cervical or cornual involvement, or metastasis of lymph nodes or peritoneum than EC group (P = 0.00). Preoperative pelvic MRI showed a sensitivity of 51.72% and a specificity of 99.87% when detecting ovarian malignancy in endometrial carcinoma. Decision tree model obtained a sensitivity of 89.66%, with an AUC (area under ROC curve) of 0.949 (95% CI 0.906, 0.993, P < 0.001). CONCLUSION: Decision tree analysis based on pelvic MRI and clinical data of patients showed that the detection rate of ovarian malignancy could be increased for patients with endometrial carcinoma.