CCCO was a type of epithelial ovarian cancer, which was highly malignant and had a poor prognosis[6]. Studies showed that the incidence of Asians was significantly higher than that of other races[7, 8]. The median age of this disease was 55 years old[9]. In our study, both CCAO and MCAO patients were selected for surgical treatment, whose age were between 50–59 years old (CCAO: 34.69%, MCAO: 32.05%), and most of them were white people. For CCAO patients, who younger than 40 years old were apt to own a poor prognosis, while there is no statistical difference between the age and poor prognosis for MCAO patients. Interestingly, bilateral ovarian disease of the patients with CCAO increased their poor prognosis. As the level of AJCC T staging increasing of CCAO or MCAO patients, the risk of adverse prognosis improved. Lymph node metastasis and distant organ metastasis were the important factors for its adverse outcome. Importantly, Multi-regional lymph node surgical removal (4 or more regional) could improve a good survival outcome for CCAO patient. In MCAO patients, the worse of the grade, the worse of the survival prognosis. Regional lymph node dissection could improve their poor prognosis. Many studies have shown that lymphadenectomy was very controversial for their prognosis[10–12], but we recommend that patients with CCAO or MCAO could undergo lymphadenectomy during surgery.
The nomogram model is currently an important tool for assessing the staging system and prognosis, and it is widely used in clinical research[13–15]. Our research comprehensively analyzed the above independent factors by establishing nomogram prediction model, using to predict the 3-year and 5-year CSS in CCAO or MCAO patients. In the nomogram, we could quickly screen out the independent risk factors. And according to their total scores added, the 3-year and 5-year CSS of patients with CCAO or MCAO after surgery were displayed, which had important clinical value.
At the same time, the ROC curve was established for the two models, which showed that the two models had better discrimination and better diagnostic effects. By drawing the calibration curve, we found that the predicted value and the actual value in the curve diagram of the two prediction models were consistent, which indicated that the prediction models had better calibration capabilities. Therefore, the establishment of the two models was satisfactory and could be used for clinical preliminary treatment evaluation. Some studies have shown that for CCCO patients, the survival rate after surgery was the key to evaluating treatment[16–18]. Our survival analysis showed that the survival time of patients with high-risk factors was shorter than that of patients with low-risk factors.
The shortcomings of this study were that the prognosis of patients was evaluated only from the perspective of surgery, and the included indicators had some limitations. At the same time, the study did not conduct external verification. Therefore, it is necessary to collect more clinical data to verify this and provide a more favorable guarantee for the implementation of precise and individualized treatment.