A prognostic model of patients with ovarian mucinous adenocarcinoma: a population-based analysis

Abstract

Background Ovarian mucinous carcinoma is a disease that requires unique treatment. But for a long time, guidelines for ovarian serous carcinoma have been used for the treatment of ovarian mucinous carcinoma. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with ovarian mucinous adenocarcinoma. Methods In this study, patients initially diagnosed with ovarian mucinous adenocarcinoma from 2004 to 2015 were screened from the Surveillance, Epidemiology, and End Results (SEER) database, and divided into the training group and the validation group at a ratio of 7:3. Independent risk factors for OS and CSS were determined by multivariate Cox regression analysis, and nomograms were constructed and validated. Results In this study, 1309 patients with ovarian mucinous adenocarcinoma were finally screened and randomly divided into 917 cases in the training group and 392 cases in the validation group according to a 7:3 ratio. Multivariate Cox regression analysis showed that the independent risk factors of OS were age, race, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. Independent risk factors of CSS were age, race, marital, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. According to the above results, the nomograms of OS and CSS in ovarian mucinous adenocarcinoma were constructed. In the training group, the C-index of the OS nomogram was 0.845 (95% CI: 0.821–0.869) and the C-index of the CSS nomogram was 0.862 (95%CI: 0.838–0.886). In the validation group, the C-index of the OS nomogram was 0.843 (95% CI: 0.810–0.876) and the C-index of the CSS nomogram was 0.841 (95%CI: 0.806–0.876). The calibration curve showed the consistency between the predicted results and the actual results, indicating the high accuracy of the nomogram. Conclusion The nomogram provides 3-year and 5-year OS and CSS predictions for patients with ovarian mucinous adenocarcinoma, which helps clinicians predict the prognosis of patients and formulate appropriate treatment plans.

Development and validation of a novel nomogram to predict the overall survival of patients with large cell lung cancer: A surveillance, epidemiology, and end results population-based study

“…In total, 2817 LCLC patients were randomly divided into traindataset (1972) and testdataset (845) by a ratio of 7:3; the 7:3 ratio is a mainstream training/testing dataset classification in clinical prognostic model study when the sample size is not huge [ 14 , 15 ] ( Table 1 ). Simultaneously, these patients were classified into 3 groups in terms of age (≤70 years,71–77years, ≥78 years) and 2 groups in terms of tumor size (≤45 mm, >45 mm) in the traindataset.…”

Development and validation of a nomogram to predict cancer-specific survival of mucinous epithelial ovarian cancer after cytoreductive surgery

“…Recently, a few retrospective survival prediction studies specialized in mEOC came into view, which may compensated for the deficiency of prospective studies. Yang et al [ 20 ] constructed a prognostic model for overall survival (OS) and CSS of mucinous adenocarcinoma based on SEER database, which included baseline age, race, TNM stage, grade, CA125 and chemotherapy in the nomogram. The C-index of nomogram for OS was 0.845 and that for CSS was 0.862.…”

“…Consistent with previous studies, baseline factors such as older age at diagnosis, higher stage of disease, poorer histopathological differentiation and lymph node metastasis had detrimental impacts on CSS [ 6 , 9 , 22 , 23 ]. We integrated the FIGO stage and SEER summary stage into analysis instead of the TNM stage included in previous studies [ 20 , 24 ], which took into account both specificity in the staging of ovarian cancer and applicability in SEER database. Notably, we included residual disease into the survival prediction model of mEOC for the first time and demonstrated that the less residual disease after cytoreductive surgery was, the less risk of death would be, whereas other models hadn’t focused on it [ 8 , 20 , 24 ].…”

“…We integrated the FIGO stage and SEER summary stage into analysis instead of the TNM stage included in previous studies [ 20 , 24 ], which took into account both specificity in the staging of ovarian cancer and applicability in SEER database. Notably, we included residual disease into the survival prediction model of mEOC for the first time and demonstrated that the less residual disease after cytoreductive surgery was, the less risk of death would be, whereas other models hadn’t focused on it [ 8 , 20 , 24 ]. It is widely acknowledged that optimal debulking plays a prominent role in the prognosis of all histotypes including mEOC because it reduces the probability of recurrence and lays a good foundation for subsequent treatment [ 25 , 26 ].…”

“…To our knowledge, this is the first study to concentrate on postoperative mEOC patients exclusively. Secondly, since there hasn’t been any predictive model for mEOC evaluated in Chinese patients so far [ 8 , 20 , 24 ], our study could make up for the scarcity of relevant research and broaden its applicability in different ethnic groups. In general, this nomogram may help doctors and patients to predict the survival of mEOC immediately after cytoreductive surgery, so that they can make appropriate decisions for more personalized clinical management.…”