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

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

BACKGROUND

Mucinous epithelial ovarian cancer (mEOC) is a relatively uncommon subtype of ovarian cancer with special prognostic features, but there is insufficient research in this area. This study aimed to develop a nomogram for the cancer-specific survival (CSS) of mEOC based on Surveillance, Epidemiology, and End Results (SEER) database and externally validate it in National Union of Real World Gynecological Oncology Research and Patient Management (NUWA) platform from China.

METHODS

Patients screened from SEER database were allocated into training and internal validation cohort in a ratio of 7: 3, with those from NUWA platform as an external validation cohort. Significant factors selected by Cox proportional hazard regression were applied to establish a nomogram for 3-year and 5-year CSS. The performance of nomogram was assessed by concordance index, calibration curves and Kaplan-Meier (K-M) curves.

RESULTS

The training cohort (n = 572) and internal validation cohort (n = 246) were filtered out from SEER database. The external validation cohort contained 186 patients. Baseline age, tumor stage, histopathological grade, lymph node metastasis and residual disease after primary surgery were significant risk factors (p < 0.05) and were included to develop the nomogram. The C-index of nomogram in training, internal validation and external validation cohort were 0.869 (95% confidence interval [CI], 0.838-0.900), 0.839 (95% CI, 0.787-0.891) and 0.800 (95% CI, 0.738-0.862), respectively. The calibration curves of 3-year and 5-year CSS in each cohort showed favorable agreement between prediction and observation. K-M curves of different risk groups displayed great discrimination.

CONCLUSION

The discrimination and goodness of fit of the nomogram indicated its satisfactory predictive value for the CSS of mEOC in SEER database and external validation in China, which implies its potential application in different populations.

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

Background

Large cell lung cancer (LCLC) is a rare subtype of non-small cell lung carcinoma (NSCLC), and little is known about its clinical and biological characteristics.

Methods

LCLC patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. All patients were randomly divided into a training group and a validation group at a ratio of 7:3. The independent prognostic factors that were identified (P < 0.01) by stepwise multivariate Cox analysis were incorporated into an overall survival (OS) prediction nomogram, and risk-stratification systems, C-index, time-ROC, calibration curve, and decision curve analysis (DCA) were applied to evaluate the quality of the model.

Results

Nine factors were incorporated into the nomogram: age, sex, race, marital status, 6th AJCC stage, chemotherapy, radiation, surgery and tumor size. The C-index of the predicting OS model in the training dataset and in the test dataset was 0.757 ± 0.006 and 0.764 ± 0.009, respectively. The time-AUCs exceeded 0.8. The DCA curve showed that the nomogram has better clinical value than the TNM staging system.

Conclusions

Our study summarized the clinical characteristics and survival probability of LCLC patients, and a visual nomogram was developed to predict the 1-year, 3-year and 5-year OS of LCLC patients. This provides more accurate OS assessments for LCLC patients and helps clinicians make personal management decisions.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Purpose

Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC.

Patients and Methods

We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan–Meier analysis was used to compare the survival results among different risk subgroups.

Results

Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791–0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791–0.915), 0.886 (95% CI: 0.852–0.920) and 0.815 (95% CI: 0.766–0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group.

Conclusion

The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.