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Background: Currently, there is a lack of prognostic evaluation methods for non-serous epithelial ovarian cancer (EOC). Method: We collected patients with non-serous EOC diagnosed between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database into a training cohort (n = 2078) and an internal validation cohort (n = 891). Meanwhile, patients meeting the criteria were screened from the Fujian Provincial Maternal and Child Health Hospital from 2013 to 2022 as an external validation cohort (n = 56). Univariate and multivariable logistic regression were used to determine the independent prognostic factors of cancer-specific survival (CSS) to construct the nomogram. The nomogram was validated by the concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curves. Result: Age, laterality, preoperative CA125 status, histologic type, tumor grade, AJCC stage, surgery lesion, number of lymph nodes examined, residual lesion size, and bone metastasis were identified as independent prognostic factors to construct the nomogram. The nomogram showed better predictive ability than FIGO stage through internal and external cohorts validation. The C-index of the nomogram in the training cohort, validation cohort, and external validation cohort were 0.831, 0.835 and 0.944 higher than those of the Federation International of Gynecology and Obstetric (FIGO) stage, P

Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]