A Novel Clinical Nomogram for Predicting Cancer-Specific Survival in Adult Patients After Primary Surgery for Epithelial Ovarian Cancer: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database and External Validation in a Tertiary Center

Abstract

BackgroundThe present study aimed to construct and validate a nomogram that can be used to predict cancer-specific survival (CSS) in patients with epithelial ovarian cancer (EOC).MethodsA total of 7,129 adult patients with EOC were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Patients were randomly divided into the training and validation cohorts (7:3). Cox regression was conducted to evaluate prognostic factors of CSS. The internal validation of the nomogram was performed using concordance index (C-index), AUC, calibration curves, and decision curve analyses (DCAs). Data from 53 adult EOC patients at Shengjing Hospital of China Medical University from 2008 to 2012 were collected for external verification. Kaplan–Meier curves were plotted to compare survival outcomes among risk subgroups.ResultsAge, grade, histological types, stage, residual lesion size, number of regional lymph nodes resected, number of positive lymph nodes, and chemotherapy were independent risk factors for CSS. Based on the above factors, we constructed a nomogram. The C-indices of the training cohort, internal validation cohort, and external verification group were 0.763, 0.750, and 0.920, respectively. The calibration curve indicated good agreement between the nomogram prediction and actual survival. AUC and DCA results indicated great clinical usefulness of the nomogram. The differences in the Kaplan–Meier curves among different risk subgroups were statistically significant.ConclusionsWe constructed a nomogram to predict CSS in adult patients with EOC after primary surgery, which can assist in counseling and guiding treatment decision making.

Prognostic Value of Lymph Node Parameters in Elderly Patients With Stage III Serous Ovarian Cancer Based on Competing Risk Model

“…In prior studies of ovarian cancer, the number of positive nodes, lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) were identified as significant predictive variables. 15,21,22 The prevalence of PNs is an independent risk factor that affects the prognosis of patients with epithelial ovarian cancer (EOC), with patients with 4 or even more PNs having the worst prognosis. 15 Using a competing risk model, we determined that the number of examined lymph nodes (ELN) > 22 was an independently protective factor, while PN > 8 was a risk factor that significantly impacted the patient's prognosis.…”

“…15,21,22 The prevalence of PNs is an independent risk factor that affects the prognosis of patients with epithelial ovarian cancer (EOC), with patients with 4 or even more PNs having the worst prognosis. 15 Using a competing risk model, we determined that the number of examined lymph nodes (ELN) > 22 was an independently protective factor, while PN > 8 was a risk factor that significantly impacted the patient's prognosis. These findings indicate a more exact range of intervals for stage III serous ovarian cancer in older people.…”

“…Numerous prior studies have determined that the number of positive lymph nodes (PN), the ratio of positive to investigated lymph nodes, and the location of PNs are significant survival predictors in ovarian cancer. 15 In addition to the N stage in FIGO staging, 13 there are 2 other metrics for evaluating lymph node status: the number of lymph nodes examined the number of lymph nodes examined (ELN) and the number of PNs. 16 According to the results of current research, ELN may indicate the state of lymphocyte response to cancer, with a higher number indicating better lymphocyte response to tumor cells, while PN mostly refers to the number of metastatic lymph nodes.…”

A novel nomogram and risk classification system predicting the overall survival of patients with papillary renal cell carcinoma after nephrectomy: A population-based study

“…It is true that the survival outcome is reflected not only by the TNM stage in the traditional AJCC staging but also by other prognostic factors, including age, sex, race, marital status, pathological grade, surgery, and adjunctive therapy. A nomogram based on AJCC staging in combination with other important clinical indicators has been widely applied as a convenient and effective tool to quantitatively predict survival time, and its accuracy and reliability have been validated in multiple cancers (32)(33)(34)(35). Our previous studies also identified novel prognosis-related biomarkers and established nomograms to predict the recurrence-free survival of patients with RCC (36)(37)(38).…”

A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer

“…At present, there have been several population-based large data studies on ovarian cancer, but most of them focus on the risk factors for ovarian cancer survival and prediction model construction [ 6 – 8 ]. Although studies have performed univariate and multivariate logistic regression analyses to determine the factors related to the development of epithelial and serous ovarian cancer lung metastasis [ 9 , 10 ] and build models and nomograms to predict the risk of lung metastasis in patients with ovarian cancer [ 11 ], the C-index was 0.761 (0.736-0.787), and the accuracy was not high.…”