Developing and validating a prognostic nomogram for ovarian clear cell carcinoma patients: A retrospective comparison of lymph node staging schemes with competing risk analysis

Abstract

PurposeLymph node (LN) involvement is a key factor in ovarian clear cell carcinoma (OCCC) although, there several indicators can be used to define prognosis. This study examines the prognostic performances of each indicator for OCCC patients by comparing the number of lymph nodes examined (TNLE), the number of positive lymph nodes (PLN), lymph node ratio (LNR), and log odds of metastatic lymph nodes (LODDS).Methods1,300 OCCC patients who underwent lymphadenectomy between 2004 and 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Primary outcomes were Overall Survival (OS) and the cumulative incidence of Cancer-Specific Survival (CSS). Kaplan–Meier’s and Fine-Gray’s tests were implemented to assess OS and CSS rates. After conducting multivariate analysis, nomograms using OS and CSS were constructed based upon an improved LN system. Each nomograms’ performance was assessed using Receiver Operating Characteristics (ROC) curves, calibration curves, and the C-index which were compared to traditional cancer staging systems.ResultsMultivariate Cox’s regression analysis was used to assess prognostic factors for OS, including age, T stage, M stage, SEER stage, and LODDS. To account for the CSS endpoint, a proportional subdistribution hazard model was implemented which suggested that the T stage, M stage, SEER stage, and LNR are all significant. This enabled us to develop a LODDS-based nomogram for OS and a LNR-based nomogram for CSS. C-indexes for both the OS and CSS nomograms were higher than the traditional American Joint Committee on Cancer (AJCC), 8th edition, staging system. Area Under the Curve (AUC) values for predicting 3- and 5-year OS and CSS between nomograms also highlighted an improvement upon the AJCC staging system. Calibration curves also performed with consistency, which was verified using a validation cohort.ConclusionsLODDS and LNR may be better predictors than N stage, TNLE, and PLNs. For OCCC patients, both the LODDS-based and LNR-based nomograms performed better than the AJCC staging system at predicting OS and CSS. However, further large sample, real-world studies are necessary to validate the assertion.

Exploration and prognostic analysis of two types of high-risk ovarian cancers: clear cell vs. serous carcinoma: a population-based study

“…However, we can confirm that it has distinct histopathological, and molecular characteristics compared with other histologic subtypes of epithelial ovarian cancer [4,24,25]. Previous studies have established that the prognosis of advanced OCCC is far from satisfactory, even worse than that of advanced OSC [26]. This aligns with our findings, which demonstrate that survival outcomes for late-stage OCCC patients are even poorer compared to those in early-stage.…”

Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast

“…It considers both positive and negative lymph node status and has been demonstrated to be clinically valuable by many studies [ 14 – 17 , 21 , 25 , 26 ]. Several studies have suggested using pLN and pLNR [ 27 , 28 ]. However, the pLN is also directly limited by the eLN, and additional help is needed to avoid staging migration [ 29 – 31 ].…”

Development and Validation of Deep Learning Models for Ovarian Clear Cell Carcinoma Survival

“…However, However, this model was only internally validated and lacks external validation using cohorts from other countries to assess its performance. Similarly, Li et al [25] established a nomogram for predicting progression-free survival and overall survival in OCCC patients at a large ovarian cancer center in China. They demonstrated that their nomogram outperformed the FIGO system, but external validation will still be necessary in future studies.…”

“…Nomograms have been widely used as the preferred method for individualized predictions in numerous model building studies [14,25,26]. However, nomograms have limitations in time-event prediction and fail to capture the nonlinear relationships among clinical features.…”