Changes in the Management and Prognosis of Ovarian Cancer Due to the New FIGO and WHO Classifications: A Case Series Observational Descriptive Study. Seven Years of Follow-up

Ferreira, Mónica; Salas, Margarita; González-Sarmiento, Rogelio; Sánchez, María José Doyague

doi:10.1097/igc.0000000000001331

Cited by 8 publications

(5 citation statements)

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“…org/ research/resources-infrastructure/core facilities/ biospecimen-core-resource/the-cancer-g enome-atlas/tcga-formsand-documents) and the recurrence time. The clinical stages of OC were divided into 4 stages (I, II, III and IV) according to the origin, pathogenesis and prognosis by FIGO criteria (15,16).…”

Section: Methodsmentioning

confidence: 99%

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

Xuan

Huang

et al. 2020

Oncol Lett

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Ovarian cancer (OC) is one of the most fatal gynecological malignancies in the world and confers a poor 5-year survival rate. The present study was designed to discover novel prognostic markers for patients with OC in order to estimate disease metastasis or recurrence. Based on the large cohorts of transcriptome data from multicenter sources, a comprehensive analysis was performed to explore potential prognostic markers. A total of 269 differentially expressed genes were identified, of which 32 were upregulated and 237 downregulated in OC tissues compared with the corresponding expression in normal tissues. Kaplan-Meier analysis, log-rank test and nomogram analysis were employed to demonstrate that low expression levels of claudin 10 (CLDN10) were associated with a less favorable disease prognosis. The most promising prognostic marker for OC was subsequently selected. Additionally, the prognostic nomogram was constructed in order to assess the 5-year survival rate using CLDN10 expression as a prognostic marker for OC. Furthermore, gene set enrichment analysis and analysis of the tumor-associated competing endogenous RNA network were performed to elucidate the potential biological processes associated with CLDN10 expression. The current results indicated that CLDN10 may influence OC progression via transforming growth factor-β (TGF-β)-or WNT/β-catenin-induced epithelial-to-mesenchymal transition (EMT). The associations among CLDN10, microRNA-486-5p, TGF-β, WNT/β-catenin and EMT should be further investigated in future studies.

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Section: Methodsmentioning

confidence: 99%

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

Xuan

Huang

et al. 2020

Oncol Lett

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“…However, the treatment outcomes of POSRCC are generally poor, and vary widely among published reports (1)(2)(3)(4)(5)(6)(7)(8)(9). The current classifications for ovarian cancer of the International Federation of Gynecology and Obstetrics (FIGO) and the World Health Organization (WHO) have improved clinicians' ability to predict the prognosis of patients; however, these classifications still have deficiencies (10). Given that the clinical characteristics and survival of POSRCC have yet to be defined explicitly, it is rational to use a population-based cancer database to elucidate them.…”

Section: Introductionmentioning

confidence: 99%

A prognostic nomogram for women with primary ovarian signet-ring cell carcinoma

Wang¹,

2021

Ann Transl Med

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Background: Primary ovarian signet-ring cell carcinoma (POSRCC) is a rare subtype of ovarian carcinoma that is characterized by abundant mucin accumulation. POSRCC is aggressive, and the prognostic factors associated with its clinical outcome remain poorly defined. This study aimed to elucidate the clinical characteristics and survival of patients with POSRCC, and to establish an effective prognostic nomogram and risk stratification model to predict the risks associated with patient outcomes.Methods: Data of patients with POSRCC from the period 1975 to 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable analyses of demographic factors, clinicopathological characteristics, and treatments were conducted to identify significant prognostic parameters. The identified independent variables were integrated to develop a nomogram and risk stratification model. The discrimination and calibration of the nomogram were assessed with the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration curves.Results: A total of 172 patients were identified as being eligible to participate in this study. The median overall survival (OS) time was 7 months [95% confidence interval (CI), 4.6-9.4 months]. The 1-, 3-, and 5-year OS rates were 35.5%, 15.3%, and 6%, respectively. A multivariable analysis of the primary patients identified the independent predictors for survival as age at diagnosis, race, marital status, T (primary tumor size) stage, and chemotherapy, which were all incorporated into the nomogram. The C-index was 0.70 (95% CI, 0.66-0.75), which was statistically higher than that of the International Federation of Gynecology and Obstetrics (FIGO) staging system (0.58; 95% CI, 0.53-0.63). ROC curve analysis also showed that the nomogram had good discrimination, with an area under the curve (AUC) of 0.74, 0.62, and 0.71 for 1-, 3-, and 5-year survival, respectively. The calibration curves showed good agreement between the prediction by the nomogram and actual observations. A risk stratification model was further used to classify patients into a low-risk or high-risk group. The median OS time for the low-and high-risk groups was 13.0 months (95% CI, 9.33-16.67) and 2.0 months (95% CI, 1.12-2.89), respectively. Surgery did not significantly prolong survival in either group [low-risk group: hazard ratio (HR), 0.69; 95% CI, 0.45-1.07; P=0.09; high-risk group: HR, 0.55; 95% CI, 0.46-0.67; P=0.18]. Conclusions:The proposed nomogram and risk stratification model showed accurate prognostic prediction for POSRCC. These methods could improve individualized evaluations of survival and therapeutic decisions for patients with POSRCC.

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“…Despite very high initial chemosensitivity and a complete clinical response, the majority of patients relapse within the first 5 years and progressively develop resistance to various chemotherapeutic treatments [ 4 ]. Recent studies have shown that postoperative residual tumour status plays an important role in tumour recurrence [ 5 , 23 , 24 ]. However, the residual tumour status can only be obtained postoperatively.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

Wang

Chen

et al. 2022

Journal of Oncology

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Objective. To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC). Methods. This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS). Results. One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group. Conclusion. By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

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Changes in the Management and Prognosis of Ovarian Cancer Due to the New FIGO and WHO Classifications: A Case Series Observational Descriptive Study. Seven Years of Follow-up

Abstract: The newly created WHO and FIGO classifications have improved the ability to predict the prognosis and consequently to change the therapeutic managements of patients with ovarian cancer.

Cited by 8 publications

References 20 publications

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

A prognostic nomogram for women with primary ovarian signet-ring cell carcinoma

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

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