Survival Analysis of Radiation Therapy in Ovarian Cancer: A SEER Database Analysis

Yu, Lushi; Gong, Hongyun; Li, Qian; Ren, Hong; Wang, Yi; He, H H; Li, Tian; Song, Qibin

doi:10.1155/2021/8849039

Cited by 5 publications

(4 citation statements)

References 47 publications

(14 reference statements)

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“…However, a recent study revealed that RT has an adverse effect on the prognosis of patients diagnosed with ovarian cancer. 31 We speculated that RT was more commonly administered to ovarian cancer patients with nonepithelial type, distant metastases, and AJCC stages III/IV, so the poor prognosis may be attributed to a more malignant phenotype in the RT group. Thus, further subgroup and stratification analyses are required in any study.…”

Section: Discussionmentioning

confidence: 99%

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study

Jiao

Guo

et al. 2023

Technol Cancer Res Treat

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Objective: The prognostic factors for elderly patients with cervical cancer differ from those of younger patients. Competitive risk events could cause biases in the Cox proportional hazards (PH) model. This study aimed to construct a competitive risk model (CRM) nomogram for patients aged > 65 years with nonmetastatic cervical cancer. Methods: We retrospectively analyzed data extracted from the Surveillance, Epidemiology, and End Results (SEER) database and a total of 1856 patients from 18 cancer registries across the United States diagnosed between 2010 and 2015 were included. Kaplan–Meier analysis and log-rank tests were used to compare intergroup survival. Univariate and multivariate Cox proportional regression analyses were performed to identify independent prognostic factors. The cumulative incidence function (CIF) and Fine and Gray's test were used to determine the impact of competitive risk events on prognosis. The CRM nomogram was internally and externally validated using time-dependent receiver operator characteristic (ROC) curve (time-AUC), Brier scores, Harrell's concordance index (C-index), calibration curve, and decision curve analysis (DCA). Results: Analyses revealed that histology, age, the International Federation of Gynaecologists and Obstetricians (FIGO) stage, number of in situ malignancies, chemotherapy, radiotherapy (RT), and surgery were independent prognostic factors. The CRM nomogram accurately predicted 1-year, 3-year, and 5-year disease-specific survival (DSS). The C-indexes and Brier scores of the CRM nomogram were 0.641 and 0.094, respectively, at the 1-year cut-off in the training set. The time-AUC of the CRM nomogram at the 1-year, 3-year, and 5-year intervals in the training set were 77.6%, 77.3%, and 74.5%, respectively. The calibration curve demonstrated a favorable concordance. DCA suggested that the nomogram had a good net benefit. Therefore, the Cox model underestimated the weight of risk factors compared to CRM. Conclusions: This study presents the CRM nomogram to predict DSS in patients aged > 65 years with nonmetastatic cervical cancer. It can help clinicians implement more accurate personalized diagnostic and treatment modalities for elderly patients with cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study

Jiao

Guo

et al. 2023

Technol Cancer Res Treat

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“…Surgery and platinum-based chemotherapy are the main treatments for ovarian cancer, and 70% of ovarian cancer patients present with metastases at the time of diagnosis, 80-90% of whom have missed surgery [107]. Therefore, cisplatin (DDP)-based chemotherapy is an important approach for the treatment of ovarian cancer.…”

Section: Response To Chemotherapymentioning

confidence: 99%

The emerging roles of miRNA-mediated autophagy in ovarian cancer

Ding,

Huang,

et al. 2024

Cell Death Dis

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Ovarian cancer is one of the common tumors of the female reproductive organs. It has a high mortality rate, is highly heterogeneous, and early detection and primary prevention are very complex. Autophagy is a cellular process in which cytoplasmic substrates are targeted for degradation in lysosomes through membrane structures called autophagosomes. The periodic elimination of damaged, aged, and redundant cellular molecules or organelles through the sequential translation between amino acids and proteins by two biological processes, protein synthesis, and autophagic protein degradation, helps maintain cellular homeostasis. A growing number of studies have found that autophagy plays a key regulatory role in ovarian cancer. Interestingly, microRNAs regulate gene expression at the posttranscriptional level and thus can regulate the development and progression of ovarian cancer through the regulation of autophagy in ovarian cancer. Certain miRNAs have recently emerged as important regulators of autophagy-related gene expression in cancer cells. Moreover, miRNA analysis studies have now identified a sea of aberrantly expressed miRNAs in ovarian cancer tissues that can affect autophagy in ovarian cancer cells. In addition, miRNAs in plasma and stromal cells in tumor patients can affect the expression of autophagy-related genes and can be used as biomarkers of ovarian cancer progression. This review focuses on the potential significance of miRNA-regulated autophagy in the diagnosis and treatment of ovarian cancer.

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“…The amount of residual disease following debulking surgery is a significant prognostic indicator for patients [ 8 ]. One study based on Surveillance, Epidemiology, and End Results (SEER) database showed that radiotherapy may be associated with a poorer prognosis in patients with primary ovarian cancer compared to those who do not receive radiation therapy [ 9 ]. In the study of Teckie et al, radiotherapy was considered an efficacious treatment for brain metastases of epithelial ovarian cancer [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of different treatment modalities on ovarian cancer patients with liver metastases: A retrospective cohort study based on SEER

Li,

Jin,

et al. 2024

PLoS ONE

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Background To examine the trends in morbidity and mortality among ovarian cancer patients with liver metastases, and investigate the impact of different treatments on both overall survival (OS) and cancer-specific survival (CSS). Methods 2,925 ovarian cancer patients with liver metastases from Surveillance, Epidemiology, and End Results 2010–2019 were included. The primary endpoint was considered as OS and CSS. We conducted trend analysis of the incidence, OS and CSS rates of liver metastases in ovarian cancer. Univariate and multivariate COX proportional risk models were used to investigate the association between different treatment methods and OS, and univariate and multivariate competing risk models were employed to evaluate the impact of treatment methods on CSS. Results At the end of follow-up, 689 patients remained alive. The OS and CSS rates were 76.44% and 72.99% for all patients, respectively. There was a significant decreasing trend in the incidence [average annual percent change (AAPC) = -2.3, 95% confidence interval (CI): -3.9, -0.7], all-cause mortality (AAPC = -12.8, 95% CI: -15.6, -9.9) and specific mortality (AAPC = -13.0, 95% CI: -16.1, -9.8) rate of liver metastases in ovarian cancer. After adjusting all confounding factor, only receiving surgery was associated with OS [hazard ratio (HR) = 0.39, 95%CI: 0.31–0.48]/CSS (HR = 0.37, 95%CI: 0.30–0.47). Chemotherapy was found to be protective factor for OS (HR = 0.33, 95%CI: 0.30–0.37)/CSS (HR = 0.44, 95%CI: 0.39–0.50) of ovarian cancer patients, while not receiving surgery remained a risk factor. Additionally, the result of subgroup analyses also showed that only receiving surgery and chemotherapy still were significant protective factor of OS and CSS for patients without other distant metastases, with distant metastases to the bone, lung, brain or other organs, with bone metastasis, and with lung metastasis. Conclusion Our research has elucidated a downward trend in morbidity and mortality rates among patients with liver metastases originating from ovarian cancer. Only receiving surgery and chemotherapy as therapies methods confer survival benefits to patients.

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Survival Analysis of Radiation Therapy in Ovarian Cancer: A SEER Database Analysis

Cited by 5 publications

References 47 publications

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study

The emerging roles of miRNA-mediated autophagy in ovarian cancer

Effect of different treatment modalities on ovarian cancer patients with liver metastases: A retrospective cohort study based on SEER

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