Socioeconomic disparities in endometrial cancer survival in Germany: a survival analysis using population-based cancer registry data

Assessment of Five-Year Relative Survival of Patients With Endometrial Cancer: A Period Analysis

Background

Endometrial cancer is one of the most common female cancers globally and in China. Although timely assessment of 5-year relative survival is crucial for guiding secondary prevention and early screening programs for endometrial cancer patients, those kinds of data are scarce in China. We aimed to provide a timely and accurate assessment of 5-year relative survival for patients with endometrial cancer from eastern China.

Methods

Overall, 945 patients diagnosed with endometrial cancer during 2004 - 2018 from four cancer registries with high-quality data from Taizhou, eastern China were included. Period analysis was used to calculate 5-year relative survival for overall and the stratification by age at diagnosis and region. Model-based period analysis was used to predict the 5-year relative survival for the upcoming period of 2019 - 2023.

Results

We found that 5-year relative survival during 2014 - 2018 reached 86.4% for overall, while urban areas had higher survival compared to rural areas (91.3% vs. 85.3%). Furthermore, there was a clear age gradient, decreasing from 89.3% for age < 55 years to 80.5% for age > 74 years. Predicted 5-year relative survival for the upcoming period 2019 - 2023 could reach 88.4%.

Conclusions

We provide, a timely and accurate assessment of 5-year relative survival for patients with endometrial cancer from Taizhou, eastern China, reaching 86.4% for overall. Our finding has important implications for the overall evaluation of early detection and screening programs for patients with endometrial cancer in eastern China.

Population based study on the progress in survival of primarily metastatic lung cancer patients in Germany

Lung cancer is known for its high mortality; many patients already present with metastases at the time of diagnosis. The aim of this study is to assess the impact of new treatment strategies on the survival of primarily metastatic lung cancer patients and to analyze the differences in outcomes between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) patients. Population-based data, provided by the Robert-Koch Institute in Germany, was used and patients diagnosed between 2007 and 2018 were included in the study. We differentiated between NSCLC and SCLC patients and analyzed the survival over time for both sexes separately, using the Kaplan-Meier method. To evaluate survival advantages, we calculated multivariable hazard ratios. In total, 127,723 patients were considered for the study. We observed a moderate increase in survival over time. All patients showed an increased survival rate when undergoing chemotherapy. Minimal to no increase in survival was shown in NSCLC patients when receiving radiotherapy, whereas SCLC patients' survival time did benefit from it. NSCLC patients receiving immunotherapy showed an increase in survival as well. It can be concluded that advancements in radiotherapy, the application of chemotherapy, and the introduction of immunotherapies lead to an increased survival time of both NSCLC and SCLC primarily metastatic lung cancer patients.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BACKGROUND

Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis.

METHODS

Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.

RESULTS

Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.

CONCLUSIONS

The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Clinical characteristics of sarcoma patients: a population-based data analysis from a German clinical cancer registry

PURPOSE

Sarcomas are a heterogeneous group of malignant neoplasms with a wide range of histological types and occur in almost any anatomic site and side. This study evaluated the prognostic factors in sarcoma patients based on German clinical cancer registry data.

METHODS

The German clinical cancer register of Saxony-Anhalt was used for all data analyses. Sarcoma cases of all clinical or pathological T-stages (T1a-T4c), all N-stages (N0-3) and M-stages (0-1b) corresponding to the Union for International Cancer Control (UICC) stages I to IVB were considered. In our analyses, 787 cases diagnosed between 2005 and 2022 were included. Further, we assessed the association of cancer-related parameters with mortality and hazard ratios (HR) from the Cox proportional hazard models. We included sex, age at diagnosis, histological grade, T-, N- and M-stages, tumor size, tumor localization and tumor side as parameters in our regression models.

RESULTS

The majority of sarcoma patients were diagnosed with leiomyosarcoma (12%), liposarcoma (11%), angiosarcoma (5.3%) and myxofibrosarcoma (2.7%). In our univariate regression models, tumors localized in more than one location, head, face and neck region as well as the pelvis and lower extremity were associated with increased mortality risk (more than one location: HR 7.10, 95% CI 2.20-22.9; head, face and neck: HR 1.35, 95% CI 0.89-2.06; pelvis: HR 1.27, 95% CI 0.86-1.89; lower extremity: HR 1.44, 95% CI 1.05-1.96). Higher histological grades, UICC-grades and TNM-stages were related to a higher mortality risk. Differing histological subtypes had significant influence on overall survival and progression-free survival. Patients diagnosed with fibromyxoid sarcoma, rhabdomyosarcoma and angiosarcoma were related to higher mortality risk compared to other histological subtypes (fibromyxoid sarcoma: HR 5.2, 95% CI 0.71-38.1; rhabdomyosarcoma: HR 2.93, 95% CI 1.44-6.00; angiosarcoma: HR 1.07, 95% CI 0.53-2.18).

CONCLUSIONS

Histological grade, tumor size, nodal and distant metastasis, tumor localization and histological subtype were determined as prognostic factors in terms of survival.

Current and Emerging Prognostic Biomarkers in Endometrial Cancer

Endometrial cancer is the most common gynaecological malignancy in high income countries and its incidence is rising. Whilst most women with endometrial cancer are diagnosed with highly curable disease and have good outcomes, a significant minority present with adverse clinico-pathological characteristics that herald a poor prognosis. Prognostic biomarkers that reliably select those at greatest risk of disease recurrence and death can guide management strategies to ensure that patients receive appropriate evidence-based and personalised care. The Cancer Genome Atlas substantially advanced our understanding of the molecular diversity of endometrial cancer and informed the development of simplified, pragmatic and cost-effective classifiers with prognostic implications and potential for clinical translation. Several blood-based biomarkers including proteins, metabolites, circulating tumour cells, circulating tumour DNA and inflammatory parameters have also shown promise for endometrial cancer risk assessment. This review provides an update on the established and emerging prognostic biomarkers in endometrial cancer.