Preclinical studies have demonstrated that propranolol inhibits several pathways involved in breast cancer progression and metastasis. We investigated whether breast cancer patients who used propranolol, or other non-selective beta-blockers, had reduced breast cancer-specific or all-cause mortality in eight European cohorts. Incident breast cancer patients were identified from eight cancer registries and compiled through the European Cancer Pharmacoepidemiology Network. Propranolol and non-selective beta-blocker use was ascertained for each patient. Breast cancer-specific and all-cause mortality were available for five and eight cohorts, respectively. Cox regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (CIs) for cancer-specific and all-cause mortality by propranolol and non-selective beta-blocker use. HRs were pooled across cohorts using meta-analysis techniques. Dose–response analyses by number of prescriptions were also performed. Analyses were repeated investigating propranolol use before cancer diagnosis. The combined study population included 55,252 and 133,251 breast cancer patients in the analysis of breast cancer-specific and all-cause mortality respectively. Overall, there was no association between propranolol use after diagnosis of breast cancer and breast cancer-specific or all-cause mortality (fully adjusted HR = 0.94, 95% CI, 0.77, 1.16 and HR = 1.09, 95% CI, 0.93, 1.28, respectively). There was little evidence of a dose–response relationship. There was also no association between propranolol use before breast cancer diagnosis and breast cancer-specific or all-cause mortality (fully adjusted HR = 1.03, 95% CI, 0.86, 1.22 and HR = 1.02, 95% CI, 0.94, 1.10, respectively). Similar null associations were observed for non-selective beta-blockers. In this large pooled analysis of breast cancer patients, use of propranolol or non-selective beta-blockers was not associated with improved survival.
To assess the value of ovarian Histo-Scanning(™) , a novel computerized technique for interpreting ultrasound data, in combination with the risk of malignancy index (RMI) in improving triage for women with adnexal masses.RMI indices were assessed in 199 women enrolled in a prospective study to investigate the use of HistoScanning. Ultrasound scores were obtained by blinded analysis of archived images. The following sequential test was developed: HistoScanning was modeled as a second-line test for RMI between a lower cut-off and an upper cut-off. The optimal combination of these cut-offs that together maximized the Youden index (Sensitivity + Specificity - 1) was determined.Using RMI at the standard cut-off value of 250 resulted in a sensitivity of 74% and a specificity of 86%. When RMI was combined with HistoScanning, the highest accuracy was achieved by using HistoScanning as a sequential second-line test for patients with RMI values between 105 and 2100. At these cut-off values, sequential use of RMI and HistoScanning resulted in mean sensitivity and specificity estimates of 88% and 95%, respectively.Our data suggest that HistoScanning may have the potential to improve the diagnostic accuracy of RMI, which could result in better triage for women with adnexal masses. Further prospective validation is warranted.
About 1500 men and 1000 women are yearly diagnosed with rectal cancer in Belgium. Total mesorectal excision surgery is regarded as the cornerstone for rectal cancer treatment. In addition, (chemo)radiotherapy is often administered prior to surgery and adjuvant chemotherapy afterwards.
A small number of studies have investigated breast cancer (BC) risk among women with a history of false-positive recall (FPR) in BC screening, but none of them has used time-to-event analysis while at the same time quantifying the effect of false-negative diagnostic assessment (FNDA). FNDA occurs when screening detects BC, but this BC is missed on diagnostic assessment (DA). As a result of FNDA, screenings that detected cancer are incorrectly classified as FPR. Our study linked data recorded in the Flemish BC screening program (women aged 50-69 years) to data from the national cancer registry. We used Cox proportional hazards models on a retrospective cohort of 298 738 women to assess the association between FPR and subsequent BC, while adjusting for potential confounders. The mean follow-up was 6.9 years. Compared with women without recall, women with a history of FPR were at an increased risk of developing BC [hazard ratio=2.10 (95% confidence interval: 1.92-2.31)]. However, 22% of BC after FPR was due to FNDA. The hazard ratio dropped to 1.69 (95% confidence interval: 1.52-1.87) when FNDA was excluded. Women with FPR have a subsequently increased BC risk compared with women without recall. The risk is higher for women who have a FPR BI-RADS 4 or 5 compared with FPR BI-RADS 3. There is room for improvement of diagnostic assessment: 41% of the excess risk is explained by FNDA after baseline screening.
Cumulative relative survival curves for many cancers reach a plateau several years after diagnosis, indicating that the cancer survivor group has reached “statistical” cure. Parametric mixture cure model analysis on grouped relative survival curves provide an interesting way to determine the proportion of statistically cured cases and the mean survival time of the fatal cases in particular for population‐based cancer registries. Based on the relative survival data from the Belgian Cancer Registry, parametric cure models were applied to seven cancer sites (cervix, colon, corpus uteri, skin melanoma, pancreas, stomach and oesophagus), at the Flemish Regional level for the incidence period 1999–2011. Statistical cure was observed for the examined cancer sites except for oesophageal cancer. The estimated cured proportion ranged from 5.9% [5.7, 6.1] for pancreatic cancer to 80.8% [80.5, 81.2] for skin melanoma. Cure results were further stratified by gender or age group. Stratified cured proportions were higher for females compared to males in colon cancer, stomach cancer, pancreas cancer and skin melanoma, which can mainly be attributed to differences in stage and age distribution between both sexes. This study demonstrates the applicability of cure rate models for the selected cancer sites after 14 years of follow‐up and presents the first population‐based results on the cure of cancer in Belgium.
The potential years of life lost (PYLL) observed in a cohort of cancer patients cannot be fully assigned to the cancer as both cancer-related and non-cancer-related deaths contribute. A method is presented to decompose the observed all-cause PYLL into cancer mortality and population background mortality fractions when cause of death information is not available. Furthermore, the association of cancer-specific PYLL with cancer-specific mortality and mean age at diagnosis is explored and the impact of the follow-up window length is examined. The framework of the actuarial relative survival and the Ederer II method is applied to estimate the population background mortality contribution, PYLL*. The fraction (PYLL−PYLL*)/PYLL is then attributed to the cancer. The method is applied to cancer incidence in Belgium 2004–2014, about 631 300 cancer patients. The cancer-specific PYLL divided by the number of cancer patients, mean PYLL, in the Belgian cancer population ranges from 0.4 years for prostate cancer to 15 years for tumours of the central nervous system. The cancer-specific mean PYLL increases with both increasing cancer-specific mortality and decreasing age at diagnosis. Longer follow-up periods yield larger cancer-specific mean PYLL until statistical cure of cancer is achieved. The mean PYLL results, obtained by dividing the PYLL by the number of cancer patients, are visualized in combination with cancer incidence and mean age and mean life expectancy at diagnosis, providing an elegant summary to rank and compare cancer sites in terms of incidence, relative survival and PYLL.
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