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Simple Summary: Personalized breast cancer screening has the potential to improve the accuracy and effectiveness of breast cancer screening by tailoring the screening protocol to an individual's risk factors. Currently, most women undergo mammograms at regular intervals based on age and family history, regardless of their risk factors. We examined the applicability of the Gail model, a well-known breast cancer prediction tool including several risk factors, in an Asian population. While the tool predicts the risks of developing breast cancer in the next 2, 5, 10, and 15 years to some extent, the age at which the disease occurs cannot be estimated. In addition, the tool performs better for longer prediction horizons (10 or 15 years), limiting its utility for risk prediction for two-year screening intervals. Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580–0.628). Calibration was better for longer-term prediction horizons (E/Olong-term ranges: 0.86–1.71; E/Oshort-term ranges:1.24–3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval. [ABSTRACT FROM AUTHOR]

Simple Summary: We aimed to systematically review current literature on the risk of developing vulvar squamous cell carcinoma in patients with differentiated vulvar intraepithelial neoplasia. Amongst clinicians, this latter entity is known as a rare premalignant vulvar lesion, which is often found adjacent to vulvar cancer. Knowledge on the exact cancer risk in differentiated vulvar intraepithelial neoplasia can help guide clinicians in the treatment and surveillance of these patients. This review confirms that patients with differentiated vulvar intraepithelial neoplasia have a high risk of developing vulvar squamous cell carcinoma and that cancer progression can occur rapidly. The results of this study highlight the importance of clinical awareness and prompt identification of differentiated vulvar intraepithelial neoplasia, given its high malignant potential. Therefore, further research on this disease should be encouraged. Differentiated vulvar intraepithelial neoplasia (dVIN) is the precursor of human papillomavirus (HPV)-independent vulvar squamous cell carcinoma (VSCC). Given the rare incidence of dVIN, limited information on the exact cancer risk is available. We systematically reviewed the primary and recurrent VSCC risk in patients with dVIN, as well as the time to cancer development. A systematic search was performed up to July 2021 according to the PRISMA guidelines. Five reviewers independently screened articles on title, abstract and full text, followed by critical appraisal of selected articles using the Quality in Prognostic Studies (QUIPS) tool. Of the 455 screened articles, 7 were included for analysis. The absolute risk for primary VSCC in dVIN varied between 33 and 86%, with a median time to progression to VSCC of 9–23 months. The risk of developing recurrent VSCC in dVIN associated VSCC was 32–94%, with a median time to recurrence of 13–32 months. In conclusion, patients with dVIN have a high risk of developing primary and recurrent VSCC with a short time to cancer progression. Increased awareness, timely recognition, aggressive treatment and close follow-up of HPV-independent vulvar conditions including dVIN is therefore strongly recommended. [ABSTRACT FROM AUTHOR]

In risk-stratified cancer screening, multiple risk factors are incorporated into the risk assessment. An individual's estimated absolute cancer risk is linked to risk categories with tailored screening recommendations for each risk category. Absolute risk, expressed as either remaining lifetime risk or shorter-term (five- or ten-year) risk, is estimated from the age at assessment. These risk estimates vary by age; however, some clinical guidelines (e.g., enhanced breast cancer surveillance guidelines) and ongoing personalised breast screening trials, stratify women based on absolute risk thresholds that do not vary by age. We examine an alternative approach in which the risk thresholds used for risk stratification vary by age and consider the implications of using age-independent risk thresholds on risk stratification. We demonstrate that using an age-independent remaining lifetime risk threshold approach could identify high-risk younger women but would miss high-risk older women, whereas an age-independent 5-year or 10-year absolute risk threshold could miss high-risk younger women and classify lower-risk older women as high risk. With risk misclassification, women with an equivalent risk level would be offered a different screening plan. To mitigate these problems, age-dependent absolute risk thresholds should be used to inform risk stratification. [ABSTRACT FROM AUTHOR]

This study derived a set of Australian low-risk gambling limits and explored the relative and absolute risk associated with exceeding these limits. Secondary analysis of population-representative Tasmanian and Australian Capital Territory (ACT) cross-sectional (11,597 respondents) and longitudinal studies (2027 respondents) was conducted. Balancing sensitivity and specificity, the limits were: gambling frequency of 20–30 times per year; gambling expenditure of AUD $380–$615 per year (USD $240–$388 per year); gambling expenditure comprising 0.83–1.68% of gross personal income; and two types of gambling activities per year. All limits, except number of activities, predicted subsequent harm, with limits related to gambling expenditure consistently the best-performing. Exceeding the limits generally conferred a higher degree of relative and absolute risk, with gamblers exceeding the limits being 3–20 times more likely to experience harm than those who do not, and having a 5–17% risk of experiencing harm. Only 7–12% of gamblers exceeding the limits actually experienced harm. Gambling consumption lower than the limits also conferred a considerable amount of harm. Using a relative risk method, this study derived similar limits from disparate Australian states and territories. These limits can serve as working guidelines for the consideration of researchers, clinicians, and policy makers, but need to be subject to further rigorous empirical investigation. [ABSTRACT FROM AUTHOR]

In the competing risks frame, the cause-specific hazard model (CSHM) can be used to test the effects of some covariates on one particular cause of failure. Sometimes, however, the observed covariates cannot explain the large proportion of variation in the time-to-event data coming from different areas such as in a multi-center clinical trial or a multi-center cohort study. In this study, a multi-center competing risks model (MCCRM) is proposed to deal with multi-center survival data, then this model is compared with the CSHM by simulation. A center parameter is set in the MCCRM to solve the spatial heterogeneity problem caused by the latent factors, hence eliminating the need to develop different models for each area. Additionally, the effects of the exposure factors in the MCCRM are kept consistent for each individual, regardless of the area they inhabit. Therefore, the coefficient of the MCCRM model can be easily explained using the scenario of each model for each area. Moreover, the calculating approach of the absolute risk is given. Based on a simulation study, we show that the estimate of coefficients of the MCCRM is unbiased and precise, and the area under the curve (AUC) is larger than that of the CSHM when the heterogeneity cannot be ignored. Furthermore, the disparity of the AUC increases progressively as the standard deviation of the center parameter (SDCP) rises. In order to test the calibration, the expected number (E) of strokes is calculated and then compared with the corresponding observed number (O). The result is promising, so the SDCP can be used to select the most appropriate model. When the SDCP is less than 0.1, the performance of the MCCRM and CSHM is analogous, but when the SDCP is equal to or greater than 0.1, the performance of the MCCRM is significantly superior to the CSHM. This suggests that the MCCRM should be selected as the appropriate model.