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Background: Diabetic retinopathy (DR) is one of the most common serious complications in diabetic patients, and early screening and diagnosis are essential to prevent visual impairment. With the rapid development of deep learning technology, network models based on attention mechanisms have shown significant advantages in medical image analysis, which can improve the accuracy and efficiency of screening., Aim: To evaluate the efficacy of an attention mechanism-based deep learning network model in screening for DR in natural and diabetic populations, as well as in screening with unilateral and bilateral fundus photography., Methods: From January 2023 to June 2024, a stratified multistage cluster sampling method was adopted to select a representative sample of permanent residents aged 18-70 years from our hospital. A total of 948 fundus images from 474 participants were included in the "deep learning model" system for scoring. The fundus images were graded via the early treatment of DR [Early Treatment Diabetic Retinopathy Study (ETDRS)] scoring system as the gold standard for the diagnosis of DR. With "DR to be referred (ETDRS > 31)" as the reference variable, a receiver operating characteristic curve was drawn to evaluate the area under the curve (AUC), sensitivity and specificity of the "deep learning model" to determine the screening efficiency of the system., Results: For each subject, in the natural population, the AUC of using the "deep learning model system" to screen "DR-requiring referral" was 0.941, and the sensitivity and specificity were 98.15% and 90.08%, respectively. The sensitivity and specificity of two-directional fundus photography were 100% and 86.91%, respectively. In the diabetic population, the AUC, sensitivity and specificity were 0.901, 98.08% and 82.10%, respectively, when "wise eye sugar net" unilateral fundus photography was used to screen for "DR-requiring referrals"., Conclusion: In both the natural population and the diabetic population, the deep learning model system has shown high sensitivity and specificity and can be used as an auxiliary means of DR screening., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Abstract Background Women with dense breasts face a double risk for breast cancer; they are at a higher risk for development of breast cancer than those with less dense breasts, and there is a greater chance that mammography will miss detection of a cancer in dense breasts due to the masking effect of surrounding fibroglandular tissue. These women may be candidates for supplemental screening. In this study, a masking risk model that was previously developed is tested on a cohort of cancer-free women to assess potential efficiency of stratification. Methods Three masking risk models based on (1) BI-RADS density, (2) volumetric breast density (VBD), and (3) a combination of VBD and detectability were applied to stratify the mammograms of 1897 cancer-free women. The fraction of cancer-free women whose mammograms were deemed by the algorithm to be masked and who would be considered for supplemental imaging was computed as was the corresponding fraction in a screened population of interval (masked) cancers that would be potentially detected by supplemental imaging. Results Of the models tested, the combined VBD/detectability model offered the highest efficiency for stratification to supplemental imaging. It predicted that 725 supplemental screens would be performed per interval cancer potentially detected, at an operating point that allowed detection of 64% of the interval cancers. In comparison, stratification based on the upper two BI-RADS density categories required 1117 supplemental screenings per interval cancer detected to capture 64% of interval cancers. Conclusion The combined VBD/detectability models perform better than BI-RADS and offer a continuum of operating points, suggesting that this model may be effective in guiding a stratified screening environment.

• Optimal liver stiffness thresholds for community-based screening of at-risk patients are 9.1–9.5 kPa for fibrosis (stages ≥F2). • Transient elastography is a cost-effective intervention for identifying patients with liver fibrosis in primary care. • Between 2,500 to 6,500 PPP-adjusted euros are needed to gain an extra year of life, adjusted for quality of life. • The survival effect of screening is most pronounced for the identification of significant (≥F2) fibrosis. Non-alcoholic fatty liver disease and alcohol-related liver disease pose an important challenge to current clinical healthcare pathways because of the large number of at-risk patients. Therefore, we aimed to explore the cost-effectiveness of transient elastography (TE) as a screening method to detect liver fibrosis in a primary care pathway. Cost-effectiveness analysis was performed using real-life individual patient data from 6 independent prospective cohorts (5 from Europe and 1 from Asia). A diagnostic algorithm with conditional inference trees was developed to explore the relationships between liver stiffness, socio-demographics, comorbidities, and hepatic fibrosis, the latter assessed by fibrosis scores (FIB-4, NFS) and liver biopsies in a subset of 352 patients. We compared the incremental cost-effectiveness of a screening strategy against standard of care alongside the numbers needed to screen to diagnose a patient with fibrosis stage ≥F2. The data set encompassed 6,295 participants (mean age 55 ± 12 years, BMI 27 ± 5 kg/m2, liver stiffness 5.6 ± 5.0 kPa). A 9.1 kPa TE cut-off provided the best accuracy for the diagnosis of significant fibrosis (≥F2) in general population settings, whereas a threshold of 9.5 kPa was optimal for populations at-risk of alcohol-related liver disease. TE with the proposed cut-offs outperformed fibrosis scores in terms of accuracy. Screening with TE was cost-effective with mean incremental cost-effectiveness ratios ranging from 2,570 €/QALY (95% CI 2,456–2,683) for a population at-risk of alcohol-related liver disease (age ≥45 years) to 6,217 €/QALY (95% CI 5,832–6,601) in the general population. Overall, there was a 12% chance of TE screening being cost saving across countries and populations. Screening for liver fibrosis with TE in primary care is a cost-effective intervention for European and Asian populations and may even be cost saving. The lack of optimized public health screening strategies for the detection of liver fibrosis in adults without known liver disease presents a major healthcare challenge. Analyses from 6 independent international cohorts, with transient elastography measurements, show that a community-based risk-stratification strategy for alcohol-related and non-alcoholic fatty liver diseases is cost-effective and potentially cost saving for our healthcare systems, as it leads to earlier identification of patients. [ABSTRACT FROM AUTHOR]

Background: The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus.Methods: We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group.Results: A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography.Conclusion: Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: High mammographic density reduces the diagnostic accuracy of screening mammography due to masking of tumors, resulting in possible delayed diagnosis and missed cancers. Women with high masking risk could be preselected for alternative screening regimens less susceptible to masking. In this study, various models to predict masking status are presented based on biometric and image-based parameters.Materials and Methods: For a cohort of 67 nonscreen-detected (cancers detected via other means after a negative mammogram) and 147 screen-detected invasive cancers, quantitative volumetric breast density, BI-RADS density, and the distribution and appearance of dense tissue through statistical and texture metrics were measured. Age and Body Mass Index were recorded. Stepwise multivariate logistic regressions were computed to select those parameters that predicted nonscreen-detected cancers. Accuracy of the models was evaluated using the area under receiver operator characteristic curve (AUC).Results: Using BI-RADS density alone to predict masking risk yielded an AUC of 0.64 (95% confidence interval [0.57-0.70]). Age-adjusted BI-RADS density or volumetric breast density had AUCs of 0.72 [0.64-0.79] and 0.71 [0.62-0.78], respectively. A model extracted from the full pool of variables had an AUC of 0.75 [0.67-0.82].Conclusion: The optimal model predicts masking more accurately than density alone, suggesting that texture metrics may be useful in models to guide a stratified screening strategy. [ABSTRACT FROM AUTHOR]

This study proposes a method to optimize the performance of BreastScreen Norway through a stratified recommendation of tests including independent double or single reading of the screening mammograms and additional imaging with or without core needle biopsy. This is carefully evaluated by a value of information analysis. An estimated graphical probabilistic model describing the relationship between a set of risk factors and the corresponding risk of breast cancer is used for this analysis, together with a Bayesian network modeling screening test results conditional on the true (but unknown) breast cancer status of a woman. This study contributes towards evaluating a possibility of improving the efficiency of the screening program, where all women aged 50 to 69 are invited every second year, regardless of individual risk factors. Our stratified recommendation of tests is dependent on the probability that an asymptomatic woman has developed breast cancer at the time she is invited to a screening. [ABSTRACT FROM AUTHOR]

Purpose To explore public attitudes towards modifying frequency of mammography screening based on genetic risk. Methods Home-based interviews were carried out with a population-based sample of 942 women aged 18–74 years in the UK. Demographic characteristics and perceived breast cancer (BC) risk were examined as predictors of support for risk-stratified BC screening and of the acceptability of raised or lowered screening frequency based on genetic risk, using multivariate logistic regression. Results Over two-thirds of respondents (65.8%) supported the idea of varying screening frequency on the basis of genetic risk. The majority (85.4%) were willing to have more frequent breast screening if they were found to be at higher risk, but fewer (58.8%) were willing to have less frequent screening if at lower risk (t (956) = 15.6, p < 0.001). Ethnic minority status was associated with less acceptability of more frequent screening (OR = 0.40, 95% CI = 0.21–0.74), but there were no other significant demographic correlates. Higher perceived risk of BC was associated with greater acceptability of more frequent screening (OR = 1.71, 95%CI = 1.27–2.30). Conclusion Women were positive about adjusting the frequency of mammography screening in line with personal genetic risk, but it will be important to develop effective communication materials to minimise resistance to reducing screening frequency for those at lower genetic risk. [ABSTRACT FROM AUTHOR]

Background: Women with dense breasts face a double risk for breast cancer; they are at a higher risk for development of breast cancer than those with less dense breasts, and there is a greater chance that mammography will miss detection of a cancer in dense breasts due to the masking effect of surrounding fibroglandular tissue. These women may be candidates for supplemental screening. In this study, a masking risk model that was previously developed is tested on a cohort of cancer-free women to assess potential efficiency of stratification.Methods: Three masking risk models based on (1) BI-RADS density, (2) volumetric breast density (VBD), and (3) a combination of VBD and detectability were applied to stratify the mammograms of 1897 cancer-free women. The fraction of cancer-free women whose mammograms were deemed by the algorithm to be masked and who would be considered for supplemental imaging was computed as was the corresponding fraction in a screened population of interval (masked) cancers that would be potentially detected by supplemental imaging.Results: Of the models tested, the combined VBD/detectability model offered the highest efficiency for stratification to supplemental imaging. It predicted that 725 supplemental screens would be performed per interval cancer potentially detected, at an operating point that allowed detection of 64% of the interval cancers. In comparison, stratification based on the upper two BI-RADS density categories required 1117 supplemental screenings per interval cancer detected to capture 64% of interval cancers.Conclusion: The combined VBD/detectability models perform better than BI-RADS and offer a continuum of operating points, suggesting that this model may be effective in guiding a stratified screening environment. [ABSTRACT FROM AUTHOR]