A case-control study to evaluate the impact of the breast screening programme on mortality in England

[Analysis of the effect of mammography allocation on women's health indicators]

The early detection of breast cancer enables more effective forms of treatment. However, widespread access to its main screening tool, mammography, remains a challenge for the Brazilian public health system. This study aimed to analyze the effect of allocating mammography equipment on women's health indicators. In 2013, of the 4,557 municipalities that lacked the equipment, 260 received it up to 2019. The main hypothesis of this study suggests that receiving the mammography device would show a heterogeneous effect between locations and that such receival would depend on observable (propensity score matching) and non-observable variables (fixed effects model). Results indicate that the Brazilian municipalities that had mammography equipment in use from 2014 onward increased their number of exams without short-term effects to diagnoses and deaths due to malignant breast neoplasia. In addition to equipment, a more complex structure involving other factors (such as access to consultations, qualified professionals, waiting time, etc.) is important to improve women's health indicators in the analyzed municipalities.

Educational Initiatives to Improve the Cancer-Related Disparities Facing Transgender and Gender Diverse (TGD) Individuals

Transgender and gender diverse (TGD) individuals face discrimination and experience disparate healthcare, and cancer care, in particular. Our team has developed four initiatives to start to mitigate the disparities facing TGD individuals, including (1) improving identification of TGD individuals with cancer in oncology clinics, (2) identifying rates and predictors of cancer screening among TGD individuals, (3) building a TGD patient-centric oncology clinic, and (4) developing prospective research that is dedicated to addressing the needs of TGD Individuals with cancer. Clinician-focused educational initiatives are integral aspects of this work to improve cancer care for TGD individuals.

Cancer literacy differences of basic knowledge, prevention, early detection, treatment and recovery: a cross-sectional study of urban and rural residents in Northeast China

Background

Cancer literacy as a potential health intervention tool directly impacted the success of cancer prevention and treatment initiatives. This study aimed to evaluate the cancer literacy in Northeast China, and explore the factors contributing to urban-rural disparities.

Methods

A cross-sectional survey was conducted in 14 cities across Liaoning Province, China, from August to October 2021, using the multistage probability proportional to size sampling (PPS) method. The survey comprised 4,325 participants aged 15-69 and encompassed 37 core knowledge-based questions spanning five dimensions. Associations between sociodemographic factors and the cancer literacy rate were evaluated using chi-square tests and multivariate logistic regression model.

Results

The overall cancer literacy rate was 66.9% (95% CI: 65.6-68.2%). In the primary indicators, cancer literacy were highest in treatment (75.8, 95% CI: 74.2-77.4%) and early detection (68.2, 95% CI: 66.8-69.6%), followed by basic knowledge (67.2, 95% CI: 65.8-68.6%), recovery (62.6, 95% CI: 60.7-64.5%) and prevention (59.7, 95% CI: 58.2-61.3%). Regarding secondary indicators, the awareness rates regarding cancer-related risk factors (54.7, 95% CI: 52.8-56.5%) and early diagnosis of cancer (54.6, 95% CI: 52.7-56.6%) were notably inadequate. Rural participates exhibited lower cancer literacy across all dimensions compared to urban. Multi-factor analysis showed that factors such as advanced age, limited education or low household income were barriers to health literacy in rural areas.

Conclusion

Strengthening awareness concerning prevention and early detection, particularly among key populations, and bridging the urban-rural cancer literacy gap are imperative steps toward achieving the Healthy China 2030 target.

Years of life lost due to cancer in the United Kingdom from 1988 to 2017

BACKGROUND

We investigated the application of years of life lost (YLL) in routine cancer statistics using cancer mortality data from 1988 to 2017.

METHODS

Cancer mortality data for 17 cancers and all cancers in the UK from 1988 to 2017 were provided by the UK Association of Cancer Registries by sex, 5-year age group, and year. YLL, age-standardised YLL rate (ASYR) and age-standardised mortality rate (ASMR) were estimated.

RESULTS

The annual average YLL due to cancer, in the time periods 1988-1992 and 2013-2017, were about 2.2 and 2.3 million years, corresponding to 4510 and 3823 ASYR per 100,000 years, respectively. During 2013-2017, the largest number of YLL occurred in lung, bowel and breast cancer. YLL by age groups for all cancers showed a peak between 60-64 and 75-79. The relative contributions to incidence, mortality, and YLL differ between cancers. For instance, pancreas (in women and men) made up a smaller proportion of incidence (3%) but bigger proportion of mortality (6 and 5%) and YLL (5 and 6%), whereas prostate cancer (26% of incidence) contributed 13% mortality and 9% YLL.

CONCLUSION

YLL is a useful measure of the impact different cancers have on society and puts a higher weight on cancer deaths in younger individuals.

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.

Epidemiology of Breast Cancer Mortality in Kazakhstan, trends and Geographic Distribution

BACKGROUND

According to the International Agency for Research on Cancer, ongoing demographic changes will lead to an increase in the number of deaths from breast cancer (BC) per year in the vast majority of regions. In 2040 it is expected that 1.04 million people worldwide will die from this malignancy, including 2,380 women in Kazakhstan.

METHODS

The retrospective study (2009-2018) was done using descriptive and analytical methods of oncoepidemiology. The extensive, crude and age-specific incidence rates are determined according to the generally accepted methodology used in sanitary statistics. The data were used to calculate the average percentage change (APС) using the Joinpoint regression analysis to determine the trend over the study period.

RESULTS

During 10 years 12,958 women died from BC. An average age of the death was 61.6 years (95%CI=60.6-62.6) and tended to increase (APC=+0.6%, R2=0.6117). Age-specific rates had a bimodal increase with peak rates at 70-74 years - 76.7±5.5 (APC=+3.4%, R2=0.2656) and 80-84 years - 78.0±9.1 (APC=+3.7%, R2=0.0875). The age-standardized rate was 13.9 per 100,000 of female population, and the trend has decreased. When compiling thematic maps, mortality rates were determined on the basis of standardized indicators: low - up to 12.5, average - from 12.5 to 15.2, high - above 15.2 per 100,000. The results of the spatial analysis showed the regions with a higher levels of BC mortality rate per 100,000: Pavlodar (16.9), Almaty (19.2) and Astana cities (19.3).

CONCLUSIONS

Age-standardized mortality rates had a strong downward trend (APC=-4.0%, R2=0.9218). The decrease mostly is due to a large coverage of the population by mammography screening and to an improvement in the effectiveness of breast cancer treatment.

Evaluating cancer screening programs using survival analysis

The main purpose of cancer screening programs is to provide early treatment to patients that are diagnosed with cancer on a screening test, thus increasing their chances of survival. To test this hypothesis directly, one should compare the survival of screen-detected cases to the survival of their counterparts not included to the program. In this study, we develop a general notation and use it to formally define the comparison of interest. We explain why the naive comparison between screen-detected and interval cases is biased and show that the total bias that arises in this case can be decomposed as a sum of lead time bias, length time bias, and bias due to overdetection. With respect to the estimation, we show what can be estimated using existing methods. To fill in the missing gap, we develop a new nonparametric estimator that allows us to estimate the survival of the control group, that is, the survival of cancer cases that would be screen-detected among those not included to the program. By joining the proposed estimator with existing methods, we show that the contrast of interest can be estimated without neglecting any of the biases. Our approach is illustrated using simulations and empirical data.

Evaluation of an AI Model to Assess Future Breast Cancer Risk

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66 Collapse

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MESH Headings

• Humans
• Female
• Middle Aged
• Breast Neoplasms/diagnostic imaging
• Carcinoma, Intraductal, Noninfiltrating
• Artificial Intelligence
• Case-Control Studies
• Retrospective Studies
• State Medicine

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Grants

• 28689 Cancer Research UK
• C49757/A28689 Cancer Research UK

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Affiliation(s)

Celeste Damiani From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Grigorios Kalliatakis From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Muthyala Sreenivas From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Miaad Al-Attar From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Janice Rose From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Clare Pudney From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Emily F Lane From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Jack Cuzick From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Giovanni Montana From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Adam R Brentnall From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)

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Overdiagnosis Due to Screening Mammography for Breast Cancer among Women Aged 40 Years and Over: A Systematic Review and Meta-Analysis

The current systematic review and meta-analysis was conducted to estimate the incidence of overdiagnosis due to screening mammography for breast cancer among women aged 40 years and older. A PRISMA systematic search appraisal and meta-analysis were conducted. A systematic literature search of English publications in PubMed, Web of Science, EMBASE, Scopus, and Google Scholar was conducted without regard to the region or time period. Generic, methodological, and statistical data were extracted from the eligible studies. A meta-analysis was completed by utilizing comprehensive meta-analysis software. The effect size estimates were calculated using the fail-safe N test. The funnel plot and the Begg and Mazumdar rank correlation tests were employed to find any potential bias among the included articles. The strength of the association between two variables was assessed using Kendall’s tau. Heterogeneity was measured using the I-squared (I2) test. The literature search in the five databases yielded a total of 4214 studies. Of those, 30 articles were included in the final analysis, with sample sizes ranging from 451 to 1,429,890 women. The vast majority of the articles were retrospective cohort designs (24 articles). The age of the recruited women ranged between 40 and 89 years old. The incidence of overdiagnosis due to screening mammography for breast cancer among women aged 40 years and older was 12.6%. There was high heterogeneity among the study articles (I2 = 99.993), and the pooled event rate was 0.126 (95% CI: 15 0.101–0.156). Despite the random-effects meta-analysis showing a high degree of heterogeneity among the articles, the screening tests have to allow for a certain degree of overdiagnosis (12.6%) due to screening mammography for breast cancer among women aged 40 years and older. Furthermore, efforts should be directed toward controlling and minimizing the harmful consequences associated with breast cancer screening.

A Clinical Prediction Model for Breast Cancer in Women Having Their First Mammogram

Background: Digital mammography is the most efficient screening and diagnostic modality for breast cancer (BC). However, the technology is not widely available in rural areas. This study aimed to construct a prediction model for BC in women scheduled for their first mammography at a breast center to prioritize patients on waiting lists. Methods: This retrospective cohort study analyzed breast clinic data from January 2013 to December 2017. Clinical parameters that were significantly associated with a BC diagnosis were used to construct predictive models using stepwise multiple logistic regression. The models’ discriminative capabilities were compared using receiver operating characteristic curves (AUCs). Results: Data from 822 women were selected for analysis using an inverse probability weighting method. Significant risk factors were age, body mass index (BMI), family history of BC, and indicated symptoms (mass and/or nipple discharge). When these factors were used to construct a model, the model performance according to the Akaike criterion was 1387.9, and the AUC was 0.82 (95% confidence interval: 0.76–0.87). Conclusion: In a resource-limited setting, the priority for a first mammogram should be patients with mass and/or nipple discharge, asymptomatic patients who are older or have high BMI, and women with a family history of BC.

A consensus approach: Understanding the support needs of women in Newport West, Wales, to participate in breast screening

INTRODUCTION

Breast screening is an effective way to improve the early detection of breast cancer and reduce mortality. Unfortunately, low uptake of screening is often reported. This study aimed to explore the support needs of women residing in Newport West, Wales, to participate in breast screening.

METHODS

Group Concept Mapping, a structured participatory consensus approach, was used as the method. Participants completed three activities either online or offline: brainstorming to generate statements, sorting statements into themed categories; rating statements for perceived importance and accessibility (easy to get).

RESULTS

Thirty-seven participants from seven ethnic groups took part. Sixty-three statements (items of support) were generated and sorted into seven conceptually similar clusters (themes) (Trusting that I will be respected; Reassurance about my experience; Accessibility and convenience; Practical support; Addressing cultural diversity; Information tailored to individual needs; Raising awareness and understanding of breast screening). The 'Trusting that I will be respected' cluster was rated most important, while the 'Practical support' cluster was rated least accessible. Some disparity between responses was found based on ethnicity, language, disability and previous attendance of breast screening.

CONCLUSIONS

Women require a range of support to participate in breast screening. The results highlight the importance of ensuring women feel and are respected, instilling trust in the staff performing the screening, offering reassurance about positive experiences of breast screening and providing practical support, especially individualized/targeted support for people who do not speak and/or read English and those with a disability.

PATIENT OR PUBLIC CONTRIBUTION

The public contributed to the development of the information sheet, consent form, recruitment and data collection method.

Cultural, Religious and Socio-Ethical Misconceptions among Muslim Women towards Breast Cancer Screening: A Systematic Review

INTRODUCTION

Breast cancer is the most diagnosed cancer worldwide. With an estimated 685,000 deaths, female breast cancer was the fifth leading cause of cancer mortality worldwide, accounting for 6.9% of all cancer deaths. Previous studies have shown that late detection and delayed diagnosis are associated with advanced-stage breast cancer and poor survival. Factors contributing to non-adherence to breast cancer screening among women were elicited from previous studies. However, few studies have focused on the Muslim community, particularly Muslim women. As such, this systematic review aims to fill this gap by collecting information from studies conducted globally over the past ten years that examined cultural, religious and socio-ethical misconceptions about breast cancer screening among Muslim women.

METHODS

Following the PRISMA guidelines, literature searches were conducted systematically through various databases including PubMed, Science Direct, Scopus, Cochrane Library and Oxford Academic Journals. Article identification, screening steps and eligibility measures were meticulously performed throughout the review.

RESULTS

A total of 22 papers were appraised and included in this review. Five main themes were generated which were socio-ethical misconceptions, cultural and religious beliefs, cultural and religious barriers, stigmatization and fear of breast cancer impact. Eight sub-themes and 14 sub sub-themes were further elicited from the main themes.

CONCLUSION

Muslim women have socio-ethical, cultural and religious misconceptions on what constitutes health and practices as well as on the nature and etiology of BC. Cultural barriers and religious values of Muslim women were indicated to influence their health behaviors such as upholding their modesty when choosing health interventions. BC stigma and fear were also found to be key sources of psychological distress that discouraged Muslim women from undergoing BC screening. The study suggests the implementation of holistic effort in educating Muslim women to increase BC screening rate.

Cultural, Religious and Socio-Ethical Misconceptions among Muslim Women towards Breast Cancer Screening: A Systematic Review

INTRODUCTION

Breast cancer is the most diagnosed cancer worldwide. With an estimated 685,000 deaths, female breast cancer was the fifth leading cause of cancer mortality worldwide, accounting for 6.9% of all cancer deaths. Previous studies have shown that late detection and delayed diagnosis are associated with advanced-stage breast cancer and poor survival. Factors contributing to non-adherence to breast cancer screening among women were elicited from previous studies. However, few studies have focused on the Muslim community, particularly Muslim women. As such, this systematic review aims to fill this gap by collecting information from studies conducted globally over the past ten years that examined cultural, religious and socio-ethical misconceptions about breast cancer screening among Muslim women.

METHODS

Following the PRISMA guidelines, literature searches were conducted systematically through various databases including PubMed, Science Direct, Scopus, Cochrane Library and Oxford Academic Journals. Article identification, screening steps and eligibility measures were meticulously performed throughout the review.

RESULTS

A total of 22 papers were appraised and included in this review. Five main themes were generated which were socio-ethical misconceptions, cultural and religious beliefs, cultural and religious barriers, stigmatization and fear of breast cancer impact. Eight sub-themes and 14 sub sub-themes were further elicited from the main themes.

CONCLUSION

Muslim women have socio-ethical, cultural and religious misconceptions on what constitutes health and practices as well as on the nature and etiology of BC. Cultural barriers and religious values of Muslim women were indicated to influence their health behaviors such as upholding their modesty when choosing health interventions. BC stigma and fear were also found to be key sources of psychological distress that discouraged Muslim women from undergoing BC screening. The study suggests the implementation of holistic effort in educating Muslim women to increase BC screening rate.

Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening

Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297 days (standard deviation, SD, 169 days) in the clinical fit and 322 days (SD, 217 days) in the simulated cohort. The mean estimated TVDT was 340 days (SD, 287 days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( p > 0.5 ). No significant difference ( p > 0.05 ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.

The Role of Artificial Intelligence in Early Cancer Diagnosis

Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.

Breast Cancer Mortality in the Americas and Australasia over the Period 1980-2017 with Predictions for 2025

Simple Summary

Globally, breast cancer is the most common neoplasm and the leading cause of cancer death in women. It is also the common cancer for which the largest advancements have been made in terms of screening, early diagnosis, management and treatment over the last decades. These advances have had an impact on breast cancer mortality, which therefore depends on many aspects, including countries income and the health care system, leading to inequalities across the world. Breast cancer mortality has been substantially decreasing in high income countries of North America and Australia, but trends have been less consistent in Latin America and Asia, indicating the scope for further global advancemets in screening and management of breast cancer.

Abstract

Substantial progress has been made in the diagnosis, management, and treatment of breast cancer over the last decades. This has affected mortality rates but has also led to inequality in epidemiological trends between different regions of the world. We extracted death certification data for breast cancer from the World Health Organization database. We analyzed trends in breast cancer mortality in selected countries from America, Asia, and Oceania over the 1980–2017 period and predicted numbers of deaths and rates for 2025. In North America, we observed decreased breast cancer mortality, reaching a rate of about 13/100,000 women in 2017. In Latin American countries, breast cancer mortality rates did not consistently decrease. The highest decreases in mortality were observed in Australia. Mortality trends in Asian countries remained among the lowest globally. We have predicted decreased mortality from breast cancer in 2025 for most of the analyzed countries. The epidemiological situation regarding breast cancer mortality is expected to change in the coming years. Advancements in diagnosis and treatment of breast cancer must be extended in various areas of the world to obtain global control of breast cancer mortality.