Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies

Global, regional, and national burden of female cancers in women of child-bearing age, 1990-2021: analysis of data from the global burden of disease study 2021

Background

The global status of women's health is underestimated, particularly the burden on women of child-bearing age (WCBA). We aim to investigate the pattern and trend of female cancers among WCBA from 1990 to 2021.

Methods

We retrieved data from the Global Burden of Disease Study (GBD) 2021 on the incidence and disability-adjusted life-years (DALYs) of four major female cancers (breast, cervical, uterine, and ovarian cancer) among WCBA (15-49 years) in 204 countries and territories from 1990 to 2021. Estimated annual percentage changes (EAPC) in the age-standardised incidence and DALY rates of female cancers, by age and socio-demographic index (SDI), were calculated to quantify the temporal trends. Spearman correlation analysis was used to examine the correlation between age-standardised rates and SDI.

Findings

In 2021, an estimated 1,013,475 new cases of overall female cancers were reported globally, with a significant increase in age-standardised incidence rate (EAPC 0.16%), and a decrease in age-standardised DALY rate (-0.73%) from 1990 to 2021. Annual increase trends of age-standardised incidence rate were observed in all cancers, except for that in cervical cancer. Contrary, the age-standardised DALY rate decreased in all cancers. Breast and cervical cancers were prevalent among WCBA worldwide, followed by ovarian and uterine cancers, with regional disparities in the burden of four female cancers. In addition, the age-standardised incidence rates of breast, ovarian, and uterine cancers basically showed a consistent upward trend with increasing SDI, while both the age-standardised incidence and DALY rates in cervical cancer exhibited downward trends with SDI. Age-specific rates of female cancers increased with age in 2021, with the most significant changes observed in younger age groups, except for uterine cancer.

Interpretation

The rising global incidence of female cancers, coupled with regional variations in DALYs, underscores the urgent need for innovative prevention and healthcare strategies to mitigate the burden among WCBA worldwide.

Funding

This study was supported by the Science Foundation for Young Scholars of Sichuan Provincial People's Hospital (NO. 2022QN44 and NO. 2022QN18); the Key R&D Projects of Sichuan Provincial Department of Science and Technology (NO. 2023YFS0196); the National Natural Science Foundation of China (No. 82303701).

Estimating sojourn time and sensitivity of screening for ovarian cancer using a Bayesian framework

BACKGROUND

Ovarian cancer is among the leading causes of gynecologic cancer-related death. Past ovarian cancer screening trials using combination of cancer antigen 125 testing and transvaginal ultrasound failed to yield statistically significant mortality reduction. Estimates of ovarian cancer sojourn time-that is, the period from when the cancer is first screen detectable until clinical detection-may inform future screening programs.

METHODS

We modeled ovarian cancer progression as a continuous time Markov chain and estimated screening modality-specific sojourn time and sensitivity using a Bayesian approach. Model inputs were derived from the screening arms (multimodal and ultrasound) of the UK Collaborative Trial of Ovarian Cancer Screening and the Prostate, Lung, Colorectal and Ovarian cancer screening trials. We assessed the quality of our estimates by using the posterior predictive P value. We derived histology-specific sojourn times by adjusting the overall sojourn time based on the corresponding histology-specific survival from the Surveillance, Epidemiology, and End Results Program.

RESULTS

The overall ovarian cancer sojourn time was 2.1 years (posterior predictive P value = .469) in the Prostate, Lung, Colorectal and Ovarian studies, with 65.7% screening sensitivity. The sojourn time was 2.0 years (posterior predictive P value = .532) in the United Kingdom Collaborative Trial of Ovarian Cancer Screening's multimodal screening arm and 2.4 years (posterior predictive P value = .640) in the ultrasound screening arm, with sensitivities of 93.2% and 64.5%, respectively. Stage-specific screening sensitivities in the Prostate, Lung, Colorectal and Ovarian studies were 39.1% and 82.9% for early-stage and advanced-stage disease, respectively. The histology-specific sojourn times ranged from 0.8 to 1.8 years for type II ovarian cancer and 2.9 to 6.6 years for type I ovarian cancer.

CONCLUSIONS

Annual screening is not effective for all ovarian cancer subtypes. Screening sensitivity for early-stage ovarian cancers is not sufficient for substantial mortality reduction.

Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models

Objectives

Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform.

Materials and Methods

We refactored R-iCARE into a Python package (Py-iCARE) and then compiled it to WebAssembly (Wasm-iCARE)-a portable web module, which operates within the privacy of the user's device.

Results

We showcase the portability and privacy of Wasm-iCARE through 2 applications: for researchers to statistically validate risk models and to deliver them to end-users. Both applications run entirely on the client side, requiring no downloads or installations, and keep user data on-device during risk calculation.

Conclusions

Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.

Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer

We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n = 675), validation (n = 169), and testing (n = 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.

Predicting absolute risk for a person with missing risk factors

We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors.

Recent Advances in Endometrial Cancer Prevention, Early Diagnosis and Treatment

Endometrial cancer is the sixth commonest cancer in women worldwide, with over 417,000 diagnoses in 2020. The disease incidence has increased by 132% over the last 30 years and is set to continue to rise in response to an ageing population and increasing global rates of obesity and diabetes. A greater understanding of the mechanisms driving endometrial carcinogenesis has led to the identification of potential strategies for primary disease prevention, although prospective evaluation of their efficacy within clinical trials is still awaited. The early diagnosis of endometrial cancer is associated with improved survival, but has historically relied on invasive endometrial sampling. New, minimally invasive tests using protein and DNA biomarkers and cytology have the potential to transform diagnostic pathways and to allow for the surveillance of high-risk populations. The molecular classification of endometrial cancers has been shown to not only have a prognostic impact, but also to have therapeutic value and is increasingly used to guide adjuvant treatment decisions. Advanced and recurrent disease management has also been revolutionised by increasing the use of debulking surgery and targeted treatments, particularly immunotherapy. This review summarises the recent advances in the prevention, diagnosis and treatment of endometrial cancer and seeks to identify areas for future research.

Evidence-based identification of breast cancer and associated ovarian and uterus cancer risk components in source waters from high incidence area in the Pearl River Basin, China

Breast cancer, ovarian cancer, and uterus cancer are among the most common female cancers. They are suspected to associate with exposures to specific environmental pollutants, which remain unidentified in source waters. In this work, we focused on the Pearl River Basin region in China, which experienced a high incidence of breast, ovarian, and uterus cancers. Combining cancer patient data, mammalian cell cytotoxicity analyses, and exhaustive historical and current chemical assessments, we for the first time identified source water components that promoted proliferation of mammalian cells, and confirmed their association with these female cancers via the estrogen receptor mediated pathway. Therefore, the components that have previously been found to enhance the proliferation of estrogen receptor-containing cells through endocrine disruption could be the crucial factor. Based on this, components that matched with this toxicological characteristic (i.e., estrogen-like effect) were further identified in source waters, including (1) organic components: phthalates, bisphenol A, nonylphenols, and per-/polyfluoroalkyls; (2) inorganic components: Sb, Co, As, and nitrate. Moreover, these identified water components were present at levels comparable to other regions with high female cancer prevalence, suggesting that the potential risk of these components may not be exclusive to the study region. Together, multiple levels of evidence suggested that long-term co-exposures to source water estrogenic components may be important to the development of breast, ovarian, and uterus cancers.

A framework for assessing interactions for risk stratification models: the example of ovarian cancer

Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pairwise interactions between the risk/protective factors were also assessed. We found that menopausal status modifies the association among endometriosis, first-degree family history of ovarian cancer, breastfeeding, and depot-medroxyprogesterone acetate use and disease risk, highlighting the importance of understanding multiplicative interactions when developing risk prediction models.

Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models

Objective

Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, such as the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face serious limitations in portability and privacy due to their need for circulating user data in remote servers for operation. Our objective was to overcome these limitations.

Materials and Methods

We refactored R-iCARE into a Python package (Py-iCARE) then compiled it to WebAssembly (Wasm-iCARE): a portable web module, which operates entirely within the privacy of the user's device.

Results

We showcase the portability and privacy of Wasm-iCARE through two applications: for researchers to statistically validate risk models, and to deliver them to end-users. Both applications run entirely on the client-side, requiring no downloads or installations, and keeps user data on-device during risk calculation.

Conclusions

Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.

Association between wine consumption and cancer: a systematic review and meta-analysis

Background

Alcohol consumption is related to the risk of developing different types of cancer. However, unlike other alcoholic beverages, moderate wine drinking has demonstrated a protective effect on the risk of developing several types of cancer.

Objective

To analyze the association between wine consumption and the risk of developing cancer.

Methods

We searched the MEDLINE (through PubMed), Scopus, Cochrane, and Web of Science databases to conduct this systematic review and meta-analysis. Pooled relative risks (RRs) were calculated using the DerSimonian and Laird methods. I2 was used to evaluate inconsistency, the τ2 test was used to assess heterogeneity, and The Newcastle-Ottawa Quality Assessment Scale were applied to evaluate the risk of bias. This study was previously registered in PROSPERO, with the registration number CRD42022315864.

Results

Seventy-three studies were included in the systematic review, and 26 were included in the meta-analysis. The pooled RR for the effect of wine consumption on the risk of gynecological cancers was 1.03 (95% CI: 0.99, 1.08), that for colorectal cancer was 0.92 (95% CI: 0.82, 1.03), and that for renal cancer was 0.92 (95% CI: 0.81, 1.04). In general, the heterogeneity was substantial.

Conclusion

The study findings reveal no association between wine consumption and the risk of developing any type of cancer. Moreover, wine drinking demonstrated a protective trend regarding the risk of developing pancreatic, skin, lung, and brain cancer as well as cancer in general.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022315864, identifier CRD42022315864 (PROSPERO).

Epidemiology and Risk Factors for Breast Cancer: 21st Century Advances, Gaps to Address through Interdisciplinary Science

Research methods to study risk factors and prevention of breast cancer have evolved rapidly. We focus on advances from epidemiologic studies reported over the past two decades addressing scientific discoveries, as well as their clinical and public health translation for breast cancer risk reduction. In addition to reviewing methodology advances such as widespread assessment of mammographic density and Mendelian randomization, we summarize the recent evidence with a focus on the timing of exposure and windows of susceptibility. We summarize the implications of the new evidence for application in risk stratification models and clinical translation to focus prevention-maximizing benefits and minimizing harm. We conclude our review identifying research gaps. These include: pathways for the inverse association of vegetable intake and estrogen receptor (ER)-ve tumors, prepubertal and adolescent diet and risk, early life adiposity reducing lifelong risk, and gaps from changes in habits (e.g., vaping, binge drinking), and environmental exposures.

Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe

BACKGROUND

Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.

PURPOSE

To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC.

METHODS

A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots.

RESULTS

From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models.

CONCLUSIONS

Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.

Exposure to p-dichlorobenzene and prevalent endocrine-related reproductive cancers among US women

P-dichlorobenzene (p-DCB) is a pest repellent and air deodorant that is commonly found in the household and public buildings. Exposure to p-DCB has been suggested to have potential metabolic and endocrine effects. Little is known about its association with endocrine-related female cancers. In this cross-sectional study, a nationally representative subsample of 4459 women, aged 20 years or older, in the 2003-2016 National Health and Nutrition Examination Survey was analyzed for the association between p-DCB exposure, measured as urinary concentrations of 2,5-dichlorophenol (2,5-DCP), the primary metabolite of p-DCB, and prevalent endocrine-related female cancers (defined as breast, ovarian, and uterine cancers) using multivariate logistic regression models, adjusting for potential confounders. Of the study participants, 202 women (weighted prevalence, 4.20%) reported being diagnosed with any of these endocrine-related reproductive cancers. Women with reproductive cancers showed a statistically significant increase in urinary 2,5-DCP concentrations (weighted geometric mean, 7.97 vs. 5.84 µg/g creatinine; p < 0.0001), compared to women without these cancers. After adjusting for potential confounders, we found that women in the moderate (1.94- < 28.10 µg/g creatinine) and high level (≥ 28.10 µg/g creatinine) of 2,5-DCP had significantly increased odds of endocrine-related reproductive cancers (odds ratio of 1.66 (95% CI: 1.02, 2.71) and 1.89 (1.08, 3.29), respectively), as compared with those in the low exposure group (< 1.94 µg/g creatinine). This study demonstrates a potential relation between p-DCB exposure and prevalent endocrine-related reproductive cancers in US women. Prospective and mechanistic studies would further explore these interactions and elucidate the pathogenesis of endocrine-related female cancers potentially associated with p-DCB exposure.

Risk prediction models for endometrial cancer: development and validation in an international consortium

BACKGROUND

Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors.

METHODS

We developed endometrial cancer risk prediction models using data on postmenopausal White women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium (E2C2). Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in 3 cohorts: Nurses' Health Study (NHS), NHS II, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

RESULTS

Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59).

CONCLUSIONS

Using data from the largest, most heterogeneous study population to date (to our knowledge), prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.

Changes in metabolic syndrome and the risk of breast and endometrial cancer according to menopause in Korean women

OBJECTIVES

This study investigated how changes in metabolic syndrome (MetS) are associated with the subsequent risk of breast and endometrial cancer according to menopausal status.

METHODS

This cohort study, using data from the National Health Insurance Service database, included women aged ≥40 years who underwent 2 biennial cancer screenings (2009-2010 and 2011-2012) and were followed up until 2020. Participants were grouped into MetS-free, MetS-recovery, MetS-development, and MetS-persistent groups. Menopausal status (premenopausal, perimenopausal, and postmenopausal) was assessed at 2 screenings. Cox proportional hazard regression was used to assess the association between MetS changes and cancer risk.

RESULTS

In 3,031,980 women, breast and endometrial cancers were detected in 39,184 and 4,298, respectively. Compared with the MetS-free group, those who recovered, developed, or had persistent MetS showed an increased risk of breast cancer, with adjusted hazard ratios (aHRs) of 1.05, 1.05, and 1.11, respectively (p<0.005). MetS persistence was associated with an increased risk of breast cancer in postmenopausal women (aHR, 1.12, 95% confidence interval [CI], 1.08 to 1.16) but not in premenopausal or perimenopausal women. MetS persistence was associated with an increased risk of endometrial cancer in premenopausal, perimenopausal, and postmenopausal women, with aHRs of 1.41 (95% CI, 1.17 to 1.70), 1.59 (95% CI, 1.19 to 2.12), and 1.47 (95% CI, 1.32 to 1.63), respectively.

CONCLUSIONS

Increased breast cancer risk was associated with recovered, developed, and persistent MetS in postmenopausal women. Meanwhile, increased endometrial cancer risk was found in obese women who recovered from MetS or persistently had MetS, regardless of menopausal status, when compared to MetS-free women.

A flexible modeling approach for biomarker-based computation of absolute risk of Alzheimer's disease dementia

INTRODUCTION

As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia.

METHODS

We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis.

RESULTS

A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aβ)42/Aβ40 (AUC = 0.82).

DISCUSSION

An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.

Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression.

As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker.

The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation.

The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models.

Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies

BACKGROUND

Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear.

METHODS

Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided.

RESULTS

Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like.

CONCLUSIONS

This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.

Trends and predictors of hysterectomy prevalence among women in the United States

BACKGROUND

Hysterectomy is the most common nonobstetrical medical procedure performed in US women. Evaluating hysterectomy prevalence trends and determinants is important for estimating gynecologic cancer rates and management of uterine conditions.

OBJECTIVE

This study aimed to assess hysterectomy prevalence trends and determinants using the Behavioral Risk Factor Surveillance System (2006-2016).

STUDY DESIGN

We estimated crude hysterectomy prevalences and multivariable-adjusted odds ratios and 95% confidence intervals for associations of race or ethnicity, age group (5-year), body mass index (categorical), smoking status, education, insurance, income, and US region with hysterectomy. Missing data were imputed. The number of women in each survey year ranged from 220,302 in 2006 to 275,631 in 2016.

RESULTS

Although overall hysterectomy prevalence changed little between 2006 and 2016 (21.4% and 21.1%, respectively), hysterectomy prevalence was lower in 2016 than in 2006 among women aged ≥40 years, particularly among non-Hispanic Black and Hispanic women. Current smoking (odds ratio, 1.38; 95% confidence interval, 1.35-1.41), increasing age (odds ratio, 1.40; 95% confidence interval, 1.39-1.40), living in the South compared with the Midwest (odds ratio, 1.36; 95% confidence interval, 1.34-1.39), higher body mass index (odds ratio, 1.26; 95% confidence interval, 1.25-1.27), Black race compared with White (odds ratio, 1.10; 95% confidence interval, 1.07-1.13), and having insurance compared with being uninsured (odds ratio, 1.26; 95% confidence interval, 1.22-1.30) were most strongly associated with increased prevalence. Hispanic ethnicity and living in the Northeast were most strongly associated with decreased prevalence (odds ratio, 0.73; 95% confidence interval, 0.70-0.76; odds ratio, 0.67; 95% confidence interval, 0.65-0.69).

CONCLUSION

Nationwide hysterectomy prevalence decreased among women aged ≥40 years from 2006 to 2016, particularly among non-Hispanic Black and Hispanic women. Age, non-Hispanic Black race, having insurance, current smoking, and living in the South were associated with increased odds of hysterectomy, even after accounting for possible explanatory factors. Further research is needed to better understand associations of race and ethnicity and region with hysterectomy prevalence.

Development and evaluation of polygenic risk scores for prediction of endometrial cancer risk in European women

PURPOSE

Single-nucleotide variations (SNVs) (formerly single-nucleotide polymorphism [SNV]) influence genetic predisposition to endometrial cancer. We hypothesized that a polygenic risk score (PRS) comprising multiple SNVs may improve endometrial cancer risk prediction for targeted screening and prevention.

METHODS

We developed PRSs from SNVs identified from a systematic review of published studies and suggestive SNVs from the Endometrial Cancer Association Consortium. These were tested in an independent study of 555 surgically-confirmed endometrial cancer cases and 1202 geographically-matched controls from Manchester, United Kingdom and validated in 1676 cases and 116,960 controls from the UK Biobank (UKBB).

RESULTS

Age and body mass index predicted endometrial cancer in both data sets (Manchester: area under the receiver operator curve [AUC] = 0.77, 95% CI = 0.74-0.80; UKBB: AUC = 0.74, 95% CI = 0.73-0.75). The AUC for PRS19, PRS24, and PRS72 were 0.58, 0.55, and 0.57 in the Manchester study and 0.56, 0.54, and 0.54 in UKBB, respectively. For PRS19, women in the third tertile had a 2.1-fold increased risk of endometrial cancer compared with those in the first tertile of the Manchester study (odds ratio = 2.08, 95% CI = 1.61-2.68, P_trend = 5.75E-9). Combining PRS19 with age and body mass index improved discriminatory power (Manchester study: AUC = 0.79, 95% CI = 0.76-0.82; UKBB: AUC =0.75, 95% CI = 0.73-0.76).

CONCLUSION

An endometrial cancer risk prediction model incorporating a PRS derived from multiple SNVs may help stratify women for screening and prevention strategies.

Prediction time of breast cancer tumor recurrence using Machine Learning

An in-depth study using the database from GLOBOCAN, CDC, and WHO health repository highlights the lethality of breast cancer, taking thousands of lives each year. However, a timely prediction of cancer can help patients to consult the doctor on time. In the past, various studies have successfully predicted the nature of the tumor to be benign or malignant and if the breast cancer tumor will reoccur or not but, no time-based models have been studied. With the help of Machine Learning, this study shows various prediction models that can be used to predict tumor reoccurrence time as accurately as 1 year. Among the 198 patients analyzed, 40% of the total patients were predicted to have breast cancer tumors reoccurring within 1^st year of the diagnosis. The proposed machine learning techniques use various classification models such as Spectral clustering, DBSCAN, and k-means along with prediction models like Support Vector Machines (SVM), Decision trees, and Random Forest. The results demonstrate the ability of the model to predict the time taken by the tumor to reoccur or the time taken by the patient for full recovery with the best accuracy of 78.7% using SVM. This population-based study performed on multivariate real attributed characteristics data can therefore provide the patients a reasonable estimate about their recovery time or the time before which they should consult the doctor.

Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors

BACKGROUND

Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention.

METHODS

We developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in BRCA1, BRCA2, RAD51C, RAD51D and BRIP1, a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively.

RESULTS

Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%-10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk.

CONCLUSION

This multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

The association between body mass index and pathological complete response in neoadjuvant-treated breast cancer patients

BACKGROUND

Obesity seems to be associated with a poorer response to adjuvant chemotherapy in breast cancer (BC); however, associations in the neoadjuvant chemotherapy (NACT) setting and according to menopausal status are less studied. This study aims to investigate the association between pretreatment body mass index (BMI) and pathological complete response (pCR) following NACT in BC according to menopausal and estrogen receptor (ER) status.

MATERIAL AND METHODS

The study cohort consisted of 491 patients receiving NACT in 2005-2019. Based on pre-NACT patient and tumor characteristics, the association between BMI and achieving pCR was analyzed using logistic regression models (crude and adjusted models (age, tumor size, and node status)) with stratification by menopausal and ER status.

RESULTS

In the overall cohort, being overweight (BMI ≥25) compared by being normal-weight (BMI <25), increased the odds of accomplishing pCR by 15%. However, based on the 95% confidence interval (CI) the data were compatible with associations within the range of a decrease of 30% to an increase of 89%. Stratification according to menopausal status also showed no strong association: the odds ratio (OR) of accomplishing pCR in overweight premenopausal patients compared with normal-weight premenopausal patients was 1.76 (95% CI 0.88-3.55), whereas for postmenopausal patients the corresponding OR was 0.71 (95% CI 0.35-1.46).

DISCUSSION

In a NACT BC cohort of 491 patients, we found no evidence of high BMI as a predictive factor of accomplishing pCR, neither in the whole cohort nor stratified by menopausal status. Given the limited precision in our results, larger studies are needed before considering BMI in clinical decision-making regarding NACT or not.

Unselected Population Genetic Testing for Personalised Ovarian Cancer Risk Prediction: A Qualitative Study Using Semi-Structured Interviews

Unselected population-based personalised ovarian cancer (OC) risk assessments combining genetic, epidemiological and hormonal data have not previously been undertaken. We aimed to understand the attitudes, experiences and impact on the emotional well-being of women from the general population who underwent unselected population genetic testing (PGT) for personalised OC risk prediction and who received low-risk (<5% lifetime risk) results. This qualitative study was set within recruitment to a pilot PGT study using an OC risk tool and telephone helpline. OC-unaffected women ≥ 18 years and with no prior OC gene testing were ascertained through primary care in London. In-depth, semi-structured and 1:1 interviews were conducted until informational saturation was reached following nine interviews. Six interconnected themes emerged: health beliefs; decision making; factors influencing acceptability; effect on well-being; results communication; satisfaction. Satisfaction with testing was high and none expressed regret. All felt the telephone helpline was helpful and should remain optional. Delivery of low-risk results reduced anxiety. However, care must be taken to emphasise that low risk does not equal no risk. The main facilitators were ease of testing, learning about children’s risk and a desire to prevent disease. Barriers included change in family dynamics, insurance, stigmatisation and personality traits associated with stress/worry. PGT for personalised OC risk prediction in women in the general population had high acceptability/satisfaction and reduced anxiety in low-risk individuals. Facilitators/barriers observed were similar to those reported with genetic testing from high-risk cancer clinics and unselected PGT in the Jewish population.

A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women

Background

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this.

Methods

We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test.

Results

After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6).

Conclusion

In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk.

Impact

Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Risk of Gynecological Cancers in Cholecystectomized Women: A Large Nationwide Cohort Study

Background: Gallstones affect women more frequently than men, and symptomatic gallstones are increasingly treated with surgical removal of the gallbladder (cholecystectomy). Breast, endometrial, and ovarian cancer share several risk factors with gallstones, including overweight, obesity, and exposure to female sex hormones. We intended to assess the association between cholecystectomy and female cancer risk, which has not been comprehensively investigated. Methods: We investigated the risk of female cancers after cholecystectomy leveraging the Swedish Cancer, Population, Patient, and Death registries. Standardized incidence ratios (SIRs) adjusted for age, calendar period, socioeconomic status, and residential area were used to compare cancer risk in cholecystectomized and non-cholecystectomized women. Results: During a median follow-up of 11 years, 325,106 cholecystectomized women developed 10,431 primary breast, 2888 endometrial, 1577 ovarian, and 705 cervical cancers. The risk of ovarian cancer was increased by 35% (95% confidence interval (CI) 2% to 77%) in the first 6 months after cholecystectomy. The exclusion of cancers diagnosed in the first 6 months still resulted in an increased risk of endometrial (19%, 95%CI 14% to 23%) and breast (5%, 95%CI 3% to 7%) cancer, especially in women cholecystectomized after age 50 years. By contrast, cholecystectomized women showed decreased risks of cervical (-13%, 95%CI -20% to -7%) and ovarian (-6%, 95%CI -10% to -1%) cancer. Conclusions: The risk of ovarian cancer increased by 35% in a just short period of time (6 months) following the surgery. Therefore, it is worth ruling out ovarian cancer before cholecystectomy. Women undergoing cholecystectomy showed an increased risk of breast and endometrial cancer up to 30 years after surgery. Further evaluation of the association between gallstones or gallbladder removal on female cancer risk would allow for the assessment of the need to intensify cancer screening in cholecystectomized women.

Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method

Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China.

A Validated Risk Prediction Model for Breast Cancer in US Black Women

PURPOSE

Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings.

METHODS

Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)-specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic).

RESULTS

The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor-specific relative risks and attributable risks.

CONCLUSION

Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

Development and external validation of a breast cancer absolute risk prediction model in Chinese population

BACKGROUNDS

In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet.

METHODS

A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004-2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women's Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy.

RESULTS

During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94-1.09) and 0.94 (95% CI, 0.89-0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608-0.661) and 0.585 (95% CI, 0.564-0.605) in the CKB and the SWHS, respectively.

CONCLUSIONS

Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals' awareness and aid risk-stratified screening and prevention strategies.

Estimating the Breast Cancer Burden in Germany and Implications for Risk-based Screening

In Germany, it is currently recommended that women start mammographic breast cancer screening at age 50. However, recently updated guidelines state that for women younger than 50 and older than 70 years of age, screening decisions should be based on individual risk. International clinical guidelines recommend starting screening when a woman's 5-year risk of breast cancer exceeds 1.7%. We thus compared the performance of the current age-based screening practice with an alternative risk-adapted approach using data from a German population representative survey. We found that 10,498,000 German women ages 50-69 years are eligible for mammographic screening based on age alone. Applying the 5-year risk threshold of 1.7% to individual breast cancer risk estimated from a model that considers a woman's reproductive and personal characteristics, 39,000 German women ages 40-49 years would additionally be eligible. Among those women, the number needed to screen to detect one breast cancer case, NNS, was 282, which was close to the NNS = 292 among all 50- to 69-year-old women. In contrast, NNS = 703 for the 113,000 German women ages 50-69 years old with 5-year breast cancer risk <0.8%, the median 5-year breast cancer risk for German women ages 45-49 years, which we used as a low-risk threshold. For these low-risk women, longer screening intervals might be considered to avoid unnecessary diagnostic procedures. In conclusion, we show that risk-adapted mammographic screening could benefit German women ages 40-49 years who are at elevated breast cancer risk and reduce cost and burden among low-risk women ages 50-69 years. PREVENTION RELEVANCE: We show that a risk-based approach to mammography screening for German women can help detect breast cancer in women ages 40-49 years with increased risk and reduce screening costs and burdens for low-risk women ages 50-69 years. However, before recommending a particular implementation of a risk-based mammographic screening approach, further investigations of models and thresholds used are needed.

The impact of mammographic screening on the subsequent breast cancer risk associated with biopsy-proven benign breast disease

Data on the risk of breast cancer following a benign breast disease (BBD) diagnosis were derived predominantly from populations of women biopsied before the widespread use of mammographic screening and in whom these lesions were mostly incidental findings. Whether or not similar risk associations are seen when these lesions are detected in mammographically screened populations is unknown. To address this, we examined the variation in BBD and breast cancer risk associations by the calendar time of BBD diagnosis (pre- vs. post-mammography era [before vs. 1985 and after]) in a nested case–control study within the Nurses’ Health Study (NHS) and NHSII BBD subcohort (488 cases; 1908 controls). We performed logistic regression analysis, adjusting for matching factors and potential confounders, to estimate odds ratio (ORs) and 95% confidence interval (CI) for the association between BBD subtype (non-proliferative, proliferative without atypia, proliferative with atypical hyperplasia (AH)) and subsequent breast cancer risk. When compared with non-proliferative lesions, both proliferative lesions without atypia (PWA) and AHs were associated with similar levels of risk in the pre-mammographic (pre) and post-mammographic (post) time periods (PWA: OR [95% CI] = 1.73 [1.27, 2.36] pre vs. 1.12 [0.73, 1.74] post; AH: 4.41 [2.90, 6.70] pre vs. 3.69 [2.21, 6.15] post). The interaction by mammography era was not statistically significant (p-interaction = 0.47). These results suggest that the risk associations reported for BBD subtypes in the pre-mammography era remain valid for BBD detected after the widespread implementation of mammographic screening.

Individualized prediction of 2-year risk of relapse as indexed by psychiatric hospitalization following psychosis onset: Model development in two first episode samples

BACKGROUND

Although most patients with psychotic disorders experience relapse, it is not possible to predict whether or when an individual patient is going to relapse. We aimed to develop a multifactorial risk prediction algorithm for predicting risk of relapse in first episode psychosis (FEP).

METHODS

Data from two prospectively collected cohorts of FEP patients (N = 1803) were used to develop three multiple logistic prediction models to predict risk of relapse (defined as hospitalization) within the first 2 years of onset of psychosis. Model 1 (M1_S1) used data obtained from clinical notes (Sample 1) while model 2 (M2_S2) applied the same set of predictors using data obtained from research interviews (Sample 2). The final model (Sample 2: M3_S2) used the same predictors plus additional detailed information on predictors. Model performance was evaluated employing measures of overall accuracy, calibration, discrimination and internal validation.

RESULTS

In both samples, the 2-year probability of psychiatric hospitalization was 37%. Of all the models, discrimination accuracy was lowest when limited information (such as socio-demographic and clinical parameters) was included in the prediction model. Model M3_S2 using additional information (descriptors of pattern of cannabis, nicotine, alcohol and other illicit drug use) obtained from research interview had the best discrimination accuracy (Harrell's C index 0.749).

CONCLUSIONS

The measures that contributed most to predicting hospitalization are readily accessible in routine clinical practice, suggesting that a risk prediction tool based on these models would be clinically practicable following validation in independent samples and permit a personalized approach to relapse prevention in psychosis.

Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence

Incidence and mortality rates of endometrial cancer are increasing, leading to increased interest in endometrial cancer risk prediction and stratification to help in screening and prevention. Previous risk models have had moderate success with the area under the curve (AUC) ranging from 0.68 to 0.77. Here we demonstrate a population-based machine learning model for endometrial cancer screening that achieves a testing AUC of 0.96. We train seven machine learning algorithms based solely on personal health data, without any genomic, imaging, biomarkers, or invasive procedures. The data come from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). We further compare our machine learning model with 15 gynecologic oncologists and primary care physicians in the stratification of endometrial cancer risk for 100 women. We find a random forest model that achieves a testing AUC of 0.96 and a neural network model that achieves a testing AUC of 0.91. We test both models in risk stratification against 15 practicing physicians. Our random forest model is 2.5 times better at identifying above-average risk women with a 2-fold reduction in the false positive rate. Our neural network model is 2 times better at identifying above-average risk women with a 3-fold reduction in the false positive rate. Our machine learning models provide a non-invasive and cost-effective way to identify high-risk sub-populations who may benefit from early screening of endometrial cancer, prior to disease onset. Through statistical biopsy of personal health data, we have identified a new and effective approach for early cancer detection and prevention for individual patients.

Progression from being at-risk to psychosis: next steps

Over the past 20 years there has been a great deal of research into those considered to be at risk for developing psychosis. Much has been learned and studies have been encouraging. The aim of this paper is to offer an update of the current status of research on risk for psychosis, and what the next steps might be in examining the progression from CHR to psychosis. Advances have been made in accurate prediction, yet there are some methodological issues in ascertainment, diagnosis, the use of data-driven selection methods and lack of external validation. Although there have been several high-quality treatment trials the heterogeneity of this clinical high-risk population has to be addressed so that their treatment needs can be properly met. Recommendations for the future include more collaborative research programmes, and ensuring they are accessible and harmonized with respect to criteria and outcomes so that the field can continue to move forward with the development of large collaborative consortiums as well as increased funding for multisite projects.

Risk assessment of endometrial cancer and endometrial intraepithelial neoplasia in women with abnormal bleeding and implications for clinical management algorithms

BACKGROUND

Most endometrial cancer cases are preceded by abnormal uterine bleeding, offering a potential opportunity for early detection and cure of endometrial cancer. Although clinical guidelines exist for diagnostic workup of abnormal uterine bleeding, consensus is lacking regarding optimal management for women with abnormal bleeding to diagnose endometrial cancer.

OBJECTIVE

We report the baseline data from a prospective clinical cohort study of women referred for endometrial evaluation at the Mayo Clinic, designed to evaluate risk stratification in women at increased risk for endometrial cancer. Here, we introduce a risk-based approach to evaluate diagnostic tests and clinical management algorithms in a population of women with abnormal bleeding undergoing endometrial evaluation at the Mayo Clinic.

STUDY DESIGN

A total of 1163 women aged ≥45 years were enrolled from February 2013 to May 2019. We evaluated baseline absolute risks and 95% confidence intervals of endometrial cancer and endometrial intraepithelial neoplasia according to clinical algorithms for diagnostic workup of women with postmenopausal bleeding (assessment of initial vs recurrent bleeding episode and endometrial thickness measured through transvaginal ultrasound). We also evaluated risks among women with postmenopausal bleeding according to baseline age (<60 vs 60+ years) as an alternative example. For this approach, biopsy would be conducted for all women aged 60+ years and those aged <60 years with an endometrial thickness of >4 mm. We assessed the clinical efficiency of each strategy by estimating the percentage of women who would be referred for endometrial biopsy, the percentage of cases detected and missed, and the ratio of biopsies per case detected.

RESULTS

Among the 593 women with postmenopausal bleeding, 18 (3.0%) had endometrial intraepithelial neoplasia, and 47 (7.9%) had endometrial cancer, and among the 570 premenopausal women with abnormal bleeding, 8 (1.4%) had endometrial intraepithelial neoplasia, and 7 (1.2%) had endometrial cancer. Maximum risk was noted in women aged 60+ years (17.7%; 13.0%-22.3%), followed by those with recurrent bleeding (14.7%; 11.0%-18.3%). Among women with an initial bleeding episode for whom transvaginal ultrasound was recommended, endometrial thickness did not provide meaningful risk stratification: risks of endometrial cancer and endometrial intraepithelial neoplasia were nearly identical in women with an endometrial thickness of >4 mm (5.8%; 1.3%-10.3%) and ≤4 mm (3.6%; 0.9%-8.6%). In contrast, among those aged <60 years with an endometrial thickness of >4 mm, the risk of endometrial cancer and endometrial intraepithelial neoplasia was 8.4% (4.3%-12.5%), and in those with an endometrial thickness of ≤4 mm, the risk was 0% (0.0%-3.0%; P=.01). The most efficient strategy was to perform biopsy in all women aged 60+ years and among those aged <60 years with an endometrial thickness of >4 mm, with the lowest percentage referred to biopsy while still detecting all cases.

CONCLUSION

Existing clinical recommendations for endometrial cancer detection in women with abnormal bleeding are not consistent with the underlying risk. Endometrial cancer risk factors such as age can provide important risk stratification compared with the assessment of recurrent bleeding. Future research will include a formal assessment of clinical and epidemiologic risk prediction models in our study population as well as validation of our findings in other populations.

A Complex Systems Model of Breast Cancer Etiology: The Paradigm II Conceptual Model

BACKGROUND

The etiology of breast cancer is a complex system of interacting factors from multiple domains. New knowledge about breast cancer etiology continues to be produced by the research community, and the communication of this knowledge to other researchers, practitioners, decision makers, and the public is a challenge.

METHODS

We updated the previously published Paradigm model (PMID: 25017248) to create a framework that describes breast cancer etiology in four overlapping domains of biologic, behavioral, environmental, and social determinants. This new Paradigm II conceptual model was part of a larger modeling effort that included input from multiple experts in fields from genetics to sociology, taking a team and transdisciplinary approach to the common problem of describing breast cancer etiology for the population of California women in 2010. Recent literature was reviewed with an emphasis on systematic reviews when available and larger epidemiologic studies when they were not. Environmental chemicals with strong animal data on etiology were also included.

RESULTS

The resulting model illustrates factors with their strength of association and the quality of the available data. The published evidence supporting each relationship is made available herein, and also in an online dynamic model that allows for manipulation of individual factors leading to breast cancer (https://cbcrp.org/causes/).

CONCLUSIONS

The Paradigm II model illustrates known etiologic factors in breast cancer, as well as gaps in knowledge and areas where better quality data are needed.

IMPACT

The Paradigm II model can be a stimulus for further research and for better understanding of breast cancer etiology.

Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

BACKGROUND

Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).

METHODS

We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.

RESULTS

The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula.

CONCLUSIONS

Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Validation of two US breast cancer risk prediction models in German women

PURPOSE

There are no models for German women that predict absolute risk of invasive breast cancer (BC), i.e., the probability of developing BC over a prespecified time period, given a woman's age and characteristics, while accounting for competing risks. We thus validated two absolute BC risk models (BCRAT, BCRmod) developed for US women in German women. BCRAT uses a woman's medical, reproductive, and BC family history; BCRmod adds modifiable risk factors (body mass index, hormone replacement therapy and alcohol use).

METHODS

We assessed model calibration by comparing observed BC numbers (O) to expected numbers (E) computed from BCRmod/BCRAT for German women enrolled in the prospective European Prospective Investigation into Cancer and Nutrition (EPIC), and after updating the models with German BC incidence/competing mortality rates. We also compared 1-year BC risk predicted for all German women using the German Health Interview and Examination Survey for Adults (DEGS) with overall German BC incidence. Discriminatory performance was quantified by the area under the receiver operator characteristics curve (AUC).

RESULTS

Among 22,098 EPIC-Germany women aged 40+ years, 745 BCs occurred (median follow-up: 11.9 years). Both models had good calibration for total follow-up, E_BCRmod/O = 1.08 (95% confidence interval: 0.95-1.21), and E_BCRAT/O = 0.99(0.87-1.11), and over 5 years. Compared to German BC incidence rates, both models somewhat overestimated 1-year risk for women aged 55+ and 70+ years. For total follow-up, AUC_BCRmod = 0.61(0.58-0.63) and AUC_BCRAT = 0.58(0.56-0.61), with similar values for 5-year follow-up.

CONCLUSION

US BC risk models showed adequate calibration in German women. Discriminatory performance was comparable to that in US women. These models thus could be applied for risk prediction in German women.

Models Predicting Psychosis in Patients With High Clinical Risk: A Systematic Review

OBJECTIVE

The present study reviews predictive models used to improve prediction of psychosis onset in individuals at clinical high risk for psychosis (CHR), using clinical, biological, neurocognitive, environmental, and combinations of predictors.

METHODS

A systematic literature search on PubMed was carried out (from 1998 through 2019) to find all studies that developed or validated a model predicting the transition to psychosis in CHR subjects.

RESULTS

We found 1,406 records. Thirty-eight of them met the inclusion criteria; 11 studies using clinical predictive models, seven studies using biological models, five studies using neurocognitive models, five studies using environmental models, and 18 studies using combinations of predictive models across different domains. While the highest positive predictive value (PPV) in clinical, biological, neurocognitive, and combined predictive models were relatively high (all above 83), the highest PPV across environmental predictive models was modest (63%). Moreover, none of the combined models showed a superiority when compared with more parsimonious models (using only neurocognitive, clinical, biological, or environmental factors).

CONCLUSIONS

The use of predictive models may allow high prognostic accuracy for psychosis prediction in CHR individuals. However, only ten studies had performed an internal validation of their models. Among the models with the highest PPVs, only the biological and neurocognitive but not the combined models underwent validation. Further validation of predicted models is needed to ensure external validity.

The Risk of Ovarian Cancer Increases with an Increase in the Lifetime Number of Ovulatory Cycles: An Analysis from the Ovarian Cancer Cohort Consortium (OC3)

Repeated exposure to the acute proinflammatory environment that follows ovulation at the ovarian surface and distal fallopian tube over a woman's reproductive years may increase ovarian cancer risk. To address this, analyses included individual-level data from 558,709 naturally menopausal women across 20 prospective cohorts, among whom 3,246 developed invasive epithelial ovarian cancer (2,045 serous, 319 endometrioid, 184 mucinous, 121 clear cell, 577 other/unknown). Cox models were used to estimate multivariable-adjusted HRs between lifetime ovulatory cycles (LOC) and its components and ovarian cancer risk overall and by histotype. Women in the 90th percentile of LOC (>514 cycles) were almost twice as likely to be diagnosed with ovarian cancer than women in the 10th percentile (<294) [HR (95% confidence interval): 1.92 (1.60-2.30)]. Risk increased 14% per 5-year increase in LOC (60 cycles) [(1.10-1.17)]; this association remained after adjustment for LOC components: number of pregnancies and oral contraceptive use [1.08 (1.04-1.12)]. The association varied by histotype, with increased risk of serous [1.13 (1.09-1.17)], endometrioid [1.20 (1.10-1.32)], and clear cell [1.37 (1.18-1.58)], but not mucinous [0.99 (0.88-1.10), P-heterogeneity = 0.01] tumors. Heterogeneity across histotypes was reduced [P-heterogeneity = 0.15] with adjustment for LOC components [1.08 serous, 1.11 endometrioid, 1.26 clear cell, 0.94 mucinous]. Although the 10-year absolute risk of ovarian cancer is small, it roughly doubles as the number of LOC rises from approximately 300 to 500. The consistency and linearity of effects strongly support the hypothesis that each ovulation leads to small increases in the risk of most ovarian cancers, a risk that cumulates through life, suggesting this as an important area for identifying intervention strategies. SIGNIFICANCE: Although ovarian cancer is rare, risk of most ovarian cancers doubles as the number of lifetime ovulatory cycles increases from approximately 300 to 500. Thus, identifying an important area for cancer prevention research.

Intercepting Endometrial Cancer: Opportunities to Expand Access Using New Technology

Although endometrial cancer is often diagnosed at an early curable stage, the incidence and mortality from endometrial cancer is rising and minority women are particularly at risk. We hypothesize that delays in clinical presentation contribute to racial disparities in endometrial cancer mortality and treatment-related morbidity. Improved methods for endometrial cancer risk assessment and distinguishing abnormal uterine bleeding and postmenopausal bleeding from physiologic variation are needed. Accordingly, we propose a multipronged strategy that combines innovative patient education with novel early detection strategies to reduce health impacts of endometrial cancer and its precursors, especially among Black women. Futuristic approaches using gamification, smartphone apps, artificial intelligence, and health promotion outside of the physical clinic hold promise in preventing endometrial cancer and reducing morbidity and mortality related to the disease, but they also raise a number of questions that will need to be addressed by future research.

Medication-related harm in older adults following hospital discharge: development and validation of a prediction tool

OBJECTIVES

To develop and validate a tool to predict the risk of an older adult experiencing medication-related harm (MRH) requiring healthcare use following hospital discharge.

DESIGN, SETTING, PARTICIPANTS

Multicentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015.

PRIMARY OUTCOME MEASURE

Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non-adherence, harm from medication error). Three data sources provided MRH and healthcare use information: hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated.

RESULTS

1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases ('possible' and 'probable') were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced 'definite' MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool's discrimination C-statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches.

CONCLUSIONS

The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool's evaluation in other settings.

Progesterone induces cell apoptosis via the CACNA2D3/Ca2+/p38 MAPK pathway in endometrial cancer

Endometrial cancer (EC) is one of the most common malignant gynecological tumors in women. The main treatments for EC (surgery, chemotherapy and radiation therapy) produce significant side effects. Thus, it is urgent to identify promising therapeutic targets and prognostic markers. CACNA2D3, as a member of the calcium channel regulatory α2δ subunit family, is reported to exert a tumor suppressive effect in numerous cancers. However, the function of CACNA2D3 in EC is not well known. In the present study, CACNA2D3 was lowly expressed in EC tissues and cells. The overexpression of CACNA2D3 via lentiviral particle injection significantly blocked the tumor growth in an in vivo xenograft model. In vitro, the overexpression of CACNA2D3 markedly inhibited cell proliferation and migration, and promoted cell apoptosis and calcium influx. These data revealed that CACNA2D3 functions as a tumor suppressor in EC. It was also revealed that the addition of progesterone (P4) blocked tumor growth in Ishikawa‑injected nude mice. P4 induced the expression of CACNA2D3 in vivo and in vitro, and the silencing of CACNA2D3 affected P4‑inhibited cell proliferation and P4‑induced cell apoptosis and calcium influx. In Ishikawa cells, P4 enhanced the expression of phosphorylated (p)‑p38 MAPK and PTEN, but blocked the levels of p‑PI3K and p‑AKT. The knockdown of CACNA2D3 blocked the function of P4. These data revealed that P4 promoted cell apoptosis via the activation of the CACNA2D3/Ca2+/p38 MAPK pathway, and blocked cell proliferation via suppression of the PI3K/AKT pathway. Collectively, these findings indicated the antitumor role of CACNA2D3 in EC, and revealed the mechanism of P4 inhibition of EC progression, which provided a new target for EC therapy and new evidence for P4 in EC therapy.

Predicting breast cancer risk using personal health data and machine learning models

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.

A way forward for cancer prevention therapy: personalized risk assessment

Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.

A prospective clinical cohort study of women at increased risk for endometrial cancer

OBJECTIVE

To evaluate endometrial cancer (EC) risk assessment and early detection strategies in high-risk populations, we designed a large, prospective cohort study of women undergoing endometrial evaluation to assess risk factors and collect novel biospecimens for future testing of emerging EC biomarkers. Here we report on the baseline findings of this study.

METHODS

Women aged ≥45 years were enrolled at the Mayo Clinic from February 2013-June 2018. Risk factors included age, body mass index (BMI), smoking, oral contraceptive and hormone therapy use, and parity. We collected vaginal tampons, endometrial biopsies, and Tao brush samples. We estimated mutually-adjusted odds ratios (OR) and 95% confidence intervals (CI) using multinomial logistic regression; outcomes included EC, atypical hyperplasia, hyperplasia without atypia, disordered proliferative endometrium, and polyps, versus normal endometrium.

RESULTS

Subjects included 1205 women with a mean age of 55 years; 55% were postmenopausal, and 90% had abnormal uterine bleeding. The prevalence of EC was 4.1% (n = 49), predominantly diagnosed in postmenopausal women (85.7%). Tampons and Tao brushings were obtained from 99% and 68% of women, respectively. Age (OR 1.14, 95% CI 1.1-1.2) and BMI (OR 1.39, 95% CI 1.1-1.7) were positively associated with EC; atypical hyperplasia (OR 1.07, 95% CI 1.0-1.1; OR 2.00, 95% CI 1.5-2.6, respectively), and polyps (OR 1.06, 95% CI 1.0-1.1; OR 1.17, 95% CI 1.0-1.3, respectively); hormone therapy use and smoking were inversely associated with EC (OR 0.42, 95%, 0.2-0.9; OR 0.43, 95% CI, 0.2-0.9, respectively). Parity and past oral contraception use were not associated with EC.

CONCLUSIONS

Well-established EC risk factors may have less discriminatory accuracy in high-risk populations. Future analyses will integrate risk factor assessment with biomarker testing for EC detection.