Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women

Breast cancer risk estimation with intelligent algorithms and risk factors for Cuban women

Breast cancer is the most common malignant neoplasm and the leading cause of cancer mortality among women globally. Current prediction models based on risk factors are inefficient in specific populations, so an appropriate and calibrated breast cancer prediction model for Cuban women is essential. This article proposes a conceptual model for breast cancer risk estimation for Cuban women using machine learning algorithms and risk factors. The model has three main components: knowledge representation, risk estimation modeling, and risk predictor evaluation. Nine of the most common machine learning algorithms were used to generate risk predictors using the proposed model. Two data sources served as case studies: the first comprised data collected from Cuban women, and the second included data from US Hispanic women obtained from the Breast Cancer Surveillance Consortium dataset. The results show that the model effectively estimates breast cancer risk and could be a valuable tool for early detection of breast cancer and identification of patients at risk. According to the first experiment results, the best predictor of breast cancer risk for the Cuban female population corresponds to the Random Forest algorithm with a weighted score of 5.981, a training accuracy of 0.996 and a training AUC of 0.997. In a second experiment, it was demonstrated that the risk predictors generated by the proposed model using data from Cuban women obtained better AUC and accuracy values compared to the predictors generated by using the US Hispanic population, potentially generalizable to other Hispanic populations. Implementing this model could be an economically viable alternative to reduce the mortality rate of this type of cancer in Latin American countries such as Cuba.

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.

The current status of risk-stratified breast screening

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Occupational risk factors and breast cancer in Beijing, China: a hospital-based case-control study

INTRODUCTION

Studies on the association between breast cancer and occupational hazards are limited, especially in China. This is the first study to explore the relationship between breast cancer and occupational hazards in Beijing, China.

DESIGN

A hospital-based case-control study.

SETTING

Eight local hospitals in Beijing, China.

PARTICIPANTS

A total of 973 female participants, comprising 495 cases and 478 controls, were recruited in our study. We identified patients who underwent diagnosis for breast cancer at one of the eight local hospitals in Beijing between 1 January 2015 and 31 December 2019; controls were individuals randomly matched from the same hospital where the cases were confirmed.

MAIN OUTCOME AND MEASURE

Least absolute shrinkage and selection operator (LASSO) regression was used to estimate the occupational risk factors associated with breast cancer, including night shift work history and work posture.

RESULTS

In the case group, the breast cancer type was mainly invasive, which accounted for 85.66% of all the breast cancer patients. Five risk factors were included in the final LASSO model, including body mass index (BMI), marital status, menopause, night shift work history and work posture. Furthermore, these risk factors were considered for multivariate logistic regression, and the analyses suggested that the risk of breast cancer was significantly associated with higher BMI (≥28.0 kg/m², OR: 2.85, 95% CI: 1.29 to 6.30); married status: married (OR: 2.67, 95% CI: 1.28 to 5.56) or divorced (OR: 4.51, 95% CI: 1.84 to 11.07); menopause (OR: 6.89, 95% CI: 5.07 to 9.36); night shift work (OR: 1.53, 95% CI: 1.11 to 2.11); and maximum standing or walking, and minimal sitting (OR: 1.80, 95% CI: 1.19 to 2.73).

CONCLUSION

Breast cancer is associated with occupational risk factors. Night shift work, especially in a standing posture, can increase the incidence of breast cancer in women in Beijing, China.

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.

Breast Cancer Risk Prediction in Korean Women: Review and Perspectives on Personalized Breast Cancer Screening

Due to an increasing proportion of older individuals and the adoption of a westernized lifestyle, the incidence rate of breast cancer is expected to rapidly increase within the next 10 years in Korea. The National Cancer Screening Program (NCSP) of Korea recommends biennial breast cancer screening through mammography for women aged 40-69 years old and according to individual risk and preference for women above 70 years old. There is an ongoing debate on how to most effectively screen for breast cancer, with many proponents of personalized screening, or screening according to individual risk, for women under 70 years old as well. However, to accurately stratify women into risk categories, further study using more refined personalized characteristics, including potentially incorporating a polygenic risk score (PRS), may be needed. While most breast cancer risk prediction models were developed in Western countries, the Korean Breast Cancer Risk Assessment Tool (KoBCRAT) was developed in 2013, and several other risk models have been developed for Asian women specifically. This paper reviews these models compared to commonly used models developed using primarily Caucasian women, namely, the modified Gail, Breast Cancer Surveillance Consortium, Rosner and Colditz, and Tyrer-Cuzick models. In addition, this paper reviews studies in which PRS is included in risk prediction in Asian women. Finally, this paper discusses and explores strategies toward development and implementation of personalized screening for breast cancer in Korea.

A systematic review and quality assessment of individualised breast cancer risk prediction models

Background

Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population.

Methods

We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised.

Results

We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context.

Conclusion

Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.

Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model

Background

Considering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women.

Methods

A cause-specific competing risk model was used to develop the Han Chinese Breast Cancer Prediction model. Data from the Shandong Case-Control Study (328 cases and 656 controls) and Taixing Prospective Cohort Study (13,176 participants) were used to develop and validate the model. The expected/observed (E/O) ratio and C-statistic were calculated to evaluate calibration and discriminative accuracy of the model, respectively.

Results

Compared with the reference level, the relative risks (RRs) for highest level of number of abortions, age at first live birth, history of benign breast disease, body mass index (BMI), family history of breast cancer, and life satisfaction scores were 6.3, 3.6, 4.3, 1.9, 3.3, 2.4, respectively. The model showed good calibration and discriminatory accuracy with an E/O ratio of 1.03 and C-statistic of 0.64.

Conclusions

We developed a risk prediction model including fertility status and relevant disease history, as well as other modifiable risk factors. The model demonstrated good calibration and discrimination ability.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5321-1) contains supplementary material, which is available to authorized users.

Development of diagnostic SCAR markers for genomic DNA amplifications in breast carcinoma by DNA cloning of high-GC RAMP-PCR fragments

Cancer is genetically heterogeneous regarding to molecular genetic characteristics and pathogenic pathways. A wide spectrum of biomarkers, including DNA markers, is used in determining genomic instability, molecular subtype determination and disease prognosis, and estimating sensitivity to different drugs in clinical practice. In a previous study, we developed highly effective DNA markers using improved random amplified polymorphic DNA (RAPD) with high-GC primers, which is a valuable approach for the genetic authentication of medicinal plants. In this study, we applied this effective DNA marker technique to generate genetic fingerprints that detect genomic alterations in human breast cancer tissues and then developed sequence-characterized amplified region (SCAR) markers. Three SCAR markers (BC10-1, BC13-4 and BC31-2) had high levels of genomic DNA amplification in breast cancer. The PHKG2 and RNF40 genes are either overlapping or close to the sequences of SCAR marker BC13-4, while SCAR marker BC10-1 is in the intron and overlap the DPEP1 gene, suggesting that alterations in the expression of these genes could contribute to cancer progression. Screening of breast cancer cell lines showed that the mRNA expression levels for the PHKG2 and DPEP1 were lower in non-tumorigenic mammary epithelial cell MCF10A, but elevated in other cell lines. The DPEP1 mRNA level in invasive ductal carcinoma specimens was significantly higher than that of the adjacent normal tissues in women. Taken together, high-GC RAMP-PCR provides greater efficacy in measuring genomic DNA amplifications, deletion or copy number variations. Furthermore, SCAR markers BC10-1 and BC13-4 might be useful diagnostic markers for breast cancer carcinomas.

Risk assessment model for invasive breast cancer in Hong Kong women

No risk assessment tool is available for identifying high risk population of breast cancer (BCa) in Hong Kong. A case-control study including 918 BCa cases and 923 controls was used to develop the risk assessment model among Hong Kong Chinese women.Each participant received an in-depth interview to obtain their lifestyle and environmental risk factors. Least absolute shrinkage and selection operator (LASSO) selection model was used to select the optimal risk factors (LASSO-model). A risk score system was constructed to evaluate the cumulative effects of selected factors. Bootstrap simulation was used to test the internal validation of the model. Model performance was evaluated by receiver-operator characteristic curves and the area under the curve (AUC).Age, number of parity, number of BCa cases in 1st-degree relatives, exposure to light at night, and sleep quality were the common risk factors for all women. Alcohol drinking was included for premenopausal women; body mass index, age at menarche, age at 1st give birth, breast feeding, using of oral contraceptive, hormone replacement treatment, and history of benign breast diseases were included for postmenopausal women. The AUCs were 0.640 (95% CI, 0.598-0.681) and 0.655 (95% CI, 0.621-0.653) for pre- and postmenopausal women, respectively. Further subgroup evaluation revealed that the model performance was better for women aged 50 to 70 years or ER-positive.This BCa risk assessment tool in Hong Kong Chinese women based on LASSO selection is promising, which shows a slightly higher discriminative accuracy than those developed in other populations.