Developing and validating an individualized breast cancer risk prediction model for women attending breast cancer screening

Alcohol consumption and breast lesions: targets for risk-based screening in high-risk Italian women

BACKGROUND

Breast cancer in Italy is still the most frequent cancer among women, and alcohol consumption is recognized as a risk factor for its development. Overall, in 2020, approximately 10% of all breast cancer-related deaths were attributable to alcohol consumption. Despite advancements in diagnostics and therapeutic options reducing mortality trends, the incidence of breast cancer is projected to rise in Italy. This study aims to assess how alcohol consumption influences the timing of breast lesion diagnosis. Understanding these associations can enhance primary prevention strategies and support the adoption of a risk-based prevention approach, integrating lifestyle factors into personalized screening programs.

METHODS

P.I.N.K. (Prevention, Imaging, Network and Knowledge) study collected data on a prospective dynamic cohort of women who voluntarily underwent breast cancer screening at breast centers throughout Italy, between 2018 and 2023, outside the free national screening program. The occurrence of breast lesion diagnosis and baseline information were collected through clinical visits and an auto-administered questionnaire, including data on absent, moderate or high alcohol consumption during the last 12 months and smoking. 3774 women (mean age 58.9 ± 10.0, range 40-98 years) were included in the present analysis, encompassing women with a suspected or confirmed diagnosis of benign or malignant tumor and healthy women that contributed at least 4 years to the study. An Event History Analysis was carried out to evaluate the effect of alcohol consumption on the timing to event. The event was represented by the transition of the health status, from not diagnosed to diagnosed with breast lesion. The Accelerated Failure Time parameterization was used to directly interpret how the covariates influence the time to the event. The model was adjusted by familiality of breast/ovarian cancer, marital status, level of education, and type of access to health care.

RESULTS

High alcohol consumption exhibited an accelerating effect on the transition to the diagnosed state, indicating a significantly shortened time to event: β coefficient - 0.33 (p-value 0.010) in the adjusted model, indicating an anticipation of about 4 months. The effect of moderate alcohol consumption did not reach statistical significance, neither in the unadjusted model nor in the adjusted model. Adjustment for smoking status led to a further increase of the β coefficient for high alcohol consumption (- 0.40; p value 0.003) and brought moderate alcohol consumption closer to statistical significance (β - 0.15; p-value 0.087). Familiality of breast or ovarian cancer showed a statistically non-significant accelerating effect, while marital status different from maiden, high education, and private access to health care showed decelerating effects.

CONCLUSIONS

High alcohol consumption was confirmed as an accelerating factor in breast lesions diagnosis, while the effect of moderate consumption did not reach statistical significance. These results help identifying actionable targets for high-risk populations, emphasizing personalized risk-based screening programs and gender-sensitive interventions.

Atypical ductal or lobular hyperplasia, lobular carcinoma in-situ, flat epithelial atypia, and future risk of developing breast cancer: Systematic review and meta-analysis

BACKGROUND

Biopsy-proven breast lesions such as atypical ductal hyperplasia (ADH) or atypical lobular hyperplasia (ALH), lobular carcinoma in situ (LCIS) and flat epithelial atypia (FEA) increase subsequent risk of breast cancer (BC), but long-term risk has not been synthesized. A systematic review was conducted to quantify future risk of breast cancer accounting for time since diagnosis of these high-risk lesions.

METHODS

A systematic search of literature from 2000 was performed to identify studies reporting BC as an outcome following core-needle or excision biopsy histology diagnosis of ADH, ALH, LCIS, lobular neoplasia (LN) or FEA. Meta-analyses were conducted to estimate cumulative BC incidence at five-yearly intervals following initial diagnosis for each histology type.

RESULTS

Seventy studies reporting on 47,671 subjects met eligibility criteria. BC incidence at five years post-diagnosis with a high-risk lesion was estimated to be 9.3 % (95 % CI 6.9-12.5 %) for LCIS, 6.6 % (95 % CI 4.4-9.7 %) for ADH, 9.7 % (95 % CI 5.3-17.2 %) for ALH, 8.6 % (95 % CI 6.5-11.4 %) for LN, and 3.8 % (95 % CI 1.2-11.7 %) for FEA. At ten years post-diagnosis, BC incidence was estimated to be 11.8 % (95 % CI 9.0-15.3 %) for LCIS, 13.9 % (95 % CI 7.8-23.6 %) for ADH, 15.4 % (95 % CI 7.2-29.3 %) for ALH, 17.0 % (95 % CI 7.2-35.3 %) for LN and 7.2 % (95 % CI 2.2-21.2 %) for FEA.

CONCLUSION

Our findings demonstrate increased BC risk sustained over time since initial diagnosis of high-risk breast lesions, varying by lesion type, with relatively less evidence for FEA.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

Investigating the relationship between breast and gynecological cancers and infertility and its treatments: a case-control study

OBJECTIVE

Due to the high prevalence of breast and gynecological cancers among women, it is crucial to identify the risk factors of these cancers. Thus, the present study aimed to evaluate the relationship between breast and gynecological cancers and infertility and its treatments in women suffering from these cancers.

METHODS

This case-control study was conducted with the participation of 400 people (200 women with breast and gynecological cancers and 200 healthy women without a history of cancer) in hospitals and health centers in Tabriz, Iran, in 2022. Data were collected using a four-part researcher-made questionnaire including sociodemographic characteristics, obstetric characteristics, information related to cancer, and information related to infertility and its treatments.

RESULT

s Based on the multivariable logistic regression model and by controlling sociodemographic and obstetric characteristics as possible confounding variables, women with cancer had an infertility history almost four times more than women who did not have a history of cancer (OR = 3.56; 95% CI: 1.36-9.33; P = 0.010). Women with breast cancer had an infertility history five times more than women without a history of breast cancer (OR = 5.11; 95% CI: 1.68-15.50; P = 0.004). The infertility history of women with gynecological cancer was more than three times that of the control group. However, there was no statistically significant difference between the two groups (OR = 3.36; 95% CI: 0.99-11.47; P = 0.053).

CONCLUSION

Infertility and its treatments may be involved in increasing the risk of breast and gynecological cancers.

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.

Family history of breast cancer, mammographic breast density and breast cancer risk: Findings from a cohort study of Korean women

BACKGROUND

This study investigated whether the association between family history of breast cancer in first-degree relatives and breast cancer risk varies by breast density.

METHODS

Women aged 40 years and older who underwent screening between 2009 and 2010 were followed up until 2020. Family history was assessed using a self-reported questionnaire. Using Breast Imaging Reporting and Data System (BI-RADS), breast density was categorized into dense breast (heterogeneously or extremely dense) and non-dense breast (almost entirely fatty or scattered areas of fibro-glandular). Cox regression model was used to assess the association between family history and breast cancer risk.

RESULTS

Of the 4,835,507 women, 79,153 (1.6%) reported having a family history of breast cancer and 77,238 women developed breast cancer. Family history led to an increase in the 5-year cumulative incidence in women with dense- and non-dense breasts. Results from the regression model with and without adjustment for breast density yielded similar HRs in all age groups, suggesting that breast density did not modify the association between family history and breast cancer. After adjusting for breast density and other factors, family history of breast cancer was associated with an increased risk of breast cancer in all three age groups (age 40-49 years: aHR 1.96, 95% confidence interval [CI] 1.85-2.08; age 50-64 years: aHR 1.70, 95% CI 1.58-1.82, and age ≥65 years: aHR 1.95, 95% CI 1.78-2.14).

CONCLUSION

Family history of breast cancer and breast density are independently associated with breast cancer. Both factors should be carefully considered in future risk prediction models of breast cancer.

Diagnose earlier, live longer? The impact of cervical and breast cancer screening on life span

Cancer has become a leading cause of death and aroused the cancer scare. Breast and cervical cancer are two main health threats for women. In order to reduce mortality through early detection and early treatment, cancer screening has been widely recommended and applied for breast and cervical cancer detection and prevention. However, the benefit of cancer screening has been a controversial issue for the recent decades. The Chinese government has launched a free screening program on breast and cervical cancer for women since 2009. There is lack of strong data and sufficient information, however, to examine the effect of breast and cervical cancer screening. A Difference-in-Difference model estimated by Cox proportional hazard estimation was applied to evaluate the effects of breast and cervical cancer screening using data from Nown County Cancer Registry between the year 2009 and 2013. Based on the case study in a county of central China, this study found that the screening program reduced the risk of death, but found the lion’s share for the benefit has been mainly due to the cervical cancer screening rather breast cancer screening, which may be related to the difference between early detection screening and preventive screening. Our results suggest sufficient funding and better education of related cancer knowledge will be meaningful measures for the prevention and treatment of breast and cervical cancer.

The translational challenges of precision oncology

The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing efforts and discoveries that have further supported this notion. In this review, we reflect on the impact of these discoveries on our understanding of several concepts: cancer initiation, cancer prevention, early detection, adjuvant therapy and minimal residual disease monitoring, cancer drug resistance, and cancer evolution in metastasis. We discuss key areas of focus for improving cancer outcomes, from biological insights to clinical application, and suggest where the development of these technologies will lead us. Finally, we discuss practical challenges to the wider adoption of molecular profiling in the clinic and the need for robust translational infrastructure.

Long-Term Risk of Breast Cancer after Diagnosis of Benign Breast Disease by Screening Mammography

Assessing the long-term risk of breast cancer after diagnosis of benign breast disease by mammography is of utmost importance to design personalised screening strategies. We analysed individual-level data from 778,306 women aged 50–69 years with at least one mammographic screening participation in any of ten breast cancer screening centers in Spain from 1996 to 2015, and followed-up until 2017. We used Poisson regression to compare the rates of incident breast cancer among women with and without benign breast disease. During a median follow-up of 7.6 years, 11,708 (1.5%) women had an incident of breast cancer and 17,827 (2.3%) had a benign breast disease. The risk of breast cancer was 1.77 times higher among women with benign breast disease than among those without (95% CI: 1.61 to 1.95). The relative risk increased to 1.99 among women followed for less than four years, and remained elevated for two decades, with relative risk 1.96 (95% CI: 1.32 to 2.92) for those followed from 12 to 20 years. Benign breast disease is a long-term risk factor for breast cancer. Women with benign breast disease could benefit from closer surveillance and personalized screening strategies.

Mammographic features of benign breast lesions and risk of subsequent breast cancer in women attending breast cancer screening

OBJECTIVES

To evaluate the mammographic features in women with benign breast disease (BBD) and the risk of subsequent breast cancer according to their mammographic findings.

METHODS

We analyzed data from a Spanish cohort of women screened from 1995 to 2015 and followed up until December 2017 (median follow-up, 5.9 years). We included 10,650 women who had both histologically confirmed BBD and mammographic findings. We evaluated proliferative and nonproliferative BBD subtypes, and their mammographic features: architectural distortion, asymmetries, calcifications, masses, and multiple findings. The adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI) for breast cancer were estimated using a Cox proportional hazards model. We plotted the adjusted cumulative incidence curves.

RESULTS

Calcifications were more frequent in proliferative disease with atypia (43.9%) than without atypia (36.8%) or nonproliferative disease (22.2%; p value < 0.05). Masses were more frequent in nonproliferative lesions (59.1%) than in proliferative lesions without atypia (35.1%) or with atypia (30.0%; p value < 0.05). Multiple findings and architectural distortion were more likely in proliferative disease (16.1% and 4.7%) than in nonproliferative disease (12.8% and 1.9%). Subsequent breast cancer occurred in 268 (2.5%) women. Compared with women who had masses, the highest risk of subsequent breast cancer was found in those with architectural distortions (aHR, 2.21; 95% CI, 1.16-4.22), followed by those with multiple findings (aHR, 1.89; 95% CI, 1.34-2.66), asymmetries (aHR, 1.66; 95% CI, 0.84-3.28), and calcifications (aHR, 1.60; 95% CI, 1.21-2.12).

CONCLUSION

BBD subtypes showed distinct mammographic findings. The risk of subsequent breast cancer was high in those who have shown architectural distortion, multiple findings, asymmetries, and calcifications than in women with masses.

KEY POINTS

• The presence of mammographic findings in women attending breast cancer screening helps clinicians to assess women with benign breast disease (BBD). • Calcifications were frequent in BBDs with atypia, which are the ones with a high breast cancer risk, while masses were common in low-risk BBDs. • The excess risk of subsequent breast cancer in women with BBD was higher in those who showed architectural distortion compared to those with masses.