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Breast Cancer: Risk Factors, Prevention and Early Detection

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Epidemiology and Prevention".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 33260

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Special Issue Editor

Prof. Antonis Valachis

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Department of Oncology, Faculty of Medicine and Health, Örebro University Hospital, 70185 Örebro, Sweden.
Interests: early breast cancer; metastatic breast cancer; epidemiological studies; evidence-based medicine

Special Issue Information

Dear Colleagues,

Despite the advances in breast cancer diagnosis and treatment strategies, breast cancer remains the leading cause of cancer death for women worldwide with a rising disease burden. Breast cancer prevention and early detection strategies could play an important role in reducing the breast cancer burden.

Recent advances in breast cancer genetics and deep learning techniques enable the development of individual-based, risk-adapted approaches for risk prediction and early detection of breast cancer. Technological advancements in breast imaging modalities and in non-invasive biomarkers provide further opportunities for a personalized breast cancer screening and diagnosis.

This Special Issue invites original research articles and reviews describing efforts towards a more personalized approach on breast cancer prevention and early detection.

Prof. Antonis Valachis
Guest Editor

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Keywords

breast cancer
screening
early detection
prevention
risk prediction
risk stratification
personalized medicine
machine learning
big data

Published Papers (13 papers)

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Research

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12 pages, 271 KiB

Open AccessArticle

The FEDRA Longitudinal Study: Repeated Volumetric Breast Density Measures and Breast Cancer Risk

by Giovanna Masala, Melania Assedi, Benedetta Bendinelli, Elisa Pastore, Maria Antonietta Gilio, Vincenzo Mazzalupo, Andrea Querci, Miriam Fontana, Giacomo Duroni, Luigi Facchini, Calogero Saieva, Domenico Palli, Daniela Ambrogetti and Saverio Caini

Cancers 2023, 15(6), 1810; https://doi.org/10.3390/cancers15061810 - 16 Mar 2023

Cited by 1 | Viewed by 1124

Abstract

Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We investigated the association between volumetric MBD measures, their changes over time, and BC risk in a cohort of women participating in the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) study. The study was carried out among 6148 women with repeated MBD measures from full-field digital mammograms and repeated information on lifestyle habits, reproductive history, and anthropometry. The association between MBD measures (modeled as time-dependent covariates), their relative annual changes, and BC risk were evaluated by adjusted Cox models. During an average of 7.8 years of follow-up, 262 BC cases were identified. BC risk was directly associated with standard deviation increments of volumetric percent density (VPD, HR 1.37, 95%CI 1.22–1.54) and dense volume (DV, HR 1.29, 95%CI 1.18–1.41). An inverse association emerged with non-dense volume (NDV, HR 0.82, 95%CI 0.69–0.98). No significant associations emerged between annual changes in VPD, DV, NDV, and BC risk. Higher values of MBD measures, modeled as time-dependent covariates, were positively associated with increased BC risk, while an inverse association was evident for increasing NDV. No effect of annual changes in MBD emerged. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

12 pages, 1337 KiB

Open AccessArticle

The Impact of Prior Mammograms on the Diagnostic Performance of Radiologists in Early Breast Cancer Detection: A Focus on Breast Density, Lesion Features and Vendors Using Wholly Digital Screening Cases

by Phuong Dung (Yun) Trieu, Natacha Borecky, Tong Li, Patrick C. Brennan, Melissa L. Barron and Sarah J. Lewis

Cancers 2023, 15(4), 1339; https://doi.org/10.3390/cancers15041339 - 20 Feb 2023

Cited by 1 | Viewed by 1709

Abstract

Background: This study aims to investigate the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images, and with the presence of prior images from the same and different vendors. Methods: 612 radiologists’ readings across 9 test sets, consisting of 540 screening mammograms (361-normal and 179-cancer) with 245 cases having prior images obtained from same vendor as current images, 129 from a different vendor and 166 cases having no prior images, were retrospectively analysed. True positive (sensitivity), true negative (specificity) and area under ROC curve (AUC) values of radiologists were calculated for three groups of cases (without prior images (NP), with prior images from same vendor (SP), and with prior images from different vendor (DP)). Logistic regression was used to estimate the odds ratio (OR) of true positive, true negative and true cancer localization among case groups with different levels of breast density and lesion characteristics. Results: Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP (0.803-and-0.785 vs. 0.712; p < 0.0001). Specificity in NP and DP cases were 4.8% and 2.0% lower than SP cases (0.749 and 0.771 vs. 0.787). The AUC values for NP and DP were significantly higher than SP cases across different levels of breast density (0.814-and-0.820 vs. 0.782; p < 0.0001). The odds ratio (OR) of true positive for NP relative to SP was 1.6 (p < 0.0001) and DP relative to SP was 1.5 (p < 0.0001). Radiologists were more like to detect architectural distortion in DP than SP cases (OR = 3.2; p < 0.0001), whilst the OR for abnormal calcifications was 2.85 (p < 0.0001). Conclusions: Cases without previous mammograms or with prior mammograms obtained from different vendors were more likely to benefit radiologists in cancer detection, whilst prior mammograms undertaken from the same vendor were more useful for radiologists in evaluating normal cases. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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12 pages, 660 KiB

Open AccessArticle

Body Shape Phenotypes and Breast Cancer Risk: A Mendelian Randomization Analysis

by Laia Peruchet-Noray, Niki Dimou, Anja M. Sedlmeier, Béatrice Fervers, Isabelle Romieu, Vivian Viallon, Pietro Ferrari, Marc J. Gunter, Robert Carreras-Torres and Heinz Freisling

Cancers 2023, 15(4), 1296; https://doi.org/10.3390/cancers15041296 - 17 Feb 2023

Cited by 1 | Viewed by 3064

Abstract

Observational and genetic studies have linked different anthropometric traits to breast cancer (BC) risk, with inconsistent results. We aimed to investigate the association between body shape defined by a principal component (PC) analysis of anthropometric traits (body mass index [BMI], height, weight, waist-to-hip ratio [WHR], and waist and hip circumference) and overall BC risk and by tumor sub-type (luminal A, luminal B, HER2+, triple negative, and luminal B/HER2 negative). We performed two-sample Mendelian randomization analyses to assess the association between 188 genetic variants robustly linked to the first three PCs and BC (133,384 cases/113,789 controls from the Breast Cancer Association Consortium (BCAC)). PC1 (general adiposity) was inversely associated with overall BC risk (0.89 per 1 SD [95% CI: 0.81–0.98]; p-value = 0.016). PC2 (tall women with low WHR) was weakly positively associated with overall BC risk (1.05 [95% CI: 0.98–1.12]; p-value = 0.135), but with a confidence interval including the null. PC3 (tall women with large WHR) was not associated with overall BC risk. Some of these associations differed by BC sub-types. For instance, PC2 was positively associated with a risk of luminal A BC sub-type (1.09 [95% CI: 1.01–1.18]; p-value = 0.02). To clarify the inverse association of PC1 with breast cancer risk, future studies should examine independent risk associations of this body shape during childhood/adolescence and adulthood. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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24 pages, 1347 KiB

Open AccessArticle

Multi-Morbidity and Risk of Breast Cancer among Women in the UK Biobank Cohort

by Afi Mawulawoe Sylvie Henyoh, Rodrigue S. Allodji, Florent de Vathaire, Marie-Christine Boutron-Ruault, Neige M. Y. Journy and Thi-Van-Trinh Tran

Cancers 2023, 15(4), 1165; https://doi.org/10.3390/cancers15041165 - 11 Feb 2023

Cited by 1 | Viewed by 1843

Abstract

(Multi-)Morbidity shares common biological mechanisms or risk factors with breast cancer. This study aimed to investigate the association between the number of morbidities and patterns of morbidity and the risk of female breast cancer. Among 239,436 women (40–69 years) enrolled in the UK Biobank cohort who had no cancer history at baseline, we identified 35 self-reported chronic diseases at baseline. We assigned individuals into morbidity patterns using agglomerative hierarchical clustering analysis. We fitted Cox models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer risk. In total, 58.4% of women had at least one morbidity, and the prevalence of multi-morbidity was 25.8%. During a median 7-year follow-up, there was no association between breast cancer risk (5326 cases) and either the number of morbidities or the identified clinically relevant morbidity patterns: no-predominant morbidity (reference), psychiatric morbidities (HR = 1.04, 95%CI 0.94–1.16), respiratory/immunological morbidities (HR = 0.98, 95%CI 0.90–1.07), cardiovascular/metabolic morbidities (HR = 0.93, 95%CI 0.81–1.06), and unspecific morbidities (HR = 0.98, 95%CI 0.89–1.07), overall. Among women younger than 50 years of age only, however, there was a significant association with psychiatric morbidity patterns compared to the no-predominant morbidity pattern (HR = 1.25, 95%CI 1.02–1.52). The other associations did not vary when stratifying by age at baseline and adherence to mammography recommendations. In conclusion, multi-morbidity was not a key factor to help identify patients at an increased risk of breast cancer. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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16 pages, 1112 KiB

Open AccessArticle

Tumour Growth Models of Breast Cancer for Evaluating Early Detection—A Summary and a Simulation Study

by Rickard Strandberg, Linda Abrahamsson, Gabriel Isheden and Keith Humphreys

Cancers 2023, 15(3), 912; https://doi.org/10.3390/cancers15030912 - 31 Jan 2023

Cited by 2 | Viewed by 1422

Abstract

With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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21 pages, 4906 KiB

Open AccessArticle

The Rochester Modified Magee Algorithm (RoMMa): An Outcomes Based Strategy for Clinical Risk-Assessment and Risk-Stratification in ER Positive, HER2 Negative Breast Cancer Patients Being Considered for Oncotype DX^® Testing

by Bradley M. Turner, Brian S. Finkelman, David G. Hicks, Numbere Numbereye, Ioana Moisini, Ajay Dhakal, Kristin Skinner, Mary Ann G. Sanders, Xi Wang, Michelle Shayne, Linda Schiffhauer, Hani Katerji and Huina Zhang

Cancers 2023, 15(3), 903; https://doi.org/10.3390/cancers15030903 - 31 Jan 2023

Viewed by 2399

Abstract

Introduction: Multigene genomic profiling has become the standard of care in the clinical risk-assessment and risk-stratification of ER⁺, HER2⁻ breast cancer (BC) patients, with Oncotype DX^® (ODX) emerging as the genomic profile test with the most support from the international community. The current state of the health care economy demands that cost-efficiency and access to testing must be considered when evaluating the clinical utility of multigene profile tests such as ODX. Several studies have suggested that certain lower risk patients can be identified more cost-efficiently than simply reflexing all ER⁺, HER2⁻ BC patients to ODX testing. The Magee equations^TM use standard histopathologic data in a set of multivariable models to estimate the ODX recurrence score. Our group published the first outcome data in 2019 on the Magee equations^TM, using a modification of the Magee equations^TM combined with an algorithmic approach—the Rochester Modified Magee algorithm (RoMMa). There has since been limited published outcome data on the Magee equations^TM. We present additional outcome data, with considerations of the TAILORx risk-stratification recommendations. Methods: 355 patients with an ODX recurrence score, and at least five years of follow-up or a BC recurrence were included in the study. All patients received either Tamoxifen or an aromatase inhibitor. None of the patients received adjuvant systemic chemotherapy. Results: There was no significant difference in the risk of recurrence in similar risk categories (very low risk, low risk, and high risk) between the average Modified Magee score and ODX recurrence score with the chi-square test of independence (p > 0.05) or log-rank test (p > 0.05). Using the RoMMa, we estimate that at least 17% of individuals can safely avoid ODX testing. Conclusion: Our study further reinforces that BC patients can be confidently stratified into lower and higher-risk recurrence groups using the Magee equations^TM. The RoMMa can be helpful in the initial clinical risk-assessment and risk-stratification of BC patients, providing increased opportunities for cost savings in the health care system, and for clinical risk-assessment and risk-stratification in less-developed geographies where multigene testing might not be available. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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13 pages, 580 KiB

Open AccessArticle

Use of Nonsteroidal Anti-Inflammatory Drugs and Risk of Breast Cancer: Evidence from a General Female Population and a Mammographic Screening Cohort in Sweden

by Kejia Hu, Maria Feychting, Donghao Lu, Arvid Sjölander, Kamila Czene, Per Hall and Fang Fang

Cancers 2023, 15(3), 692; https://doi.org/10.3390/cancers15030692 - 23 Jan 2023

Viewed by 1737

Abstract

A link has been proposed between the use of nonsteroidal anti-inflammatory drugs (NSAIDs) and the risk of breast cancer. There is, however, insufficient data regarding the subtype and stage of breast cancer, and few studies have assessed the interaction between the use of NSAIDs and breast density or previous breast disorders. There is also a lack of data from population-based studies. We first conducted a nested case-control study within the general female population of Sweden, including 56,480 women with newly diagnosed breast cancer during 2006–2015 and five breast cancer-free women per case as controls, to assess the association of NSAID use with the risk of incident breast cancer, focusing on subtype and stage of breast cancer as well as the interaction between NSAID use and previous breast disorders. We then used the Karolinska Mammography Project for Risk Prediction of Breast Cancer (Karma) cohort to assess the interaction between NSAID use and breast density in relation to the risk of breast cancer. Conditional logistic regression was used to estimate the hazard ratio (HR) and a 95% confidence interval (CI) was used for breast cancer in relation to the use of aspirin and non-aspirin NSAIDs. In the nested case-control study of the general population, exclusive use of aspirin was not associated with the risk of breast cancer, whereas exclusive use of non-aspirin NSAIDs was associated with a modestly higher risk of stage 0–2 breast cancer (HR: 1.05; 95% CI: 1.02–1.08) but a lower risk of stage 3–4 breast cancer (HR 0.80; 95% CI: 0.73–0.88). There was also a statistically significant interaction between the exclusive use of NSAIDs and previous breast disorders (p for interaction: <0.001). In the analysis of Karma participants, the exclusive use of non-aspirin NSAIDs was associated with a lower risk of breast cancer among women with a breast dense area of >40 cm² (HR: 0.72; 95% CI: 0.59–0.89). However, the possibility of finding this by chance cannot be ruled out. Overall, we did not find strong evidence to support an association between the use of NSAIDs and the risk of breast cancer. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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21 pages, 4952 KiB

Open AccessArticle

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

by Rahman Shafique, Furqan Rustam, Gyu Sang Choi, Isabel de la Torre Díez, Arif Mahmood, Vivian Lipari, Carmen Lili Rodríguez Velasco and Imran Ashraf

Cancers 2023, 15(3), 681; https://doi.org/10.3390/cancers15030681 - 22 Jan 2023

Cited by 10 | Viewed by 2852

Abstract

Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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20 pages, 4923 KiB

Open AccessArticle

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics

by Wenlong Ming, Fuyu Li, Yanhui Zhu, Yunfei Bai, Wanjun Gu, Yun Liu, Xiaoan Liu, Xiao Sun and Hongde Liu

Cancers 2022, 14(22), 5507; https://doi.org/10.3390/cancers14225507 - 9 Nov 2022

Cited by 6 | Viewed by 2293

Abstract

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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18 pages, 528 KiB

Open AccessArticle

UK Women’s Views of the Concepts of Personalised Breast Cancer Risk Assessment and Risk-Stratified Breast Screening: A Qualitative Interview Study

by Charlotte Kelley-Jones, Suzanne Scott and Jo Waller

Cancers 2021, 13(22), 5813; https://doi.org/10.3390/cancers13225813 - 19 Nov 2021

Cited by 14 | Viewed by 3147

Abstract

Any introduction of risk-stratification within the NHS Breast Screening Programme needs to be considered acceptable by women. We conducted interviews to explore women’s attitudes to personalised risk assessment and risk-stratified breast screening. Twenty-five UK women were purposively sampled by screening experience and socioeconomic background. Interview transcripts were qualitatively analysed using Framework Analysis. Women expressed positive intentions for personal risk assessment and willingness to receive risk feedback to provide reassurance and certainty. Women responded to risk-stratified screening scenarios in three ways: ‘Overall acceptors’ considered both high- and low-risk options acceptable as a reasonable allocation of resources to clinical need, yet acceptability was subject to specified conditions including accuracy of risk estimates and availability of support throughout the screening pathway. Others who thought ‘more is better’ only supported high-risk scenarios where increased screening was proposed. ‘Screening sceptics’ found low-risk scenarios more aligned to their screening values than high-risk screening options. Consideration of screening recommendations for other risk groups had more influence on women’s responses than screening-related harms. These findings demonstrate high, but not universal, acceptability. Support and guidance, tailored to screening values and preferences, may be required by women at all levels of risk. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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16 pages, 1071 KiB

Open AccessArticle

Influencers of the Decision to Undergo Contralateral Prophylactic Mastectomy among Women with Unilateral Breast Cancer

by Akshara Singareeka Raghavendra, Hala F. Alameddine, Clark R. Andersen, Jesse C. Selber, Abenaa M. Brewster, Carlos H. Barcenas, Abigail S. Caudle, Banu K. Arun, Debu Tripathy and Nuhad K. Ibrahim

Cancers 2021, 13(9), 2050; https://doi.org/10.3390/cancers13092050 - 23 Apr 2021

Cited by 2 | Viewed by 1870

Abstract

(1) Background: The relatively high rate of contralateral prophylactic mastectomy (CPM) among women with early stage unilateral breast cancer (BC) has raised concerns. We sought to assess the influence of partners, physicians, and the media on the decision of women with unilateral BC to undergo CPM and identify clinicopathological variables associated with the decision to undergo CPM. (2) Patients and Methods: Women with stage 0 to III unilateral BC who underwent CPM between January 2010 and December 2017. Patients were surveyed regarding factors influencing their self-determined decision to undergo CPM. Partner, physician, and media influence factors were modeled by logistic regressions with adjustments for a family history of breast cancer and pathological stage. (3) Results: 397 (29.6%) patients completed the survey and were included in the study. Partners, physicians, and the media significantly influenced patients’ decision to undergo CPM. The logistic regression models showed that, compared to self-determination alone, overall influence on the CPM decision was significantly higher for physicians (p = 0.0006) and significantly lower for partners and the media (p < 0.0001 for both). Fifty-nine percent of patients’ decisions were influenced by physicians, 28% were influenced by partners, and only 17% were influenced by the media. The model also showed that patients with a family history of BC had significantly higher odds of being influenced by a partner than did those without a family history of BC (p = 0.015). (4) Conclusions: Compared to self-determination, physicians had a greater influence and partners and the media had a lower influence on the decision of women with unilateral BC to undergo CPM. Strong family history was significantly associated with a patient’s decision to undergo CPM. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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27 pages, 1214 KiB

Open AccessReview

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations

by Brian S. Finkelman, Huina Zhang, David G. Hicks and Bradley M. Turner

Cancers 2023, 15(3), 808; https://doi.org/10.3390/cancers15030808 - 28 Jan 2023

Cited by 7 | Viewed by 4744

Abstract

The 1983 discovery of a mouse monoclonal antibody—the Ki-67 antibody—that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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11 pages, 1016 KiB

Open AccessSystematic Review

Mammographic Density Changes over Time and Breast Cancer Risk: A Systematic Review and Meta-Analysis

by Arezo Mokhtary, Andreas Karakatsanis and Antonis Valachis

Cancers 2021, 13(19), 4805; https://doi.org/10.3390/cancers13194805 - 25 Sep 2021

Cited by 23 | Viewed by 3269

Abstract

The aim of this meta-analysis was to evaluate the association between mammographic density changes over time and the risk of breast cancer. We performed a systematic literature review based on the PubMed and ISI Web of Knowledge databases. A meta-analysis was conducted by computing extracted hazard ratios (HRs) and 95% confidence intervals (CIs) for cohort studies or odds ratios (ORs) and 95% confidence interval using inverse variance method. Of the nine studies included, five were cohort studies that used HR as a measurement type for their statistical analysis and four were case–control or cohort studies that used OR as a measurement type. Increased breast density over time in cohort studies was associated with higher breast cancer risk (HR: 1.61; 95% CI: 1.33–1.96) whereas decreased breast density over time was associated with lower breast cancer risk (HR: 0.78; 95% CI: 0.71–0.87). Similarly, increased breast density over time was associated with higher breast cancer risk in studies presented ORs (pooled OR: 1.85; 95% CI: 1.29–2.65). Our findings imply that an increase in breast density over time seems to be linked to an increased risk of breast cancer, whereas a decrease in breast density over time seems to be linked to a lower risk of breast cancer. Full article

(This article belongs to the Special Issue Breast Cancer: Risk Factors, Prevention and Early Detection)

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