Mammographic Density Category Research Articles

Association Between Radio-pathological Breast Tumor Characteristics and Mammographic Breast Density

Background: Despite mammographic density being a strong indicator of breast cancer risk, it is unclear whether there is association between breast density and certain breast cancer subtypes. This study aimed to investigate the relation between radiologic breast density category and tumor characteristics. Methods: Patients with histologically proven breast cancer who had undergone diagnostic mammography were reviewed retrospectively from 2016 to 2019. The American College of Radiology BI-RADS mammographic density categories were recorded and grouped into low (a and b), and high (c and d). Patient characteristics as well as tumor size, border, pathology, ER, PR, Her2 immunohistochemistry was extracted from the mammography, ultrasonography, and core needle pathology reports. Binary logistic regression model was used to analyze the association between breast density and receptors, molecular subtypes, or tumor features. Results: The present study was comprised of 129 patients, with 7, 47, 41, and 34 patients in the density categories a, b, c, and d, respectively. Patients who had a higher breast density were significantly younger (p=0.001). Those with a lower density were more likely to have HER2, IHC 0 tumors (Odds ratio adjusted for age = 4.9, 95% CI 1.25-18.27, p=0.022). Mammographic density was not related to molecular subtypes and other tumor features. Conclusion: Mammographic dense breast may be associated with Her2 positive breast cancer.

Evaluation of Automated Mammographic Density Classification in Tomosynthesis: Comparison with Radiologists

Objective: Breast cancer screening is a valuable field of health research conducted through mammography. However, mammography evaluation is the examination with the most frequent lack of to agrement among radiologists. In this study we aimed to show the compatibility of mammographic density classification with a new software, Bellus Breast Density Measurement Software (Option), with visual examination. Methods: The mammographic density classification of 500 patients was retrospectively determined by five radiologists with varying levels of experience, according to the 5th version of the breast imaging reporting and data system (BIRADS). The mean age of 500 women included in the study was calculated as 53.8±10.08. The obtained data were compared with the Bellus software mammographic density classification of the same patients. Then, the visual evaluation and the compatibility of the Bellus software and the readers were compared. Results: The agreement between the Bellus software and all five readers was poor (kappa value 0.07-0.12). The agreement of the readers with each other is moderate-good (kappa value 0.054-0.64). The Intraclass Correlation Coefficient (ICC) value for the five separate readers was calculated to be 0.80, indicating good compatibility, while the ICC value for the Bellus software with the five separate readers was calculated to be 0.74, indicating moderate compatibility. The Friedman test revealed that while the mammographic density classification of each reader remained consistent, the classification provided by the Bellus software differed. Conclusion: Bellus Breast Density Measurement Software (Option) diagnostic accuracy is lower than visual examination. We recommend that the manufacturer develop the software.

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study.

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer.

TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network

Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care.

Association of mammographic density and benign breast calcifications individually or combined with hypertension, diabetes, and hypercholesterolemia in women ≥40 years of age: a retrospective study

Recent evidence has linked certain mammographic characteristics, including breast calcifications (Bcs) and mammographic density (MD), with atherosclerotic cardiovascular disease risk factors in women, but data are limited and inconsistent. We...

Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors

Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors

A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm

Mammographic density is known to be a risk indicator for breast abnormalities development. Therefore, the breast tissue classification is an important part used in computer aided diagnosis (CAD) system to detect the cancer. In this paper, a CAD system for breast tissue classification using an equilibrating approach is proposed. The first contribution consists of using a representation of textons distribution by a topological map. This approach allows a good mammographic density classification using the distribution of breast tissue. The second contribution of this work consists of the equilibration of the dataset in the CAD system. Indeed, an improvement of the synthetic minority over-sampling technique (SMOTE) algorithm is developed. Our experiments are carried out with MIAS and digital database of screening mammography (DDSM) datasets to validate the CAD system and two different datasets to validate the proposed modified SMOTE algorithm. The obtained results confirm the validity of the presented proposal.

Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors

Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors

A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm

A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm

906Can mammographic density add value to the Gail model in risk-stratifying women in BreastScreen Australia?

Abstract Background There is significant interest in personalised, risk-based breast cancer screening. This requires high quality risk assessment. The ‘Gail model’ risk assessment tool has been validated on over 40,000 BreastScreen Australia participants. We assess whether adding mammographic density (MD) information improves risk stratification on that cohort. Methods We used questionnaire data, baseline MD readings (using AutoDensity) and linked screening and cancer registry records from 40,158 BreastScreen Australia participants aged 50–69 years (via the lifepool cohort). We investigated incident invasive breast cancer rates by quintiles of Gail model scores, MD, and combinations of Gail and MD. Results Gail scores and MD values were weakly correlated (r≤0.02). Gail and MD were each strong predictors of incident breast cancer, but stronger predictors when used in combination. For example, the odds ratio for incident invasive breast cancer was 3.6 (95%CI 2.5-6.3) for the 17% of women in the upper two quintiles of both Gail and MD scores compared to the 17% of women in the lower two quintiles of both scores. In comparison, the odds ratio for breast cancer between same-size (each 17%) upper and lower groups for Gail score alone was 2.5 (95%CI 1.8-3.4), and for MD 1.9 (95%CI 1.2-2.9). Conclusions Combining Gail and MD categories improves risk stratification on BreastScreen Australia participants, compared to using Gail or MD alone. Key messages While questionnaire data and MD measures are each strong predictors of future invasive breast cancer among BreastScreen Australia participants, risk prediction is stronger when questionnaire and MD measures are combined.

BASCNet: Bilateral adaptive spatial and channel attention network for breast density classification in the mammogram

Breast density is a significant element for breast cancer precaution. The existing mammographic density classification methods cannot achieve satisfactory classification accuracy while achieving end-to-end. In this paper, we present a novel bilateral adaptive spatial and channel attention network (BASCNet) which integrates the information of the left and right breasts and adaptively pays attention to the discriminative features in spatial and channel dimensions. The proposed BASCNet has been fully proved on the public Digital Database for Screening Mammography (DDSM) and INbreast dataset, and the classification accuracies of 85.10% and 90.51% were achieved with fivefold cross-validation, respectively. Our method is fully automatic and has achieved the classification performance superior to the existing breast density classification methods. Massive ablation experiments were conducted to demonstrate the effectiveness of the network structure. Moreover, we compared the effects of different views (CC and MLO) on breast density classification and verified the effectiveness of the contralateral breast information integration. Overall, the proposed BASCNet has the potential to be applied to clinical diagnosis.

Multi-View Mammographic Density Classification by Dilated and Attention-Guided Residual Learning.

Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to the limited model capacity. In this study, we present a radiomics approach based on dilated and attention-guided residual learning for the task of mammographic density classification. The proposed method was instantiated with two datasets, one clinical dataset and one publicly available dataset, and classification accuracies of 88.7 and 70.0 percent were obtained, respectively. Although the classification accuracy of the public dataset was lower than the clinical dataset, which was very likely related to the dataset size, our proposed model still achieved a better performance than the naive residual networks and several recently published deep learning-based approaches. Furthermore, we designed a multi-stream network architecture specifically targeting at analyzing the multi-view mammograms. Utilizing the clinical dataset, we validated that multi-view inputs were beneficial to the breast density classification task with an increase of at least 2.0 percent in accuracy and the different views lead to different model classification capacities. Our method has a great potential to be further developed and applied in computer-aided diagnosis systems. Our code is available at https://github.com/lich0031/Mammographic_Density_Classification.

Association between changes in mammographic density category and the risk of breast cancer: A nationwide cohort study in East‐Asian women

Breast density is strongly associated with breast cancer risk; however, studies on the association between density changes and breast cancer risk have controversial results. The aim of our study was to determine the association between breast density changes and breast cancer risk in East-Asian women. We included 3 301 279 women aged ≥40 years screened for breast cancer twice during 2009 to 2010 and 2011 to 2012. Data were obtained from the National Health Insurance Service (NHIS) database. Breast density was evaluated using the Breast Imaging-Reporting and Data System (BI-RADS). Relative risk (RR) and 5-year risk of developing breast cancer according to density category changes were calculated. Overall, 23.0% of the women had a higher breast density and 22.2% of the women had a lower breast density in second screening compared to the first. An increase in the BI-RADS density category between two subsequent mammographic screenings was associated with an increase in breast cancer risk and vice versa in terms of RR. The 5-year breast cancer risk was affected by the initial BI-RADS density category, changes in density category and patients' characteristics such as age, menopausal status and family history of breast cancer. In patients with breast cancer family history, the 5-year breast cancer risk was prominent, at a maximum of 2.39% (95% CI = 1.23-3.55) in women with breast density category of 2 to 4. Changes in the BI-RADS density category were associated with breast cancer risk. Longitudinal measures of BI-RADS density may be helpful in identifying high-risk women, especially those with a breast cancer family history.

Breast density, benign breast disease, and risk of breast cancer over time.

Assessing the combined effect of mammographic density and benign breast disease is of utmost importance to design personalized screening strategies. We analyzed individual-level data from 294,943 women aged 50-69 years with at least one mammographic screening participation in any of four areas of the Spanish Breast Cancer Screening Program from 1995 to 2015, and followed up until 2017. We used partly conditional Cox models to assess the association between benign breast disease, breast density, and the risk of breast cancer. During a median follow-up of 8.0 years, 3697 (1.25%) women had a breast cancer diagnosis and 5941 (2.01%) had a benign breast disease. More than half of screened women had scattered fibroglandular density (55.0%). The risk of breast cancer independently increased with the presence of benign breast disease and with the increase in breast density (p for interaction = 0.84). Women with benign breast disease and extremely dense breasts had a threefold elevated risk of breast cancer compared with those with scattered fibroglandular density and without benign breast disease (hazard ratio [HR] = 3.07; 95%CI = 2.01-4.68). Heterogeneous density and benign breast disease was associated with nearly a 2.5 elevated risk (HR = 2.48; 95%CI = 1.66-3.70). Those with extremely dense breast without a benign breast disease had a 2.27 increased risk (95%CI = 2.07-2.49). Women with benign breast disease had an elevated risk for over 15 years independently of their breast density category. Women with benign breast disease and dense breasts are at high risk for future breast cancer. • Benign breast disease and breast density were independently associated with breast cancer. • Women with benign breast disease had an elevated risk for up to 15 years independently of their mammographic density category.

Urinary Magnesium and Other Elements in Relation to Mammographic Breast Density, a Measure of Breast Cancer Risk

ABSTRACTPurpose: Heavy metals and other elements may act as breast carcinogens due to estrogenic activity. We investigated associations between urine concentrations of a panel of elements and breast density. Methods: Mammographic density categories were abstracted from radiology reports of 725 women aged 40–65 yr in the Avon Army of Women. A panel of 27 elements was quantified in urine using high resolution magnetic sector inductively coupled plasma mass spectrometry. We applied LASSO (least absolute shrinkage and selection operator) logistic regression to the 27 elements and calculated odds ratios (OR) and 95% confidence intervals (CI) for dense vs. nondense breasts, adjusting for potential confounders. Results: Of the 27 elements, only magnesium (Mg) was selected into the optimal regression model. The odds ratio for dense breasts associated with doubling the Mg concentration was 1.24 (95% CI 1.03–1.49). Doubling the calcium-to-magnesium ratio was inversely associated with dense breasts (OR 0.83, 95% CI 0.70–0.98). Conclusions: Our cross-sectional study found that higher levels of urinary magnesium were associated with greater breast density. Prospective studies are needed to confirm whether magnesium as evaluated in urine is prospectively associated with breast density and, more importantly, breast cancer.

Mammographic Density Distribution in Ras Al Khaimah (RAK): Relationships with Demographic and Reproductive Factors

Objective:Mammographic density is an important risk factor for breast cancer and determines to a large extent mammographic screening efficacy. This study aims to provide baseline data for mammographic density profiling of women living in Ras Al Khaimah (RAK) and to identify risk factors associated with high mammographic density.Methods:A cross-sectional design was used to examine a series of 366 mammography cases. The Breast Imaging Reporting and Data System (BI-RADS, 5th edition) was used to evaluate mammographic density. Pearson’s chi-squared, Mann-Whitney U test and multivariate logistic regression were used for statistical analysis.Results:Most participants (67%) fell into BI-RADS b and c mammographic density categories. Of the total sample, women who were aged ≤ 45 years (p=0.004, OR=1.9), weighed ≤ 71kg (p=<0.0001, OR=4.8), had a body mass index of ≤ 27 kg/m2 (p=<0.0001, OR=5.1) and were of non-Arab descent (p=0.007, OR=1.8) were significantly more likely to have denser breast tissue. Adjusted ethnicity regression analysis showed that Emirati women were significantly less likely to have dense breast tissue compared with Western women (p=0.04, OR=0.4). Among the sample of survey participants, increased odds of having mammographic density were among women who were full-time workers (p=0.02, OR=2.8), of Christian faith (p=0.007, OR=4.4), nulliparous (p=0.003, OR=10.8), had three or fewer children (p=0.03, OR=3.8), and had used oral contraceptives for three years or more (p=0.01, OR=6.1).Conclusion:This study indicated that because Emirati women have a low mammographic density profile, screening mammography can be considered as an effective early detection imaging modality.

Screening methods (clinical breast examination and mammography) to detect breast cancer in women aged 40-49 years.

Objective:The aim of this study is to detect breast cancer rate, nodal status, tumor size, and associated risk factors using clinical breast examination (CBE) and mammography as screening tools in women aged 40–49 years.Materials and Methods:A total of 500 women were screened in a time period of 2 years, between the ages of 40–49 years for breast cancer. Screening tools used were CBE and mammography. Clinical history and risk factors related to breast cancer were recorded. CBE was performed to detect any breast pathology followed by mammographic screening. Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories were used for reporting breast imaging on mammography. For women with dense breasts or an inconclusive mammography report, ultrasonography was performed to assess the lesion/s. Suspicious lesion was subjected to fine-needle aspiration cytology or an open surgical biopsy for a confirmatory diagnosis. Women with history of breast cancer were excluded from the study.Results:CBE was normal in almost 90% of the women. Screening mammography revealed Breast Imaging Reporting and Data System (BI-RADS) I and BI-RADS II in 58.4% and 34.6% of women, respectively. Only 7% of women belonged to BI-RADS III and none in BI-RADS IV category.Conclusion:The study findings are in agreement with the recommendations of the World Health Organization, US preventive task force and UK guidelines that recommend screening mammography in women starting at 50 years.

Abstract IA22: Mammographic density: A risk factor for all breast cancers or only specific subtypes?

Abstract Gene expression profiling has identified molecular subtypes that classify invasive breast cancers into distinct categories that vary in their clinical behavior and response to treatment. These subtypes highlight the many possible biologically and clinically distinct types of breast cancer. Immunohistochemical (IHC) staining of tumor sections using antibody panels has been used as a surrogate for the molecular subtypes identified through gene expression profiling, resulting in a set of intrinsic molecular subtypes. Given the heterogeneity within breast cancer, one might expect that different risk factors influence specific subtypes of breast cancer through various etiologic pathways. We evaluated whether percent mammographic density (MD), the proportion of fibroglandular or white tissue on a mammogram and one of the strongest and most consistent risk factors for breast cancer, is associated equally with all intrinsic molecular subtypes. Data were pooled from six cohort or case-control studies including 3389 women with invasive breast cancer and 9860 without, who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms (for cases, at a median 4 years prior to diagnosis) using a computer-assisted threshold technique, and categorized as 0-10%, 11-25%, 26-50% and 51%+. Receptor status was abstracted from clinical pathology records and supplemented by IHC staining of tumor sections or microarrays. With estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2) information, we classified tumors into the intrinsic molecular subtypes: Luminal A ( ER+ and/or PR+ and HER2- and grade 1 or 2), Luminal B (ER+ and/or PR+ and HER2+ or Luminal A and grade 3), HER2 expressing (HER2+/ER-/PR-) and triple negative (ER-/PR-/HER2-). For cancers found to be triple negative (TN), we also stained for epidermal growth factor receptor (EGFR) and cytokeratins (CK) 5/6 to differentiate basal-like tumors (positive for EGFR and/or CK 5/6) from the unclassifieds (negative on both EGFR and CK 5/6). We used polytomous logistic regression to calculate the odds (OR) of each of the intrinsic subtypes of breast cancer by categories of percent MD, adjusting for age, BMI and study. Contrasts were constructed and tested within the polytomous model framework to investigate differences in the strength of association of percent MD across subtypes. On average, cases were slightly older (56.4 years [SD=11.3]) than controls (55.8 [10.9]). Of the 3389 invasive breast cancer cases, 2146 (63%) were classified as Luminal A, 709 (21%) as Luminal B, 169 (5%) as HER-2 expressing, and 365(11%) as triple negative (TN). Of the TN tumors, 203 had tumor tissue available and were evaluated for CK 5/6 and EGFR, with 167 (82%) classified as basal-like and 36 (18%) as unclassified. Percent (%) MD was associated with breast cancer risk across all intrinsic subtypes. For Luminal A, compared to women with 11-25% MD (reference), women with 0-10% MD had a reduced risk of breast cancer (OR=0.64 [95% confidence interval (CI): 0.55, 0.74]) while women with 26-50% MD had an OR=1.5 (1.3,1.7) and women with 51%+ MD had the highest risk, OR=2.4 (2.0, 2.8). Similar associations were seen for Luminal B, HER2 expressing, and TN across percent MD categories. The small differences among the percent MD-breast cancer associations observed for the different subtypes were not statistically significant, either when comparing the four (Luminal A, Luminal B, HER-2 expressing and TN; p=0.61) or five categories of subtypes (Luminal A, Luminal B, HER-2 expressing, basal-like, unclassified; p=0.66). Our results suggest percent MD is a risk factor for all intrinsic molecular subtypes. Additional study is needed to examine whether these results hold for younger and older aged women. Understanding the importance of mammographic density measures for subtypes of breast cancer has significance for development of subtype-specific risk models. Citation Format: Celine Vachon, Rulla Tamimi, Yunn-Yi Chen, Christopher Scott, Kimberly Bertrand, Matthew Jensen, Daniel Visscher, Aaron Norman, Fergus Couch, John Shepherd, Bo Fan, Fang-Fang Wu, Lin Ma, Andrew Beck, Steve Cummings, V. Shane Pankratz, Karla Kerlikowske. Mammographic density: A risk factor for all breast cancers or only specific subtypes? [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr IA22.

Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction.

IntroductionKnown prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.MethodsWe estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.ResultsDuring 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.ConclusionMammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.

Mammographic density is not a worthwhile examination to distinguish high cancer risk women in screening

Numerous studies established high mammographic density (MD) as a significant breast cancer risk. By adopting both radiological and epidemiological perspectives, we analysed the capacity of this radiological parameter to express an individual level of risk and the methods for assessing the relationship between MD categories and risk. MD is unable to identify individual underlying anatomical and physiological components. Many factors affect accurate and reproducible measurements and consequently classifications of MD. Significant relative risks were found by comparing the MD categories in the tails of distribution (i.e. the group of women with the lowest MD to that with the highest MD), which represent <10% of women in each group: the majority of the population was ignored. When a relevant threshold of MD was applied to compare another group and the entire population was included to compare the two groups, some studies showed no significant or only moderate relative risk (RR) between women with readings above and those below the threshold. Sensitivity and specificity remain unknown. MD cannot be considered a worthwhile test by which to categorically identify high-risk women in screening. Key points • Unknown individual anatomical and physiological components do not express the risk level.• The epidemiological conditions are not relevant to distinguish a high-risk category.• The most relevant studies show no or moderate risks.