Breast Tissue Organisation and its Association with Breast Cancer Risk

Analysis of the relationship between global breast density and maximum points of breast density in a sample of Brazilian women

Maximum points of breast density have been strongly associated with breast cancer masking than global breast density, which is a widely used measure today. The objective of this work was to verify the correlation between two measures of global breast density (VBD_GLOBAL and DAB_GLOBAL) and a measure of maximum point breast density (VBD_MP). Mammographic images of 4.020 patients were analyzed using the Volpara software, which calculated or extracted the variables needed for the study from the DICOM header. Two-tailed partial correlation tests were performed between the variable VBD_GLOBAL with VBD_MP and DAB_GLOBAL with VBD_MP in the following contexts: keeping PA and CBT constant, keeping only CBT constant, and keeping only PA constant. The Pearson test was also used to verify the bivariate correlation between VBD_GLOBAL with VBD_MP and DAB_GLOBAL with VBD_MP. For the two-tailed partial correlation tests between VBD_GLOBAL with VBD_MP, keeping the CBT and PA variables constant resulted in r = 0.845 (p < 0.05). When kept constant only the CBT, r = 0.875 (p < 0.05), and keeping only the PA constant r = 0.866 (p < 0.05). Pearson's test showed r = 0.883 (p < 0.05). For the two-tailed partial correlation tests between the DAB_GLOBAL with VBD_MP quantities, the results were r = 0.675 (p < 0.05), r = 0.725 (p < 0.05) and r = 0.701 (p < 0.05) for constant CBT and PA, constant CBT and constant PA, respectively, while the Pearson test resulted in r = 0.738 (p < 0.05). We conclude that a woman who has high global breast density is also highly likely to have maximum points of breast density.

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.

Analysis of breast cancer subtypes and their correlations with receptors and ultrasound

The study aim was to evaluate the ultrasound (US) signs of the mammary lesions classified in the Breast Imaging-Reporting and Data System (BI-RADS) score category 3, 4, and 5, corresponding to US BI-RADS. It also followed the correlation between US changes of lesions suggestive for malignancy with the histopathological results and evaluated the proper management of those lesions. There were correlations of breast cancer (BC) subtypes with the receptors [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2)], and Ki67 index, and the signs of conventional ultrasonography and US elastography. We selected 108 female patients examined with US, mammography and fine-needle biopsy who presented suspicions for malignancy lesions. Following the immunohistochemical analysis, they were classified in one of the BC subtypes. According to chi-squared analysis of molecular cancer subtypes correlation to receptors and Ki67 index, we found significant associations between both luminal A and luminal B HER2-negative subtypes and hormone receptors (ER, PR). These have an inverse relationship with Ki67 index elevated values; luminal B HER2-positive subtype has a direct association with HER2 presence; HER2-enriched subtype was statistically significant associated to HER2 presence and elevated Ki67 index values but had an inverse relationship to hormone receptors (ER, PR); triple-negative subtype was strongly associated to Ki67 index values and inversely correlated to ER and PR. We found luminal A subtype as being the most common and luminal B HER2-positive subtype as having the fewer cases.

Breast clinical target volume: HU-based glandular CTVs and ESTRO CTVs in modern and historical radiotherapy treatment planning

PURPOSE

The current study aimed to compare contouring of glandular tissue only (gCTV) with the clinical target volume (CTV) as defined according to European Society for Radiotherapy and Oncology (ESTRO) guidelines (eCTV) and historically treated volumes (marked by wire and determined by palpation and anatomic landmarks) in breast cancer radiotherapy.

METHODS

A total of 56 consecutive breast cancer patients underwent treatment planning based solely on anatomic landmarks/wire markings ("wire based"). From these treatment plans, the 50% and 95% isodoses were transferred as structures and compared to the following CT-based volumes: eCTV; a Hounsfield unit (HU)-based automatic contouring of the gCTV; and standardized planning target volumes (PTVs) generated with 1‑cm safety margins (resulting in the ePTVs and gPTVs, respectively).

RESULTS

The 95% isodose volume of the wire-based plan was larger than the eCTV by 352.39 ± 176.06 cm³ but smaller than the ePTV by 157.58 ± 189.32 cm³. The 95% isodose was larger than the gCTV by 921.20 ± 419.78 cm³ and larger than the gPTV by 190.91 ± 233.49 cm³. Patients with larger breasts had significantly less glandular tissue than those with small breasts. There was a trend toward a lower percentage of glandular tissue in older patients.

CONCLUSION

Historical wire and anatomic landmarks-based treatment planning sufficiently covers the glandular tissue and the theoretical gPTV generated for the glandular tissue. Modern CT-based CTV and PTV definition according to ESTRO results in a larger treated volume than the historical wire-based techniques. HU-standardized glandular tissue contouring results in a significantly smaller CTV and might be an option for reducing the treatment volume and improving reproducibility of contouring between institutions.

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

BACKGROUND

Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.

OBJECTIVE

This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.

METHODS

In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.

RESULTS

The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.

CONCLUSIONS

From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

Incorporating Breast Anatomy in Computational Phenotyping of Mammographic Parenchymal Patterns for Breast Cancer Risk Estimation

We retrospectively analyzed negative screening digital mammograms from 115 women who developed unilateral breast cancer at least one year later and 460 matched controls. Texture features were estimated in multiple breast regions defined by an anatomically-oriented polar grid, and were weighted by their position and underlying dense versus fatty tissue composition. Elastic net regression with cross-validation was performed and area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate ability to predict breast cancer. We also compared our anatomy-augmented features to current state-of-the-art in which parenchymal texture was assessed without considering breast anatomy and evaluated the added value of the extracted features to breast density, body-mass-index (BMI) and age as baseline predictors. Our anatomy-augmented texture features resulted in higher discriminatory capacity (AUC = 0.63 vs. AUC = 0.59) when breast anatomy was not considered (p = 0.021), with dense tissue regions and the central breast quadrant being more heavily weighted. Texture also improved baseline models (from AUC = 0.62 to AUC = 0.67, p = 0.029). Our findings suggest that incorporating breast anatomy information could augment imaging markers of breast cancer risk with the potential to improve personalized breast cancer risk assessment.

A comparison of five methods of measuring mammographic density: a case-control study

BACKGROUND

High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening.

METHODS

Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors.

RESULTS

The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05).

CONCLUSIONS

Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

Breast density: why all the fuss?

The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.