Is mammographic density a marker of breast cancer phenotypes?

Mammographic Breast Density at Breast Cancer Diagnosis and Breast Cancer-Specific Survival

Background: Breast density impacts upon breast cancer risk and recurrence, but its influence on breast cancer-specific survival is unclear. This study examines the influence of mammographic breast density (MBD) at diagnosis on breast cancer-specific survival. Methods: The data of 224 patients diagnosed with breast cancer were analyzed. Two area-based MBD measurement tools-AutoDensity and LIBRA-were used to measure MBD via a mammogram of the contralateral breast acquired at the time of diagnosis. These patients were split into two groups based on their percent breast density (PBD): high (PBD ≥ 20%) versus low (PBD < 20%). Breast cancer-specific survival in each of these PBD groups was assessed at a median follow-up of 34 months using Kaplan-Meier analysis and the Cox proportional hazards model. Results: The proportion of women with low PBD who died from breast cancer was significantly higher than that seen with high PBD (p = 0.01). The 5-year breast cancer-specific survival was poorer among women with low PBD than those with high PBD (0.348; 95% CI: 0.13-0.94) vs. 0.87; 95% CI: (0.8-0.96); p < 0.001)]. Women with higher breast density demonstrated longer survival regardless of the method of PBD measurement: LIBRA [log-rank test (Mantel-Cox): 9.4; p = 0.002)]; AutoDensity [log-rank test (Mantel-Cox) 7.6; p = 0.006]. Multivariate analysis also demonstrated that there was a higher risk of breast cancer-related deaths in women with low PBD (adjusted HR: 5.167; 95% CI: 1.974-13.521; p = 0.001). Conclusion: Women with <20% breast density at breast cancer diagnosis demonstrate poor survival regarding the disease. The impact of breast density on survival is not influenced by the method of measurement.

Development of a predictive model for identifying previously undetected vertical root fractures

This study aimed to develop a predictive model to screen for undetected vertical root fractures (VRFs) in root canal treated teeth. We included 95 root canal treated teeth with suspected VRFs; 77 for training and 18 for validation. Following clinical and cone-beam CT parameters were recorded: sex, tooth type, coronal restoration, time interval from completion of endodontic treatment to definitive diagnosis (TI), type of bone loss (BL), apical extent of root filling (AR) and the ratio of root filling diameter to the actual diameter in the coronal (1/3TA) and middle (2/3TA) root thirds. A predictive model p = 1/(1 - e-x ) was generated, where x = -7.433 + 1.977BL + 1.479 (2/3TA) + 1.102 AR; the sensitivity and specificity were 0.852 and 0.875 for training and 0.917 and 0.833 for validation. VRF teeth were more likely to have vertical bone loss and overfilled root canals. This model had a high diagnostic efficacy for VRFs.

Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram

Radiomic features from mammograms have been shown to predict breast cancer (BC) risk; however, their contribution to BC characteristics has not yet been explored. This study included 184 women with BC between January 2012 and April 2017. A set of 33 global radiomic features were extracted from the ipsilateral breast mammogram. Associations between radiomic features and BC characteristics were investigated by univariate logistic regression analysis, and receiver-operating characteristic curve analysis was employed to evaluate the predictive performance of radiomic features. Histogram-based features (mean, 70th percentile, and 30th percentile) weakly differentiated progesterone status and tumor size (AUC range: 0.627-0.652, p ≤ 0.007). One gray level run length matrix (GLRLM)-based feature achieved an AUC of 0.68 in discriminating lymph-node status, and the fractal dimension achieved an AUC of 0.65 in predicting tumor size. After stratifying by age at BC diagnosis and baseline percent density (PD), the average predictive performance of the abovementioned features improved from 0.652 to 0.707 for baseline PD adjustment, and from 0.652 to 0.674 for age at BC diagnosis. Higher predictive performances were found for GLRLM-based features in predicting lymph-node status among younger women with high baseline PD (AUC range: 0.710-0.863), and for fractal features in predicting tumor size among patients with low PD (AUC: 0.704). Global radiomic features from the ipsilateral breast mammogram can predict lymph-node status and tumor size among certain categories of women and should be considered as a non-invasive tool for clinical decision-making in BC-affected women and for forecasting disease progression.

Breast cancer detection: Comparison of digital mammography and digital breast tomosynthesis across non-dense and dense breasts

INTRODUCTION

Breast density is associated with an increase in breast cancer risk and limits early detection of the disease. This study assesses the diagnostic performance of mammogram readers in digital mammography (DM) and digital breast tomosynthesis (DBT).

METHODS

Eleven breast readers with 1-39 years of experience reading mammograms and 0-4 years of experience reading DBT participated in the study. All readers independently interpreted 60 DM cases (40 normal/20 abnormal) and 35 DBT cases (20 normal/15 abnormal). Sensitivity, specificity, ROC AUC, and diagnostic confidence were calculated and compared between DM and DBT.

RESULTS

DBT significantly improved diagnostic confidence in both dense breasts (p = 0.03) and non-dense breasts (p = 0.003) but not in other diagnostic performance metrics. Specificity was higher in DM for readers with >7 years' experience (p = 0.03) in reading mammography, non-radiologists (p = 0.04), readers who had completed a 3-6 months training fellowship in breast imaging (p = 0.04), and those with ≤2 years' experience in reading DBT (p = 0.02), particularly in non-dense breasts.

CONCLUSION

Diagnostic confidence was higher in DBT when compared to DM. In contrast, other performance metrics appeared to be similar or better with DM and may be influenced by the lack of experience of the reader cohort in reading DBT.

IMPLICATIONS FOR PRACTICE

The benefits of DBT may not be entirely accrued until radiologists attain expertise in DBT interpretation. Specificity of DBT varied according to reader characteristics, and these characteristics may be useful for optimising pairing strategies in independent double reading of DBT as practiced in Australia to reduce false positive diagnostic errors.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.