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.

Introduction: Mammographic density is a significant risk factor for breast cancer. Classification of mammographic density based on Breast Imaging Reporting and Data System (BI-RADS) is usually used to describe breast density categories but the visual assessment can have some restrictions in a routine check in the screening mammography centers. The object of this study was to investigate the effectiveness of artificial neural networks in predicting breast density, based on the clinical patient dataset in a University hospital. Material and Methods: In this study, mammographic breast density was assessed for 219 women who underwent digital mammography screening using Volpara software. A model based on the Multi-Layer Perceptron Neural Network was trained to predict patient density by identifying the (dense vs. non-dense) breast density categories. The predictive model applied to the classification was examined by the Receiver operating characteristic (ROC) curve. Results: The results show that the model predicted the breast density of patients with a classification rate of 98. 2%. In addition, the area under the curve (AUC) was 0. 998, signifying a high level of classification accuracy. Conclusion: The use of artificial neural networks is useful for predicting patients breast density based on clinical mammograms.

Background: Breast density is routinely reported on mammograms, while breast nodularity is seldom reported on clinical examination, and there is no standard system for its classification. Objectives: The aim of this study was to evaluate the agreement of mammographic density with nodularity on clinical breast examination (CBE). In addition, the relationship of mammographic density with age, menopausal status, and number of parities were assessed. Methods: This cross‑ sectional study was conducted from September 2013 to February 2014, on 30– 70‑ year‑ old women who referred for screening or diagnostic mammography to Shahid Beheshti Hospital, Kashan, Iran. Nodule size, nodule consistency, and mammographic density were assessed, and Cohen’ s kappa coefficient was calculated. The Chi‑ square test was also used to assess the relationship of mammographic density with age, menopausal status, and a number of parities. Results: In this study, 320 women with an age mean value of 46. 01 ± 7. 73 underwent mammography and CBE. Mammographic density showed a statistically significant weak agreement with nodule size (Kappa coefficient = 0. 275; P < 0. 001) and nodule consistency (Kappa coefficient = 0. 256; P < 0. 001). Moreover, mammographic density was inversely related to age, menopausal status, and number of parities (P < 0. 001). Conclusions: Breast nodularity on clinical examination has only a weak agreement with mammographic density. Further studies are needed to develop more comprehensive instruments for assessing not only nodule size and consistency but also nodules distribution in all breast quadrants.

Background: A breast mammogram is considered the gold standard modality for breast cancer screening. The sensitivity of breast mammograms is highly linked to mammographic breast density (MBD) which is strongly linked to women's age, ethnicity, and other factors. Higher breast density is considered one of the risk factors for breast cancer development and it can be associated with different pathological and biological features of breast cancer. Methods: This is a retrospective review of 97 women with a diagnosis of breast cancer in a tertiary hospital in Jordan who were diagnosed in the period between 2018 and 2020. We compared patients with a positive status for estrogen (ER) or progesterone (PR) receptors (ER+/PR+) and others with negative receptors (ER-/PR-), in terms of their correlation with MBD. Results: The analysis has included 97 patients. Of which, 87.6% had either a PR+ or an ER+, and 38.1% of patients were positive for HER2/neu protein. The sample mean age was 56.4 and most patients were obese (56.7%). MBD of our cohort was 30.9% (30/97) fatty, 29.9% (29/97) scattered, 23.7% (23/97) heterogeneously dense, and 15.5% (15/97) dense. The ER+/PR+ group was more associated with fatty (35.3% vs 0.0%) and scattered (31.8% vs 25.0%) MBD types than the ER-/PR- group, and less associated with the dense type (9.4% vs 58.3%), and the association was statistically significant (P < 0.001). However, no statistically significant correlation was found between MBD and the HER2/neu protein status. Conclusion: According to our results, ER+/PR+ group patients tend to have fatty and scattered MBD types than the ER-/PR- patients. Larger randomized cohorts should investigate the association between breast cancer biological subtypes and MBD.