Diagnostic Reference Levels for digital mammography in Australia

Aims: In 3 phases, this thesis explores: radiation doses delivered to women during mammography, methods to estimate mean glandular dose (MGD), and the use of mammographic breast density (MBD) in MGD calculations. Firstly, it examines Diagnostic reference levels (DRLs) for digital mammography in Australia, with novel focus on the use of compressed breast thickness (CBT) and detector technologies as a guide when determining patient derived DRLs. Secondly, it analyses the agreement between Organ Dose estimated by different digital mammography units and calculated MGD for clinical data. Thirdly, it explores the novel use of MBD in MGD calculations, suggesting a new dose estimation called the actual glandular dose (AGD), and compares MGD to AGD. Methods: DICOM headers were extracted from 52405 anonymised mammograms using 3rd party software. Exposure and QA information were utilised to calculate MGD using 3 methods. LIBRA software was used to estimate MBD for 31097 mammograms. Median, 75th and 95th percentiles were calculated across MGDs obtained for all included data and according to 9 CBT ranges, average population CBT, and for 3 detector technologies. The significance of the differences, correlations, and agreement between MGDs for different CBT ranges, calculation methods, and different density estimation methods were analysed. Conclusions: This thesis have recommended DRLs for mammography in Australia, it shows that MGD is dependent upon CBT and detector technology, hence DRLs were presented as a table for different CBTs and detectors. The work also shows that Organ Doses reported by vendors vary from that calculated using established methodologies. Data produced also show that the use of MGD calculated using standardised glandularities underestimates dose at lower CBTs compared to AGD by up to 10%, hence, underestimating radiation risk. Finally, AGD was proposed; it considers differences in breast composition for individualised radiation-induced risk assessment

Imaging of the Breast

Early detection of breast cancer combined with targeted therapy offers the best outcome for breast cancer patients. This volume deal with a wide range of new technical innovations for improving breast cancer detection, diagnosis and therapy. There is a special focus on improvements in mammographic image quality, image analysis, magnetic resonance imaging of the breast and molecular imaging. A chapter on targeted therapy explores the option of less radical postoperative therapy for women with early, screen-detected breast cancers

Effect on kacip fatimah (labisia pumila) water extract on mamographic density- a pilot study

Introduction Kacip Fatimah is a traditional herb that contains phytoestrogen and is commonly used by the Malay population in Malaysia to treat various gynecological illnesses. It is also used as an alternative to hormone replacement therapy due to its estrogenic effect. Postmenopausal hormone use is associated with increase in mammographic density and mammographic density is an independent risk factor for breast cancer. Objective Our purpose was to evaluate the effect of Kacip Fatimah (Labisia pumila) water extract on mammographic density in postmenopausal women. Material and Method A prospective, randomized, double-blind placebo-controlled pilot study was conducted. A total of 69 postmenopausal women were equally randomized to receive Kacip Fatimah water extract 140 mg/day, 280 mg/day, 560 mg/day or placebo. Mammograms were performed at baseline and after 6 months of treatment. Mammographic density was evaluated according to percentage scale, BIRAD classification and computer assisted measurement of breast density. Result The categorical assessments showed that there was no significant shift in categorical classification as assessed by BIRAD and percentage categories in either control or treatment groups. There was slight increase in breast density as assessed by computer assisted method although the increases were not statistically significant. The increases in breast density over pretreatment baseline were 0.2 °/o, 0.1 %, 1.5 % and 0.6 % for placebo, 140 mg group, 280 mg group and 560 mg group, respectively. These values were not significantly different from one another. This small increase in breast density might be due to the fact that phytoestrogen is a weak estrogen. Conclusion Kacip Fatimah extract given over a period of 6 months did not significantly affect mammographic density

Risk of radiation-induced cancer from screening mammography

Background and Objectives: When the benefits and risks of mammography are considered, the risk of radiation-induced cancer is calculated only for the breast using the mean glandular dose (MGD). Whilst MGD is a useful concept, it has many limitations. This thesis aims to establish a novel method to determine and convey radiation risk from full field digital mammography (FFDM) screening using lifetime effective risk. Method: For effective risk calculations, organ doses as well as examined breast MGD are required. Screening mammography was simulated by exposing a breast phantom for cranio-caudal and medio-lateral oblique for each breast using 16 FFDM machines. An anthropomorphic dosimetry phantom loaded with thermo-luminescent detectors (TLDs) was positioned in contact with the breast phantom to simulate the client’s body. Once the risk per individual was calculated, total effective lifetime risk across 48 worldwide screening programmes was calculated. The total effective risk data sets were analysed to establish a regression model to predict the effective risk of any screening programme. Graphs were generated to extrapolate the total effective risk of any screening programme of specific screening commencement age and frequency considering the MGD differences of different FFDM machines. Since the highest radiation dose after examined breast was received by contralateral breast, the effect of a contralateral breast lead shield on effective risk was also investigated. Results: Large differences in the effective lifetime risk exist between worldwide screening programmes. The effective lifetime risk varied from approximately 50 cases/106 to more than 1000 cases/106. These differences were mainly attributed to the commencement age and frequency of screening. Since tissue radio-sensitivity reduces with age, the cessation age of screening mammography does not result in a noteworthy effect on the total effective risk. The use of contralateral breast shield reduces the total effective risk by about 1.5% for most worldwide screening programmes.Conclusion: A novel method has been proposed to assess radiation-induced cancer risk from FFDM screening which considers the radiation dose received by all body tissues in addition to the examined breast. Using effective risk, the data is more likely to be understandable by screening clients and referring clinicians, unlike MGD which is not readily available or understandable by the general populace. This novel method and the data are compatible with the incoming European Commission legislation about giving the patient information on radiation risk

Predicting Mammographic Breast Density Assessment Using Artificial Neural Networks

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

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume