History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century

A comparison of breast cancer incidence and cancer stages before and after the introduction of the Austrian national breast cancer screening program in the federal state of Salzburg : Real-world data from the Tumor Registry Salzburg

BACKGROUND

In January 2014 a national, quality-assured breast cancer screening program was introduced in Austria. To investigate if the program reduced the incidence of advanced breast cancer stages, we evaluated data from the Tumor Registry Salzburg, which records all cancer cases diagnosed in the federal state of Salzburg, Austria. Secondary objectives were changes in nodal status and the influence of age and urban or rural residence on stage distribution.

METHODS

Female patients resident in the federal state of Salzburg with a first diagnosis of breast cancer in 2010-2022 were included. For the main objectives, patients aged 45-69 years with known tumor stages were evaluated. Age-standardized incidence rates were compared between 2010-2013 and 2016-2019 by normal approximation of Poisson rates and stage distributions by ordinal logistic regression.

RESULTS

The distribution of stages 0-IV did not differ significantly between 2010-2013 and 2016-2019 (P = 0.380). The percentage of stage IV breast cancer decreased numerically from 9.4-4.5% (P = 0.141). No statistically significant differences between early stages (0-I), advanced stages (II-IV, P = 0. 524) and between lymph node negative and positive cases (P = 0.538) were detected. Neither age nor urban/rural residence had a substantial influence on tumor stage. Interestingly, the breast cancer incidence rates in Salzburg decreased nonsignificantly after the introduction of screening: annual 245.7 vs. 229.8 cases per 100,000 standard population (P = 0.483).

CONCLUSION

Our findings do not support the assumption that the introduction of the Austrian breast cancer screening program significantly reduced advanced stage breast cancer in the federal state of Salzburg compared to the opportunistic screening established before.

Evolution of an Artificial Intelligence-Powered Application for Mammography

Background: The implementation of radiological artificial intelligence (AI) solutions remains challenging due to limitations in existing testing methodologies. This study assesses the efficacy of a comprehensive methodology for performance testing and monitoring of commercial-grade mammographic AI models. Methods: We utilized a combination of retrospective and prospective multicenter approaches to evaluate a neural network based on the Faster R-CNN architecture with a ResNet-50 backbone, trained on a dataset of 3641 mammograms. The methodology encompassed functional and calibration testing, coupled with routine technical and clinical monitoring. Feedback from testers and radiologists was relayed to the developers, who made updates to the AI model. The test dataset comprised 112 medical organizations, representing 10 manufacturers of mammography equipment and encompassing 593,365 studies. The evaluation metrics included the area under the curve (AUC), accuracy, sensitivity, specificity, technical defects, and clinical assessment scores. Results: The results demonstrated significant enhancement in the AI model's performance through collaborative efforts among developers, testers, and radiologists. Notable improvements included functionality, diagnostic accuracy, and technical stability. Specifically, the AUC rose by 24.7% (from 0.73 to 0.91), the accuracy improved by 15.6% (from 0.77 to 0.89), sensitivity grew by 37.1% (from 0.62 to 0.85), and specificity increased by 10.7% (from 0.84 to 0.93). The average proportion of technical defects declined from 9.0% to 1.0%, while the clinical assessment score improved from 63.4 to 72.0. Following 2 years and 9 months of testing, the AI solution was integrated into the compulsory health insurance system. Conclusions: The multi-stage, lifecycle-based testing methodology demonstrated substantial potential in software enhancement and integration into clinical practice. Key elements of this methodology include robust functional and diagnostic requirements, continuous testing and updates, systematic feedback collection from testers and radiologists, and prospective monitoring.

A Review on Integrating Breast Cancer Clinical Data: A Unified Platform Perspective

OPINION STATEMENT

Integrating clinical datasets in breast cancer research emerges as a necessary tool for advancing our knowledge of the disease and enhancing patient outcomes. Synthesizing diverse datasets offers advantages, from facilitating evidence-based insights to enabling predictive analytics and precision medicine strategies. Crucially, effective integration of clinical datasets necessitates collaborative efforts, policy interventions, and technological advancements to elevate global standards of breast cancer care. This narrative review underscores the imperative and substantial benefits of dataset integration in advancing breast cancer research and optimizing patient management. First, integrating diverse datasets-encompassing patient demographics, tumor characteristics, treatment modalities, and clinical outcomes-can significantly enhance our understanding of the disease's complexities and treatment responses across diverse patient populations. Second, we suggest that regulatory approval processes should allow new treatments to be conditionally approved for patients who were not part of the initial trials. This approval would depend on evaluating how well these treatments perform in real-world situations before full approval is granted. Third, we emphasize the importance of incorporating high-quality real-world evidence into treatment guidelines to better inform patient counselling and optimize personalized treatment strategies.

Influence of Breast Density and Menopausal Status on Background Parenchymal Enhancement in Contrast-Enhanced Mammography: Insights from a Retrospective Analysis

Background: Contrast-enhanced mammography (CEM) has recently gained recognition as an effective alternative to breast magnetic resonance imaging (MRI) for assessing breast lesions, offering both morphological and functional imaging capabilities. However, the phenomenon of background parenchymal enhancement (BPE) remains a critical consideration, as it can affect the interpretation of images by obscuring or mimicking lesions. While the impact of BPE has been well-documented in MRI, limited data are available regarding the factors influencing BPE in CEM and its relationship with breast cancer (BC) characteristics. Materials: This retrospective study included 116 patients with confirmed invasive BC who underwent CEM prior to biopsy and surgery. Data collected included patient age, breast density, receptor status, tumor grading, and the Ki-67 proliferation index. BPE was evaluated by two radiologists using the 2022 ACR BI-RADS lexicon for CEM. Statistical analyses were conducted to assess the relationship between BPE, patient demographics, and tumor characteristics. Results: The study found a significant association between higher levels of BPE and specific patient characteristics. In particular, increased BPE was more commonly observed in patients with higher breast density (p < 0.001) and those who were pre-menopausal (p = 0.029). Among patients categorized under density level B, the majority exhibited minimal BPE, while those in categories C and D showed progressively higher levels of BPE, indicating a clear trend correlating higher breast density with increased enhancement. Additionally, pre-menopausal patients demonstrated a higher likelihood of moderate to marked BPE compared to post-menopausal patients. Despite these significant associations, the analysis did not reveal a meaningful correlation between BPE intensity and tumor subtypes (p = 0.77) or tumor grade (p = 0.73). The inter-reader agreement for BPE assessment was substantial, as indicated by a weighted kappa of 0.78 (95% CI: 0.68-0.89), demonstrating consistent evaluation between radiologists. Conclusions: These findings suggest that BPE in CEM is influenced by factors like breast density and age, aligning with patterns observed in MRI studies. However, BPE intensity was not associated with tumor subtypes or grades, indicating a poorer prognosis. These insights highlight the potential of BPE as a risk biomarker in preventive follow-up, particularly for patients with high breast density and pre-menopausal status. Further multicentric and prospective studies are needed to validate these results and deepen the understanding of BPE's role in CEM diagnostics.

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis

Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.

Intraoperative evaluation of surgical margins in breast cancer

Achieving clear resection margins at the time of lumpectomy is essential for optimal patient outcomes. Margin status is traditionally determined by pathologic evaluation of the specimen and often is difficult or impossible for the surgeon to definitively know at the time of surgery, resulting in the need for re-operation to obtain clear surgical margins. Numerous techniques have been investigated to enhance the accuracy of intraoperative margin and are reviewed in this manuscript.

Can Radiologists Replace Digital 2D Mammography with Synthetic 2D Mammography in Breast Cancer Screening and Diagnosis, or Are Both Still Needed?

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM's potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40-65 who underwent DM with DBT at King Khaled University Hospital from 2020-2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening.

Breast ultrasound knobology and the knobology of twinkling for marker detection

Breast ultrasound utilizes various scanning techniques to acquire optimal images for diagnostic evaluation. During interventional procedures, such as ultrasound-guided biopsies or preoperative localizations, knowledgeable and purposeful scanning adjustments are critical for successfully identifying the targeted mass or biopsy marker or clip. While most ultrasound scanning parameters are similar across different vendors, detailed descriptions specifically addressing the scanning parameters-often referred to as "knobology"- for breast ultrasound is at best limited in the literature. This review highlights ten key operator-controlled adjustments (including transducer selection, beam focusing, power, depth, gain and time gain compensation, harmonic imaging, spatial compounding, dynamic range, beam steering, and color Doppler) that significantly influence image quality in breast ultrasound. Each adjustment is accompanied by an "In practice" section providing examples and practical tips on implementation. The last topic discusses color Doppler which is generally used in breast ultrasound for evaluating the vascularity of a finding. Color Doppler, or more specifically, color Doppler twinkling, can be leveraged as a technique to detect certain breast biopsy markers that are challenging to detect by conventional B-mode ultrasound. While the cause of color Doppler twinkling is still under active investigation, twinkling is a clinically well-known, compelling ultrasound feature typically described with kidney stones. A step-by-step guide on how to use color Doppler twinkling to detect these markers is provided.

Exploring non-surgical alternatives for low to intermediate-grade in situ ductal carcinoma of the breast using vacuum-assisted excision: the VACIS protocol

Background

Surgery is still the standard treatment for breast lesions such as in situ ductal carcinoma (DCIS); however, its survival benefit is minimal, particularly for low-grade DCIS. Surgical complications and related depression status can adversely affect patients' quality of life. Approximately 25% of breast cancer (BC) cases are in situ forms, with DCIS making up 90% of these. Low and intermediate-grade DCIS often grow slowly and do not always progress clinically significant diseases. Identifying non-invasive lesions could help prevent overtreatment. In this context, new diagnostic tools like vacuum-assisted excision (VAE) could enhance the management of these conditions.

Methods

The prospective VACIS study explores the role of VAE in ensuring the absence of pathology at subsequent surgery and reducing the diagnostic underestimation of breast biopsies for microcalcifications. Patients with suspicious breast microcalcifications up to 15 mm, who are candidates for stereotactic biopsy, will be enrolled and randomised into two groups. The control group will complete the biopsy with typical sampling, aiming to collect some microcalcifications from the target, while the experimental group will focus on the complete removal of the biopsy target (confirmed by mammography on the biopsy table), followed by a second sequence of cleaning samples. Radiograms will confirm lesion removal. Pathologic outcomes at surgery will be compared between the groups, and the percentage of underestimation will be assessed. The sample size is calculated to be 70 patients per group, using statistical tests and multivariate logistic models to detect a significant difference in the absence of pathology. Data collected will include patient age, lesion characteristics, and details of the biopsy, pathology and surgery.

Discussion

Current surgical treatments for low-and sometimes intermediate-grade DCIS offer limited survival benefits and may hurt patients' quality of life due to surgery-related complications and associated depression. These lesions often grow slowly and might not become clinically significant, suggesting a need to avoid overtreatment. Improved diagnostics procedures, such as VAE, could help distinguish non-invasive from potentially invasive lesions, reduce biopsy underestimation, enable personalised management and optimise treatment strategies. This study hypothesises that VAE could be a viable alternative to surgery, capable of removing pathology during the biopsy procedure.

Clinical trial registration

Clinicaltrials.gov, identifier NCT05932758.

Advancing breast ultrasound diagnostics through hybrid deep learning models

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the "EfficientKNN" model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms-SGD, Adam, and RMSprop-with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.

Magnetic resonance imaging of the breast: Could it be used as a screening test?

OBJECTIVES

To investigate whether magnetic resonance imaging (MRI) best detects early malignancy in high-risk women.

METHODS

A retrospective, cross-sectional study, carried out at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, included 419 female breast cancer patients aged 16-84 years (mean age of 49). Data were collected from the radiological department's database to compare the MRI, ultrasound (US), and mammography results, with or without tissue biopsy.

RESULTS

In diagnosing benign versus malignant lesions, MRI showed significant agreement with tissue biopsy, with high sensitivity (70%) and specificity (87%); its positive predictive value (PPV) was 92% and negative predictive value (NPV) was 56%. While US has a PPV of 84% and NPV of 63%; with a sensitivity (79%) and specificity (71%). In patients without tissue biopsy, there was little difference between mammography and US compared with MRI results.

CONCLUSION

Magnetic resonance imaging is more effective than US and mammography for early detection of BC. It showed high sensitivity in detecting breast lesions and high specificity in characterizing their nature when correlated with pathological results. Ultrasound screening followed by MRI is suggested for undetected or suspected lesions. This will increase the breast lesion detection rate, reduce unneeded tissue biopsies, and enhance the disease's survival rate.

Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

Breast imaging in focus: A bibliometric overview of visual quality, modality innovations, and diagnostic performance

INTRODUCTION

Breast imaging plays a crucial role in the early detection and management of breast cancer, with visual quality, modality innovation and diagnostic performance being key factors in achieving accurate diagnoses and optimal patient outcomes. This paper presents a comprehensive bibliometric analysis of the literature on the three above elements focusing on breast imaging, aiming to uncover publication trends, identify influential works and authors, and highlight future research directions.

METHODS

We employed a methodical bibliometric approach, making use of Scopus and Web of Science (WoS) databases for gathering literatures. We planned our search strategy, concentrating on terms linked to "breast imaging," "image quality," and "diagnostic accuracy" to ensure a systematic examination of the subject. The enhanced search functions in these databases enabled us to narrow down and improve our findings, choosing only the articles, conference papers, and book sections that are most relevant. After conducting a thorough screening process to remove duplicates and evaluate significance, we utilized ScientoPy and VOSviewer software for an in-depth bibliometric analysis. This helped to explore trends in publications, patterns of citations, and thematic groups, giving us a better understanding of how the field has changed and where it currently stands. Our approach prioritized assessing methodological quality and bias in the studies we included, guaranteeing the reliability of our findings.

RESULTS

We reviewed 2984 relevant publications, revealing a consistent annual growth rate of 2.8% in breast imaging research, with the United States and Europe leading in contributions. The study found that advancements in radiological technologies and international collaboration are driving forces behind the field's expansion. Key subject areas such as 'Radiology, Nuclear Medicine, and Medical Imaging' dominated, underscoring their impact on diagnostic quality. Notable authors and institutions have been identified for their influential research, characterized by high citation metrics and significant scholarly impact.

CONCLUSION

The study shows a continuous increase in research on breast imaging, considered by new technologies and teamwork defining the present time. The assessment highlights a key move towards utilizing digital imaging methods and computational analysis, affecting the improvement of future diagnostic procedures and patients' results. The study highlights the importance of continued international collaborations to tackle the new barriers in breast imaging and make the most of technological progress.

IMPLICATIONS FOR PRACTICE

This study shows a focus on using interdisciplinary methods and cutting-edge technology in breast imaging to help healthcare professionals improve their performance and accuracy in diagnosis. Recognizing vital research and emerging trends should guide clinical guidelines, radiology training, and patient care plans to encourage the use of effective techniques and stimulate innovation in diagnostic approaches.

Breast Cancer Screening: Can We Justify Deescalation?

Novel breast cancer screening methods that detect greater numbers of occult (nonpalpable) tumors have been rapidly incorporated into clinical practice, with the aim of reducing mortality. Yet, tumor detection has never been validated as a proper surrogate outcome measure for breast cancer mortality. Moreover, the detection of greater numbers of occult cancers increases the risk of overdiagnosis, which refers to detection of tumors that pose no threat to life and would never have been detected in the absence of screening. With recent advances in breast cancer therapy, many cancers that were previously curable only if detected as occult tumors with mammography screening are perhaps now curable even when detected as small palpable tumors, thereby giving us an opportunity to deescalate screening and mitigate the risk of overdiagnosis. Thus, a randomized trial comparing screening mammography versus screening clinical breast examination (CBE), with breast cancer mortality as the endpoint, is now warranted. In such a trial, hand-held ultrasound might aid in the interpretation of screening CBE findings. In conclusion, recent improvements in breast cancer therapy provide the justification to assess the deescalation of breast cancer screening. See related article by Farber et al., p. 671.

Mucinous Carcinoma of the Breast: A Case Report

This case report presents the diagnostic journey of a 65-year-old female presenting with symptoms suggestive of breast pathology, ultimately diagnosed with mucinous carcinoma, following comprehensive clinical evaluation and histopathological confirmation. Initial assessments indicated a fibroadenoma; however, subsequent histopathological examination revealed mucinous carcinoma, highlighting the importance of histopathological confirmation in establishing definitive diagnoses. The case underscores the challenges in distinguishing between benign and malignant breast lesions based on clinical presentation and imaging findings alone. The multidisciplinary approach facilitated discussions regarding treatment options tailored to the patient's clinical and pathological characteristics. This case emphasizes the significance of a comprehensive diagnostic approach, integrating clinical evaluation, imaging studies, and histopathological analysis, in ensuring accurate diagnosis and guiding optimal management strategies for patients with breast cancer.

Contrast-enhanced mammography BI-RADS: a case-based approach to radiology reporting

Contrast-enhanced mammography (CEM) is a relatively recent diagnostic technique increasingly being utilized in clinical practice. Until recently, there was a lack of standardized reporting for CEM findings. However, this has changed with the publication of a supplement in the Breast Imaging Reporting and Data System (BI-RADS). A comprehensive understanding of CEM is essential for further enhancing its role in both screening and managing patients with breast malignancies. CEM can also be beneficial for problem-solving, improving the management of uncertain breast findings. Practitioners in this field should become more cognizant of how and when to employ this technique and interpret the various CEM findings. This paper aims to outline the key findings in the updated version of the BI-RADS specifically dedicated to CEM. Additionally, it will present some clinical cases commonly encountered in clinical practice.Critical relevance statement Standardized reporting and a thorough understanding of CEM findings are pivotal for advancing the role of CEM in screening and managing breast cancer patients. This standardization contributes significantly to integrating CEM as an essential component of daily clinical practice.Key points • A complete knowledge and understanding of the findings outlined in the new BI-RADS CEM are necessary for accurate reporting.• BI-RADS CEM supplement is intuitive and practical to use.• Standardization of the CEM findings enables more accurate patient management.

Breast cancer: Epidemiology, risk factors and screening

Breast cancer is a global health concern with a significant impact on the well-being of women. Worldwide, the past several decades have witnessed changes in the incidence and mortality of breast cancer. Additionally, epidemiological data reveal distinct geographic and demographic disparities globally. A range of modifiable and non-modifiable risk factors are established as being associated with an increased risk of developing breast cancer. This review discusses genetic, hormonal, behavioral, environmental, and breast-related risk factors. Screening plays a critical role in the effective management of breast cancer. Various screening modalities, including mammography, ultrasound, magnetic resonance imaging (MRI), and physical examination, have different applications, and a combination of these modalities is applied in practice. Current screening recommendations are based on factors including age and risk, with a significant emphasis on minimizing potential harms to achieve an optimal benefits-to-harms ratio. This review provides a comprehensive insight into the epidemiology, risk factors, and screening of breast cancer. Understanding these elements is crucial for improving breast cancer management and reducing its burden on affected individuals and healthcare systems.

Breast Imaging Physics in Mammography (Part II)

One of the most frequently detected neoplasms in women in Italy is breast cancer, for which high-sensitivity diagnostic techniques are essential for early diagnosis in order to minimize mortality rates. As addressed in Part I of this work, we have seen how conditions such as high glandular density or limitations related to mammographic sensitivity have driven the optimization of technology and the use of increasingly advanced and specific diagnostic methodologies. While the first part focused on analyzing the use of a mammography machine from a physical and dosimetric perspective, in this paper, we will examine other techniques commonly used in breast imaging: contrast-enhanced mammography, digital breast tomosynthesis, radio imaging, and include some notes on image processing. We will also explore the differences between these various techniques to provide a comprehensive overview of breast lesion detection techniques. We will examine the strengths and weaknesses of different diagnostic modalities and observe how, with the implementation of improvements over time, increasingly effective diagnoses can be achieved.

Breast Imaging Physics in Mammography (Part I)

Breast cancer is the most frequently diagnosed neoplasm in women in Italy. There are several risk factors, but thanks to screening and increased awareness, most breast cancers are diagnosed at an early stage when surgical treatment can most often be conservative and the adopted therapy is more effective. Regular screening is essential but advanced technology is needed to achieve quality diagnoses. Mammography is the gold standard for early detection of breast cancer. It is a specialized technique for detecting breast cancer and, thus, distinguishing normal tissue from cancerous breast tissue. Mammography techniques are based on physical principles: through the proper use of X-rays, the structures of different tissues can be observed. This first part of the paper attempts to explain the physical principles used in mammography. In particular, we will see how a mammogram is composed and what physical principles are used to obtain diagnostic images.