Breast Hemangiomas: Imaging Features With Histopathology Correlation.

Breast hemangiomas are rare benign vascular lesions. In a previously performed review of approximately 10,000 breast surgical pathology results, roughly 0.15% (15/~10,000) were hemangiomas. Hemangiomas are more frequent in women and have a documented age distribution of 1.5 to 82 years. They are most often subcutaneous or subdermal and anterior to the anterior mammary fascia but may rarely be seen in the pectoralis muscles or chest wall. On imaging, breast hemangiomas typically present as oval or round masses, often measuring less than 2.5 cm, with circumscribed or mostly circumscribed, focally microlobulated margins, equal or high density on mammography, and variable echogenicity on US. Calcifications, including phleboliths, can be seen. Color Doppler US often shows hypovascularity or avascularity. MRI appearance can vary, although hemangiomas are generally T2 hyperintense and T1 hypointense with variable enhancement. Pathologic findings vary by subtype, which include perilobular, capillary, cavernous, and venous hemangiomas. If core biopsy pathology results are benign, without atypia, and concordant with imaging and clinical findings, surgical excision is not routinely indicated. Because of histopathologic overlap with well-differentiated or low-grade angiosarcomas, surgical excision may be necessary for definitive diagnosis. Findings that are more common with angiosarcomas include size greater than 2 cm, hypervascularity on Doppler US, irregular shape, and invasive growth pattern.

Breast cancer risk prediction using machine learning: a systematic review.

Background: Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies. Objective and methods: This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers. Results: A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI. Conclusion: This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.

Ductal carcinoma in situ within a fibroadenoma: A case report and review of literature.

BACKGROUND: Fibroadenoma (FA) is the most common tumor found in young women, although it can occur in any age group. Ductal carcinoma in situ (DCIS) that is confined in a FA is rare; it is most frequently reported as an incidental finding. CASE SUMMARY: We report a case of DCIS within a FA in a 46-year-old female without cancer-related personal and family histories. The patient was diagnosed with a breast conglomerate of nodules and was followed for 1 year. In the current control image study, we found suspicious microcalcification, as a new finding, within one of the nodules. Consequently, a core biopsy of the tumor, which appeared hypoechoic, oval, and circumscribed, was performed. The pathological diagnosis was ductal carcinoma in situ within a fibroepithelial lesion. The patient underwent breast-conserving surgery and received radiotherapy as well as endocrine therapy (tamoxifen). CONCLUSION: We recommend a multidisciplinary approach for adequate treatment and follow-up.

Missed screening mammography appointments: patient sociodemographic characteristics and mammography completion after one year.

OBJECTIVE: Patients who miss screening mammogram (SM) appointments without notifying the healthcare system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience SM no-shows at a community health center and whether and when the missed exams are completed. METHODS: We included patients with SM appointments at a community health center between 1/1/2021-12/31/2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancellation, completed), and dates of completed SMs after no-show appointments with ≥ 1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes. RESULTS: Of 6,159 patients, 12.1% (743/6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P<.05). Patients with no-shows more often had: primary language other than English (32% [238/743] versus 26.7% [1,265/4,741]), race and ethnicity other than White non-Hispanic (42.3% [314/743] versus 33.6% [1,595/4,742]), Medicaid/means-tested insurance (62.0% [461/743] versus 34.4% [1,629/4,742]), and higher poverty ZCTAs (19.5% [145/743] versus 14.1% [670/4,742]). Independent predictors of no-shows were: Black/non-Hispanic race and ethnicity (aOR, 1.52; 95% CI, 1.12-2.07; P=.007), Medicaid/means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P<.001), and higher poverty ZCTAs (aOR, 1.76; 95% CI, 1.14-2.72; P=.011). At one-year follow-up, 40.7% (302/743) of patients with no-shows had not completed SM. DISCUSSION: SM no-shows is a health equity issue where socioeconomically disadvantaged and racial and ethnic minority patients are more likely to experience missed appointments and continued delays in SM completion.

Communication channels of breast cancer screening awareness campaigns among women presenting for mammography in Ghana.

OBJECTIVE: The channels and content of communication play an integral role in creating breast cancer screening awareness. Although breast cancer screening awareness campaigns are increasing in Ghana, no study has been conducted to investigate the communication channels used by these campaigns. This study aimed to identify the most effective source of breast cancer screening awareness information among women presenting for mammography in Ghana. METHODS: Ethical approval was sought prior to data collection. A cross-sectional quantitative approach was adopted for the study and involved 192 women who visited two mammography centers in October 2020 for mammography screening. A self-administered closed-ended questionnaire was used for data collection. Descriptive and inferential statistics were carried out using the Statistical Package for Social Sciences (SPSS) version 26. RESULTS: A total of 192 responses were obtained. 72 (37.5%) participants had Diploma/HND/Degree education, with 105 (54.7%) of them being traders/non-professionals. All participants had heard of mammography screening/examination prior to this study. Mass media was the most common source of information on mammography screening [86 (44.8%)], of which radio was the highest subcategory [34 (39.5%)]. Moreover, women presenting for mammography in Ghana demonstrated a high level of knowledge of breast cancer screening. DISCUSSION: Mass media is the most common source of information on breast cancer screening awareness in Ghana and has the potential to positively impact sensitization programmes by reaching out to more women. There is a need to engage the Ghanaian population using mass media and health facilities to maximize the impact of breast cancer screening awareness campaigns.

Assessing breast arterial calcification in mammograms and its implications for atherosclerotic cardiovascular disease risk.

PURPOSE: Breast arterial calcifications (BAC) are incidentally observed on mammograms, yet their implications remain unclear. We investigated lifestyle, reproductive, and cardiovascular determinants of BAC in women undergoing mammography screening. Further, we investigated the relationship between BAC, coronary arterial calcifications (CAC) and estimated 10-year atherosclerotic cardiovascular (ASCVD) risk. METHODS: In this cross-sectional study, we obtained reproductive history and CVD risk factors from 215 women aged 18 or older who underwent mammography and cardiac computed tomographic angiography (CCTA) within a 2-year period between 2007 and 2017 at hospital. BAC was categorized as binary (present/absent) and semi-quantitatively (mild, moderate, severe). CAC was determined using the Agatston method and recorded as binary (present/absent). Adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated, accounting for age as a confounding factor. ASCVD risk over a 10-year period was calculated using the Pooled Cohort Risk Equations. RESULTS: Older age, systolic and diastolic blood pressures, higher parity, and younger age at first birth (≤28 years) were significantly associated with greater odds of BAC. Women with both BAC and CAC had the highest estimated 10-year risk of ASCVD (13.30 %). Those with only BAC (8.80 %), only CAC (5.80 %), and no BAC or CAC (4.40 %) had lower estimated 10-year risks of ASCVD. No association was detected between presence of BAC and CAC. CONCLUSIONS: These findings support the hypothesis that BAC on a screening mammogram may help to identify women at potentially increased risk of future cardiovascular disease without additional cost and radiation exposure.

Systematic review and meta-analysis of prognostic characteristics for breast cancers in populations with digital versus film mammography indicate the transition may have increased both early detection and overdiagnosis.

OBJECTIVE: Film mammography has replaced digital mammography in breast screening programs globally. This led to a small increase in the rate of detection, but whether the detection of clinically important cancers increased is uncertain. We aimed to assess the impact on tumour characteristics of screen-detected and interval breast cancers. STUDY DESIGN AND SETTING: We searched seven databases from inception to 08 October 2023 for publications comparing film and digital mammography within the same population of asymptomatic women at population (average) risk of breast cancer. We recorded reported tumour characteristics and assessed risk of bias using the ROBINS-I tool. We synthesized results using meta-analyses of random effects. RESULTS: Eighteen studies were included in the analysis from 8 countries, including 11,592,225 screening examinations (8,117,781 film; 3,474,444 digital). There were no differences in tumour size, morphology, grade, node status, receptor status, or stage in the pooled differences for screen-detected and interval invasive cancer tumour characteristics. There were statistically significant increases in screen-detected DCIS across all grades: 0.05 (0.00-0.11), 0.14 (0.05-0.22), and 0.19 (0.05-0.33) per 1,000 screens for low, intermediate, and high grade DCIS respectively. There were similar (non-statistically significant) increases in screen-detected invasive cancer across all grades. CONCLUSION: The increased detection of all grades of DCIS and invasive cancer may indicate both increased early detection of more aggressive disease and increased overdiagnosis. FUNDING: Australian National Health and Medical Research Council and the National Breast Cancer Foundation. REGISTRATION: PROSPERO 2017:CRD42017070601.

Breast percent density changes in digital mammography pre- and post-radiotherapy.

INTRODUCTION: Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. METHODS: This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density. CONCLUSIONS: This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

Long-term Mammography Screening Trends and Predictors of Return to Screening after the COVID-19 Pandemic: Results from a Statewide Registry.

Purpose To evaluate long-term trends in mammography screening rates and identify sociodemographic and breast cancer risk characteristics associated with return to screening after the COVID-19 pandemic. Materials and Methods In this retrospective study, statewide screening mammography data of 222 384 female individuals aged 40 years or older (mean age, 58.8 years ± 11.7 [SD]) from the Vermont Breast Cancer Surveillance System were evaluated to generate descriptive statistics and Joinpoint models to characterize screening patterns during 2000-2022. Log-binomial regression models estimated associations of sociodemographic and risk characteristics with post-COVID-19 pandemic return to screening. Results The proportion of female individuals in Vermont aged 50-74 years with a screening mammogram obtained in the previous 2 years declined from a prepandemic level of 61.3% (95% CI: 61.1%, 61.6%) in 2019 to 56.0% (95% CI: 55.7%, 56.3%) in 2021 before rebounding to 60.7% (95% CI: 60.4%, 61.0%) in 2022. Screening adherence in 2022 remained substantially lower than that observed during the 2007-2010 apex of screening adherence (66.1%-67.0%). Joinpoint models estimated an annual percent change of -1.1% (95% CI: -1.5%, -0.8%) during 2010-2022. Among the cohort of 95 644 individuals screened during January 2018-March 2020, the probability of returning to screening during 2020-2022 varied by age (eg, risk ratio [RR] = 0.94 [95% CI: 0.93, 0.95] for age 40-44 vs age 60-64 years), race and ethnicity (RR = 0.84 [95% CI: 0.78, 0.90] for Black vs White individuals), education (RR = 0.84 [95% CI: 0.81, 0.86] for less than high school degree vs college degree), and by 5-year breast cancer risk (RR = 1.06 [95% CI: 1.04, 1.08] for very high vs average risk). Conclusion Despite a rebound to near prepandemic levels, Vermont mammography screening rates have steadily declined since 2010, with certain sociodemographic groups less likely to return to screening after the pandemic. Keywords: Mammography, Breast, Health Policy and Practice, Neoplasms-Primary, Epidemiology, Screening Supplemental material is available for this article. © RSNA, 2024.

AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ.

OBJECTIVES: To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. METHODS: Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0-100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS. RESULTS: Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, p = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, p = 0.023 and OR: 5.190, p < 0.001, respectively), higher AI-CAD score (OR: 1.009, p = 0.007), AI-CAD score ≥ 50% (OR: 1.960, p = 0.017), and AI-CAD score ≥ 75% (OR: 2.306, p = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, p = 0.035), and higher AI-CAD score (OR: 1.008, p = 0.047) were significant predictors of invasive upgrade. CONCLUSION: The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of ≥ 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy. CRITICAL RELEVANCE STATEMENT: Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment. KEY POINTS: • Predicting ductal carcinoma in situ upgrade is important, yet there is a lack of conclusive non-invasive biomarkers. • AI-CAD scores-raw numbers, ≥ 50%, and ≥ 75%-predicted ductal carcinoma in situ upgrade independently. • Quantitative AI-CAD results may help predict ductal carcinoma in situ upgrade and guide patient management.

MAM-E: Mammographic Synthetic Image Generation with Diffusion Models.

Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high quality of the generated images, and their relatively less complex training process compared with Generative Adversarial Networks. Still, the implementation of such models in the medical domain remains at an early stage. In this work, we propose exploring the use of diffusion models for the generation of high-quality, full-field digital mammograms using state-of-the-art conditional diffusion pipelines. Additionally, we propose using stable diffusion models for the inpainting of synthetic mass-like lesions on healthy mammograms. We introduce MAM-E, a pipeline of generative models for high-quality mammography synthesis controlled by a text prompt and capable of generating synthetic mass-like lesions on specific regions of the breast. Finally, we provide quantitative and qualitative assessment of the generated images and easy-to-use graphical user interfaces for mammography synthesis.

Lead-Time Corrected Effect on Breast Cancer Survival in Germany by Mode of Detection.

(1) Background: Screen-detected breast cancer patients tend to have better survival than patients diagnosed with symptomatic cancer. The main driver of improved survival in screen-detected cancer is detection at earlier stage. An important bias is introduced by lead time, i.e., the time span by which the diagnosis has been advanced by screening. We examine whether there is a remaining survival difference that could be attributable to mode of detection, for example, because of higher quality of care. (2) Methods: Women with a breast cancer (BC) diagnosis in 2000-2022 were included from a population-based cancer registry from Schleswig-Holstein, Germany, which also registers the mode of cancer detection. Mammography screening was available from 2005 onwards. We compared the survival for BC detected by screening with symptomatic BC detection using Kaplan-Meier, unadjusted Cox regressions, and Cox regressions adjusted for age, grading, and UICC stage. Correction for lead time bias was carried out by assuming an exponential distribution of the period during which the tumor is asymptomatic but screen-detectable (sojourn time). We used a common estimate and two recently published estimates of sojourn times. (3) Results: The analysis included 32,169 women. Survival for symptomatic BC was lower than for screen-detected BC (hazard ratio (HR): 0.23, 95% confidence interval (CI): 0.21-0.25). Adjustment for prognostic factors and lead time bias with the commonly used sojourn time resulted in an HR of 0.84 (CI: 0.75-0.94). Using different sojourn times resulted in an HR of 0.73 to 0.90. (4) Conclusions: Survival for symptomatic BC was only one quarter of screen-detected tumors, which is obviously biased. After adjustment for lead-time bias and prognostic variables, including UICC stage, survival was 27% to 10% better for screen-detected BC, which might be attributed to BC screening. Although this result fits quite well with published results for other countries with BC screening, further sources for residual confounding (e.g., self-selection) cannot be ruled out.

A systematic review and meta-analysis of English language online patient education materials in breast cancer: Is readability the only story?

BACKGROUND: Online patient education materials (OPEMs) are an increasingly popular resource for women seeking information about breast cancer. The AMA recommends written patient material to be at or below a 6th grade level to meet the general public's health literacy. Metrics such as quality, understandability, and actionability also heavily influence the usability of health information, and thus should be evaluated alongside readability. PURPOSE: A systematic review and meta-analysis was conducted to determine: 1) Average readability scores and reporting methodologies of breast cancer readability studies; and 2) Inclusion frequency of additional health literacy-associated metrics. MATERIALS AND METHODS: A registered systematic review and meta-analysis was conducted in Ovid MEDLINE, Web of Science, Embase.com, CENTRAL via Ovid, and ClinicalTrials.gov in June 2022 in adherence with the PRISMA 2020 statement. Eligible studies performed readability analyses on English-language breast cancer-related OPEMs. Study characteristics, readability data, and reporting of non-readability health literacy metrics were extracted. Meta-analysis estimates were derived from generalized linear mixed modeling. RESULTS: The meta-analysis included 30 studies yielding 4462 OPEMs. Overall, average readability was 11.81 (95% CI [11.14, 12.49]), with a significant difference (p < 0.001) when grouped by OPEM categories. Commercial organizations had the highest average readability at 12.2 [11.3,13.0]; non-profit organizations had one of the lowest at 11.3 [10.6,12.0]. Readability also varied by index, with New Fog, Lexile, and FORCAST having the lowest average scores (9.4 [8.6, 10.3], 10.4 [10.0, 10.8], and 10.7 [10.2, 11.1], respectively). Only 57% of studies calculated average readability with more than two indices. Only 60% of studies assessed other OPEM metrics associated with health literacy. CONCLUSION: Average readability of breast cancer OPEMs is nearly double the AMA's recommended 6th grade level. Readability and other health literacy-associated metrics are inconsistently reported in the current literature. Standardization of future readability studies, with a focus on holistic evaluation of patient materials, may aid shared decision-making and be critical to increased screening rates and breast cancer awareness.

Phantom-based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM.

BACKGROUND: Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. METHODS: Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers' strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). RESULTS: For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772-1.668) for DM, 1.257 mGy (0.971-1.863) for DBT, 1.280 mGy (0.937-1.878) for LE-CEM, and 0.630 mGy (0.397-0.713) for HE-CEM. Medians CNRs were 14.2 (7.8-20.2) for DM, 4.91 (2.58-7.20) for a single projection in DBT, 11.9 (8.0-18.2) for LE-CEM, and 5.2 (3.6-9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. CONCLUSIONS: The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers' strategies, with potential implications in radiation dose and image quality in clinical settings. RELEVANCE STATEMENT: The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. KEY POINTS: • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the "optimal" exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34.

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects.

PURPOSE: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. METHODS: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches. RESULTS: DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption. CONCLUSIONS: AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP-L) grades 1-2 or high MP (MP-H) grades 3-5) in patients with ER-positive/HER2-negative breast cancer. MATERIALS AND METHODS: In this retrospective study, 265 breast cancer patients were randomly allocated into training and test sets (used for models training and testing, respectively) at a 4:1 ratio. Deep learning models, based on the pre-trained ResNet34 model and initially fine-tuned for identifying breast cancer, were trained using low-energy and subtracted CESM images. The predicted results served as deep learning features for the deep learning-based model. Clinical-pathological features, including age, progesterone receptor (PR) status, estrogen receptor (ER) status, Ki67 expression levels, and neutrophil-to-lymphocyte ratio, were used for the clinical model. All these features contributed to the nomogram. Feature selection was performed through univariate analysis. Logistic regression models were developed and chosen using a stepwise selection method. The deep learning-based and clinical models, along with the nomogram, were evaluated using precision-recall curves, receiver operating characteristic (ROC) curves, specificity, recall, accuracy, negative predictive value, positive predictive value (PPV), balanced accuracy, F1-score, and decision curve analysis (DCA). RESULTS: The nomogram demonstrated considerable predictive ability, with higher area under the ROC curve (0.95, P < 0.05), accuracy (0.94), specificity (0.98), PPV (0.89), and precision (0.89) compared to the deep learning-based and clinical models. In DCA, the nomogram showed substantial clinical value in assisting breast cancer treatment decisions, exhibiting a higher net benefit than the other models. CONCLUSION: The nomogram, integrating CESM deep learning with clinical-pathological features, proved valuable for predicting NAC response in patients with ER-positive/HER2-negative breast cancer. Nomogram outperformed deep learning-based and clinical models.

Arterial Calcification Disappearance in Breast Imaging: A Key Indicator for Transition to Invasive Ductal Carcinoma.

A woman in her 70s, initially suspected of having fibroadenoma due to a well-defined mass in her breast, underwent regular mammography and ultrasound screenings. Over several years, no appreciable alterations in the mass were observed, maintaining the fibroadenoma diagnosis. However, in the fourth year, an ultrasound indicated slight enlargement and peripheral irregularities in the mass, even though the mammography images at that time showed no alterations. Interestingly, mammography images over time showed the gradual disappearance of previously observed arterial calcification around the mass. Pathological examination eventually identified the mass as invasive ductal carcinoma. Although the patient had breast tissue arterial calcification typical of atherosclerosis, none was present around the tumor-associated arteries. This case highlights the importance of monitoring arterial calcification changes in mammography, suggesting that they are crucial indicators in breast cancer diagnosis, beyond observing size and shape alterations.

Diagnostic value of qualitative and quantitative enhancement parameters on contrast-enhanced mammography.

PURPOSE: To determine whether qualitative and quantitative enhancement parameters obtained from contrast-enhanced mammography (CEM) can be used in predicting malignancy. METHODS: After review board approval, consecutive 136 suspicious lesions with definite diagnosis were retrospectively analyzed on CEM. Acquisition was routinely started with craniocaudal view and ended with mediolateral oblique view of the affected breast. Lesion conspicuity (low, moderate, high), internal enhancement pattern (homogeneous, heterogeneous, rim), contrast-to-noise ratio (CNR), percentage of signal difference (PSD) and relative enhancement from early to late view were analyzed. PSD and relative enhancements were used to determine patterns of descending, steady or ascending enhancements. Receiver operating characteristic analysis, Cohen's kappa statistics and Spearman correlation tests were used. RESULTS: There were 29 benign and 107 malignant lesions. 64% of the malignant lesions exhibited high conspicuity compared to 14% of the benign lesions (P < 0.001). CNR values were higher in malignant lesions compared to benign ones (P ≤ 0.004). CNR from early view yielded 82% sensitivity, 72% specificity and PSD yielded 79% sensitivity, 65% specificity. Descending pattern and rim enhancement observed in 44% and 21% of breast cancers, respectively, and both provided 96% positive predictive value for malignancy. CONCLUSION: Diagnostic accuracy of quantitative parameters was higher than that of qualitative parameters. High CNR, rim enhancement, and descending pattern were features commonly seen in malignant lesions, while low CNR, homogeneous enhancement, and ascending pattern were commonly seen in benign lesions.