MV-Swin-T: MAMMOGRAM CLASSIFICATION WITH MULTI-VIEW SWIN TRANSFORMER.

Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammography exam, leveraging the inherent correlations in these views to effectively detect tumors. Acknowledging the significance of multi-view analysis, some studies have introduced methods that independently process mammogram views, either through distinct convolutional branches or simple fusion strategies, inadvertently leading to a loss of crucial inter-view correlations. In this paper, we propose an innovative multi-view network exclusively based on transformers to address challenges in mammographic image classification. Our approach introduces a novel shifted window-based dynamic attention block, facilitating the effective integration of multi-view information and promoting the coherent transfer of this information between views at the spatial feature map level. Furthermore, we conduct a comprehensive comparative analysis of the performance and effectiveness of transformer-based models under diverse settings, employing the CBIS-DDSM and Vin-Dr Mammo datasets. Our code is publicly available at https://github.com/prithuls/MV-Swin-T.

Diagnostic performance of tomosynthesis plus synthetic mammography versus full-field digital mammography with or without tomosynthesis in breast cancer screening: A systematic review and meta-analysis.

Digital breast tomosynthesis (DBT) with full-field digital mammography (FFDM) exposes women to a higher radiation dose. A synthetic 2D mammogram (S2D) is a two-dimensional image constructed from DBT. We aim to evaluate the S2D performance when used alone or combined with DBT compared to FFDM alone or with DBT. Studies were included if they recruited screening participants and reported on S2D performance. Studies were excluded if they included symptomatic patients, imaging was for diagnostic purposes, or if participants had a breast cancer history. Meta-analyses for cancer detection rates (CDR) and Specificities were conducted where available. Differences in the performance of imaging modalities were calculated within individual studies, and these were pooled by meta-analysis. Out of 3241 records identified, 17 studies were included in the review and 13 in the meta-analysis. The estimated combined difference in CDRs per thousand among individual studies that reported on DBT plus S2D vs. FFDM and those reporting on DBT plus S2D versus DBT plus FFDM was 2.03 (95% CI 0.81-3.25) and - 0.15 (95% CI -1.17 to 0.86), respectively. The estimated difference in percent specificities was 1.13 (95% CI -0.06 to 2.31) in studies comparing DBT plus S2D and FFDM. In studies comparing DBT plus S2D and DBT plus FFDM, the estimated difference in specificities was 1.08 (95% CI 0.59-1.56). DBT plus S2D showed comparable accuracy to FFDM plus DPT and improved cancer detection to FFDM alone. Integrating S2D with DBT in breast cancer screening is safe and preserves performance.

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search.

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a Population-based Screening Sample.

OBJECTIVE: To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample. METHODS: In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics. Up to 5 years of follow-up examination results were evaluated in cases to establish benignity. The algorithm sorted cases into groups of "suspicious" and "low suspicion." A theoretical workload reduction was calculated by subtracting cases triaged as "low suspicion" from the sample. RESULTS: At the default 93% sensitivity setting, there was significant improvement (P <.05) in the following triage simulation mean performance measures compared with actual outcome: 45.5% improvement in recall rate (13.4% to 7.3%; 95% CI, 6.2-8.3), 119% improvement in positive predictive value (PPV) 1 (5.3% to 11.6%; 95% CI, 9.96-13.4), 28.5% improvement in PPV2 (24.6% to 31.6%; 95% CI, 24.8-39.1), 20% improvement in sensitivity (83.3% to 100%; 95% CI, 100-100), and 7.2% improvement in specificity (87.2% to 93.5%; 95% CI, 92.4-94.5). A theoretical 62.5% workload reduction was possible. At the ultrahigh 99% sensitivity setting, a theoretical 27% workload reduction was possible. No cancers were missed by the algorithm at either sensitivity. CONCLUSION: Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.

ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process.

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)". This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

A Rare Case of Concurrent Lupus Mastitis and Sarcoidosis in a 62-Year-Old Female.

Systemic lupus erythematosus (SLE) and sarcoidosis are two of the most well-recognized, chronically diagnosed conditions in the United States, with a plethora of known multisystem manifestations. With regard to breast pathology, lupus mastitis is a relatively uncommon manifestation of SLE, commonly involving both the mammary gland and subcutaneous soft tissues of the breast. Sarcoidosis in the breast is a similarly, exceedingly rare manifestation of this multi-system disorder, classically presenting with non-caseating granulomas. Both present with non-specific mammographic and sonographic features. We present a 62-year-old female with known diagnosis of discoid lupus and Graves' disease who presented initially with an abnormal screening mammogram, ultimately undergoing mammographic work-up and subsequent biopsy demonstrating lupus mastitis, including vasculitis, panniculitis, and fibrosis with chronic inflammation. The patient was also found to have small non-caseating granulomas, some in a perivascular distribution, classically seen in sarcoidosis. Given the rarity of both manifestations, our case explores the coexistence of these autoimmune processes and this atypical presentation.

Patient- vs Technologist-Controlled Mammography Compression: A Prospective Comparative Study of Patient Discomfort and Breast Compression Thickness.

OBJECTIVE: We assess whether mammographic patient-assisted compression (PAC) has an impact on breast compression thickness and patient discomfort compared with technologist-assisted compression (TAC). METHODS: A total of 382 female patients between ages 40 and 90 years undergoing screening mammography from February 2020 to June 2021 were recruited via informational pamphlet to participate in this IRB-approved study. Patients without prior baseline mammograms were excluded. The participating patients were randomly assigned to the PAC or TAC study group. Pre- and postmammogram surveys assessed expected pain and experienced pain, respectively, using a 100-mm visual analogue scale and the State-Trait Anxiety Inventory. Breast compression thickness values from the most recent mammogram were compared with the patient's recent prior mammogram. RESULTS: Between the 2 groups, there was no significant difference between the expected level of pain prior to the mammogram (P = .97). While both study groups reported a lower level of experienced pain than was expected, the difference was greater for the PAC group (P <.0001). Additionally, the PAC group reported significantly lower experienced pain during mammography compared with the TAC group (P = .014). The correlation of trait/state anxiety scores with pre- and postmammogram pain scores was weak among the groups. Lastly, the mean breast compression thickness values for standard screening mammographic views showed no significant difference in the PAC group when compared with the patient's prior mammogram. CONCLUSION: Involving patients in compression reduces their pain independent of the patient's state anxiety during mammography while having no effect on breast compression thickness. Implementing PAC could improve the mammography experience.

Barriers and facilitators to breast cancer screening among high-risk women: a qualitative study.

PURPOSE: Women with greater than 20-25% lifetime breast cancer risk are recommended to have breast cancer screening with annual mammogram and supplemental breast MRI. However, few women follow these screening recommendations. The objective of this study was to identify barriers and facilitators of screening among women at high risk for breast cancer, guided by the Health Services Utilization Model (HSUM). METHODS: Unaffected high-risk women (N=63) completed semi-structured qualitative interviews exploring their experiences with breast cancer screening. Interviews were audio recorded, transcribed verbatim, and analyzed using a combined deductive and inductive approach. RESULTS: Most participants (84%) had received a screening mammogram; fewer (33%) had received a screening breast MRI. Only 14% had received neither screening. In line with the HSUM, qualitative analysis identified predisposing factors, enabling factors, and need factors associated with receipt of breast cancer screening. Enabling factors - including financial burden, logistic barriers, social support, and care coordination - were most frequently discussed. Predisposing factors included knowledge, health beliefs, and self-advocacy. Need factors included healthcare provider recommendation, family history of breast cancer, and personal medical history. Although HSUM themes were consistent for both mammography and breast MRI, participants did highlight several important differences in barriers and facilitators between the two screening modalities. CONCLUSION: Barriers and enabling factors associated with supplemental screening for high-risk women represent possible intervention targets. Future research is needed to develop and test multilevel interventions targeting these factors, with the ultimate goal of increasing access to supplemental screening for high-risk women.

Interpretation of immediately released health information: Informing patient medical education in breast oncology.

BACKGROUND: The Cures Act mandated immediately released health information. In this study, we investigated patient comprehension of mammography reports and the utility of online resources to aid report interpretation. METHODS: Patients who received a normal mammogram from February to April 2022 were invited to complete semi-structured interviews paired with health literacy questionnaires to assess patient's report comprehension before and after internet search. RESULTS: Thirteen selected patients via purposeful sampling completed interviews. Most patients described their initial understanding of the mammography report as "good" and improved to between "good" and "very good" after an internet search. Patients suggested "a little column on the side" for medical terminology, "an extra prompt" for making an appointment, or a recommendation for "good sites" to improve mammography reports. CONCLUSION: Patients varied in their ability to independently interpret medical reports and seek additional resources. While online resources marginally improved patient understanding, actionable and clear resources are needed.

Mobile breast screening services in Australia: A qualitative exploration of perceptions and experiences among rural and remote women aged ≥75 years.

OBJECTIVE: This qualitative descriptive study draws on data collected from a sub-sample of 15 women participating in a national study (n = 60) exploring the breast cancer screening motivations and behaviours of women aged ≥75 years. The study aimed to understand why women living in rural and remote areas might continue accessing mobile breast cancer screening despite being outside the targeted age range. SETTING: Settings ranged from large towns to very remote communities (according to Monash Modified Model (MMM) classification 3-7) where BreastScreen Australia mobile screening services were available. PARTICIPANTS: Interview data from 15 women aged ≥75 years living in rural and remote locations who had used mobile screening services was utilised for this study. DESIGN: In-depth individual interviews were conducted via telephone or online platform (Zoom). These were transcribed verbatim and imported into NVivo software to enable thematic analysis to identify key themes. RESULTS: Many women aged ≥75 years in rural and remote areas expressed clear intentions to continue breast cancer screening, despite no longer being invited to do so. They perceived great value in the mobile service and were highly appreciative for it yet acknowledged limited sources of information about the process of ongoing screening. CONCLUSION: Few women in rural and remote areas had discussed ongoing breast cancer screening with their general practitioner (GP). More information is required to inform women about the risks and benefits of ongoing screening. Without an invitation to attend screening rural women reported difficulty in knowing when the service would be available. Ongoing notification of the availability of mobile services for women aged ≥75 years in rural areas is recommended.

Radiologic-Pathologic Correlation: Is There an Association Between Contrast-Enhanced Mammography Imaging Features and Molecular Subtypes of Breast Cancer?

OBJECTIVE: This study aims to assess the correlation between imaging features of contrast-enhanced mammography (CEM) and molecular subtypes of breast cancer. METHODS: This is a retrospective single-institution study of patients who underwent CEM from December 2019 to August 2023. Each patient had at least one histologically proven invasive breast cancer with a core biopsy performed. Patients with a history of breast cancer treatment and lesions not entirely included in the CEM images were excluded. The images were interpreted using the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) lexicon for CEM, published in 2022. Different imaging features, including the presence of calcifications, architectural distortion, non-mass enhancement, mass morphology, internal enhancement pattern, the extent of enhancement, and lesion conspicuity, were analyzed. The molecular subtypes were studied as dichotomous variables, including luminal A, luminal B, HER2, and basal-like. The association between the imaging features and molecular subtypes was analyzed with a Fisher's exact test. Statistical significance was assumed when the p-value was <0.05. RESULTS: A total of 31 patients with 36 malignant lesions were included in this study. Sixteen lesions (44.4%) were luminal A, four lesions (11.1%) were luminal B, 10 lesions (27.8%) were HER2, and six (16.7%) were basal-like subtypes. The presence of calcifications was associated with the HER2 subtype (p=0.024). Rim-enhancement on recombined images was associated with a basal-like subtype (p=0.001). Heterogeneous enhancement on recombined images was associated with non-basal-like breast cancer (p=0.027). No statistically significant correlation was found between other analyzed CEM imaging features and molecular subtypes. CONCLUSION: CEM imaging features, including the presence of calcifications and certain internal enhancement patterns, were correlated with distinguishing breast cancer molecular subtypes and thus may further expand the role of CEM.

Metaplastic Breast Carcinoma: A Rare Presentation in a Young Female With Double Hormone Receptor-Positive Tumor.

Metaplastic breast carcinoma (MBC) represents a rare subtype of breast cancer, characterized by poor prognostic indicators that have been recently identified. Clinical and radiological presentations often mimic other breast cancer types, necessitating immunohistochemistry (IHC) for accurate diagnosis. In this study, we report a case involving a 31-year-old female presenting with a painless, fixed, non-inflammatory mass in the left breast, which was confirmed as MBC. Treatment encompassed lumpectomy, chemotherapy, radiotherapy, and subsequent hormonal therapy. Understanding this rarely reported yet aggressive form of breast cancer holds significant importance for clinicians, enabling them to promptly establish a diagnosis and implement effective management strategies.

Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis.

Pneumomastia following subdermal upper arm helium contouring treatment.

A case is presented of extensive pneumomastia seen on a screening mammogram of an asymptomatic patient who had helium plasma treatment 2 weeks earlier for flabby upper arms. Contribution: Rare complications of subcutaneous emphysema, following helium plasma treatment, have been discussed to highlight that such emphysema is usually self-limiting.

Radiological response of non-specific granulomatous mastitis to anti-tuberculous treatment: A single centre study in an endemic nation.

BACKGROUND: Tuberculous mastitis (TBM), is an uncommon form of extra-pulmonary tuberculosis. Clinical and radiological overlap of tuberculous mastitis with malignancy and other granulomatous conditions, along with its paucibacillary nature, make it a diagnostic challenge. In our study, we aim to assess the radiological response of microbiologically negative granulomatous mastitis cases to anti-tuberculous treatment (ATT) in an endemic country. METHODS: Eighty-seven cases demonstrating granulomatous lesions on breast biopsy were identified. Of these, 49 patients who were treated with ATT and had at least two serial ultrasound follow-ups were included in our study. Mammogram and ultrasound were used for initial imaging. Subsequently, ultrasound was used for serial follow-up. Mantoux skin test, acid fast staining and histological examination of tissue sample were the other investigations used. RESULTS: Radiologically, on ultrasound, well-circumscribed hypoechoic masses were noted in 18 patients, followed by ill-defined collections with tubular extensions in 15 cases, abscesses in 8, and a focal heterogeneity in 8 patients. Following ATT, 17 patients showed radiological resolution in 4 weeks, 18 of them at 3 months, and nine of them in 6 months. CONCLUSION: Excellent and prompt radiological response to ATT, indicates the need for a high degree of suspicion for tuberculous mastitis (TBM), in endemic countries, even though microbiological tests may turn out negative.

Computer-aided Detection and Diagnosis of Cancer Regions in Mammogram Images Using Resource-Efficient CNN Architecture.

AIM: The automatic computer-assisted mammogram classification system is important for women patients to detect and diagnose the cancer regions. In this work, the mammogram images are classified into three cases: healthy, benign and cancer, using the proposed Resource Efficient Convolutional Neural Network (RECNN architecture). METHODS: The proposed mammogram image classification system consists of Data Augmentation (DA) module and Spatial transformation module and CNN architecture with a segmentation module. The DA methods are used to increase the mammogram image count and Spatial Gabor Transform is used as the spatial transformation module for transforming the spatial pixels into spatial-frequency pixels. Then, the proposed RECNN architecture is used to perform the classification of mammogram images into healthy, benign and cancer cases. Further, the cancer mammogram images are diagnosed as either 'Early' or 'Severe' using the proposed RECNN architecture in this work. RESULTS: The proposed MCDS obtains 98.65% SeDR, 98.93% SpDR and 98.84% ADR for benign case mammogram images on DDSM dataset and also obtains 98.84% SeDR, 98.7% SpDR and 98.92% ADR for benign case mammogram images on DDSM dataset. The proposed MCDS obtains 98.94% SeDR, 98.86% SpDR and 98.96% ADR for benign case mammogram images on DDSM dataset and also obtains 98.89% SeDR, 98.88% SpDR and 99.03% ADR for benign case mammogram images on MIAS dataset. CONCLUSION: This proposed method is tested on the mammogram images from DDSM and MIAS datasets and the experimental results are compared with other similar mammogram classification works in this paper. Based on several performance evaluation measures, the experimental results show that MCDS outperforms the state-of-the-art methods currently used for the diagnosis and detection of mammography cancer.

Evaluating the features of breast lesions identified by bimodal breast examination: a real-world study.

Background: Several image-based diagnostic methods have been developed to examine the features of breast lesions among women, while the value of combining palpation imaging and ultrasound by a bimodal breast examination system is still unknown. Methods: A real-world study was conducted among 424 patients who visited Fujian Maternal and Child Health Hospital and Fujian Obstetrics and Gynecology Hospital, and used the Bimodal Breast Exam (BBE) systems which combines palpation imaging and ultrasound imaging. Among them, 97 patients had additional ultrasound, mammogram, or pathological examination. These patients were used to evaluate the consistency and efficacy of the BBE in interpreting the features of breast lesions as compared to results of ultrasound, mammogram, and pathological examinations. Results: The BBE system detected 1517 lesions with palpation imaging, 1126 lesions with ultrasound examination (950 solid lesions and 176 cysts), and 391 non mass lesions. Among them, 404 patients were diagnosed as benign and 20 were diagnosed as malignant tumor. However, 12, 9 and 4 cases were diagnosed as malignant tumors by ultrasound, mammogram and pathological examination, respectively. Compared with the integrative results of ultrasound, mammogram and pathology, the sensitivity of BBE is 55.6%, and the specificity is 90.9%, with a kappa coefficient of 0.387 (0.110, 0.665), indicating moderate consistency. Conclusions: In clinical practice, BBE can be used to evaluate features of breast lesions with a high specificity. The diagnostic efficacy is comparable to the integrative results of ultrasound, mammography, and pathological examination.

Upper Extremity Deep Vein Thrombosis in the Setting of Ductal Carcinoma In Situ: A Case Report.

Deep vein thrombosis (DVT) is a type of venous thromboembolism that usually involves a clot formation in the deep veins of the lower extremities. Its formation is linked to Virchow's Triad which factors in venous stasis, endothelial damage, and hypercoagulability. Venous stasis is the primary factor contributing to the development of DVT and it refers to varicosity, external pressure placed on the extremity, or immobilization due to bed rest or long flights. Clinical presentation of DVT depends on the extent and location of the thrombus with common signs including localized swelling, pain, warmth, and edema. The Wells criteria are typically applied to assess the likelihood of thrombus formation alongside D-dimer assay, ultrasound, or CT imaging. As previously mentioned, these mostly occur in the lower extremities. However, upper extremity DVT has been noted and has been linked to inherited issues with coagulation and autoimmune disorders. This report will discuss a case of left-arm DVT in a patient who underwent bilateral mastectomy with sentinel node biopsy for a diagnosis of ductal carcinoma in situ in the left breast.

A Case Report on Breast Cancer Following Mantle Radiation for Hodgkin Lymphoma: Screening and Management.

Hodgkin lymphoma survivors who received mantle radiation are at risk of developing secondary malignant neoplasms. There is no established recommended screening guideline for this population. We discuss the case of a patient with a history of Hodgkin lymphoma status post-mantle field radiation, thyroid cancer status post-thyroidectomy, and now breast cancer following mantle radiation. The risk of adverse effects from mantle field radiation is well documented and includes secondary cancers of the thyroid, breast, lung, and cardiovascular disease. Advances in technology have led to an international paradigm shift in the management of Hodgkin lymphoma to reduce the diameter and dose of radiation based on the patient's anatomy. However, there is no consensus regarding the optimal frequency or modality of breast cancer screening in patients with Hodgkin lymphoma status post-mantle radiation who are now in remission. We discuss screening methods for this population, which has a high risk of developing breast cancer, and emphasize the need for personalized medicine.

The Usefulness of the Breast Density Assessment Application Used by Breast Radiologists.

Breast density determined by breast radiologists and also automatically estimated by applications has been widely investigated. However, no study has yet clarified whether the use of these applications by breast radiologists improves reading efficacy. Therefore, this study aimed to assess the usefulness of applications when used by breast radiologists. A Breast Density Assessment application (App) developed by Konica Minolta, Inc. (Tokyo, Japan) was used. Independent and sequential tests were conducted to assess the usefulness of the concurrent- and second-look modes. Fifty and 100 cases were evaluated using sequential and independent tests, respectively. Each dataset was configured based on the evaluation by an expert breast radiologist who developed the Japanese guidelines for breast density. Nine breast radiologists evaluated the mammary gland content ratio and breast density; the inter-observer and expert-to-observer variability were calculated. The time required to complete the experiments was also recorded. The inter-observer variability was significant with the App, as revealed by the independent test. The use of the App significantly improved the agreement between the responses of the observers for the mammary gland content ratio and those of the expert by 6.6% and led to a reduction of 186.9 seconds in the average time required by the observers to evaluate 100 cases. However, the results of the sequential test did not suggest the effectiveness of the App. These findings suggest that the concurrent use of the App improves reading efficiency.