Implications of breast density for breast cancer screening.

ABSTRACT: Extremely dense breasts can be an independent risk factor for breast cancer. A new FDA rule requires that patients be notified of their breast density and the possible benefits of additional imaging to screen for breast cancer. Clinicians should be cognizant of the data about breast cancer risk, breast density, and recommendations to change screening techniques if patients, particularly premenopausal females, have extremely dense breasts but no other known risk factors.

Attention based UNet model for breast cancer segmentation using BUSI dataset.

Breast cancer, a prevalent and life-threatening disease, necessitates early detection for the effective intervention and the improved patient health outcomes. This paper focuses on the critical problem of identifying breast cancer using a model called Attention U-Net. The model is utilized on the Breast Ultrasound Image Dataset (BUSI), comprising 780 breast images. The images are categorized into three distinct groups: 437 cases classified as benign, 210 cases classified as malignant, and 133 cases classified as normal. The proposed model leverages the attention-driven U-Net's encoder blocks to capture hierarchical features effectively. The model comprises four decoder blocks which is a pivotal component in the U-Net architecture, responsible for expanding the encoded feature representation obtained from the encoder block and for reconstructing spatial information. Four attention gates are incorporated strategically to enhance feature localization during decoding, showcasing a sophisticated design that facilitates accurate segmentation of breast tumors in ultrasound images. It displays its efficacy in accurately delineating and segregating tumor borders. The experimental findings demonstrate outstanding performance, achieving an overall accuracy of 0.98, precision of 0.97, recall of 0.90, and a dice score of 0.92. It demonstrates its effectiveness in precisely defining and separating tumor boundaries. This research aims to make automated breast cancer segmentation algorithms by emphasizing the importance of early detection in boosting diagnostic capabilities and enabling prompt and targeted medical interventions.

In Vivo Detection of Multidrug-Resistance Related Proteins in Locally Advanced Breast Cancer Using 99mTc-MIBI SPECT/CT Imaging: Correlation with Clinical Outcomes.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is widely used for treating locally advanced Breast cancer (LABC). However, development of multidrug resistance (MDR) is the main underlying factor for chemoresistance. Technetium-99m methoxyisobutylisonitrile (99mTc-MIBI) is a substrate for MDR. This study aimed to analyze the relationship between expression of MDR-related proteins (P-gp and Bcl-2) and 99mTc-MIBI uptake and retention in BC tumor cells, pathologic response to NACT, disease free survival (DFS) and overall survival (OS). METHODS: prospective analysis recruited 31 patients with LABC who received NACT between January 2019 and March 2020. 99mTc-MIBI planar and SPECT/CT imaging was conducted before and after NACT. Qualitative and quantitative analyses were performed, pre and post-NACT early and delayed lesion to non-lesion (LNL) ratios, and retention index (RI) of 99mTc-MIBI were calculated. Expression of P-gp and Bcl-2 in tumor cells was determined by immunohistochemistry. RESULTS: Quantitively, inter-reader ICC for SPECT/CT based quantification was consistently higher than that of planar images. Post-NACT LNL ratios were significantly higher in patients with pathologic persistent disease (PPD). A change in RI between pre- and post-NACT scans demonstrated a significant association with DFS with a hazard ratio of 0.7 (95%CI: 06-1.0). Qualitatively, SPECT/CT was significantly more accurate compared to planar imaging in identifying residual viable tumor (81% compared to 57%).  Her2neu positivity and high post-operative Bcl-2 and P-gp were associated with worse DFS. A significant association was found between increased expression of post-NACT Bcl-2 and PPD, advanced tumor stage and poor OS. CONCLUSION: 99mTc-MIBI SPECT/CT based qualitative evaluation of BC response to NACT is more accurate than planar imaging. Post-NACT MIBI retention is positively correlated with P-gp and Bcl-2 expression. 99mTc-MIBI SPECT/CT may predict MDR development. High post-NACT Bcl-2 expression is significantly associated with advanced tumor stage and OS. High post-NACT P-gp expression has a worse impact on pathologic response and DFS.

Application of Intelligent Response Fluorescent Probe in Breast Cancer.

As one of the leading cancers threatening women's lives and health, breast cancer is challenging to treat and often irreversible in advanced cases, highlighting the critical importance of early detection and intervention. In recent years, fluorescent probe technology, a revolutionary in vivo imaging tool, has gained attention in medical research for its ability to improve tumor visualization significantly. This review focuses on recent advances in intelligent, responsive fluorescent probes, particularly in the field of breast cancer, which are divided into five categories, near-infrared responsive, fluorescein-labeled, pH-responsive, redox-dependent, and enzyme-triggered fluorescent probes, each of which has a different value for application based on its unique biological response mechanism. In addition, this review also covers the strategy of combining fluorescent probes with various anti-tumor drugs, aiming to reveal the possibility of synergistic effects between the two in breast cancer treatment and provide a solid theoretical platform for the clinical translation of fluorescent probe technology, which is expected to promote the expansion of cancer treatment technology.

Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer.

Background/Objectives: Increasing evidence indicates that body composition can significantly influence prognosis in women with breast cancer. However, alterations in body composition, particularly among young women (<40 years), remain largely unknown and underexplored. This study aimed to investigate the relationship of computed tomography (CT)-derived body composition with mortality rates among young women recently diagnosed with breast cancer, identifying the best-correlated cutoff value. Methods: This is a bi-set cohort study with retrospective data collection. Women newly diagnosed with ductal invasive breast cancer, aged 20 to 40 years, treated in reference oncology units were included. Body composition was assessed using CT scans at the third lumbar vertebra (L3) level, including muscle and adipose compartments. The outcome of interest was the incidence of overall mortality. A maximally selected log-rank Cox-derived analysis was employed to assess the cutoffs associated with mortality. Results: A total of 192 women were included before any form of treatment (median age of 35 years, IQ range: 31-37). Overall mortality occurred in 12% of the females. Stages III-IV were the most frequent (69.5%). Patients who died had a significantly lower muscle area index. CT-derived muscle area was inversely associated with mortality. Each 1 cm2/m2 decrease in skeletal muscle index increased the mortality hazard by 9%. Higher values of adiposity compartments were independently associated with higher mortality. Conclusions: Our study highlights the predictive significance of skeletal muscle area and adipose tissue in predicting survival among young women recently diagnosed with breast cancer.

Disease extent according to baseline [18F]fluorodeoxyglucose PET/CT and molecular subtype: prediction of axillary treatment response after neoadjuvant systemic therapy for breast cancer.

BACKGROUND: Axillary disease extent according to baseline [18F]fluorodeoxyglucose PET/CT combined with pathological axillary treatment response has been proposed to guide de-escalation of axillary treatment for clinically node-positive breast cancer patients treated with neoadjuvant systemic therapy. The aim of this study was to assess whether axillary disease extent according to baseline [18F]fluorodeoxyglucose PET/CT and breast cancer molecular subtype are predictors of axillary pCR. METHODS: This study included clinically node-positive patients treated with neoadjuvant systemic therapy in the prospective Radioactive Iodine Seed placement in the Axilla with Sentinel lymph node biopsy ('RISAS') trial (NCT02800317) with baseline [18F]fluorodeoxyglucose PET/CT imaging available. The predictive value of axillary disease extent according to baseline [18F]fluorodeoxyglucose PET/CT and breast cancer molecular subtype to estimate axillary pCR was evaluated using logistic regression analysis. Discriminative ability is expressed using ORs with 95% confidence intervals. RESULTS: Overall, 185 patients were included, with an axillary pCR rate of 29.7%. The axillary pCR rate for patients with limited versus advanced baseline axillary disease according to [18F]fluorodeoxyglucose PET/CT was 31.9% versus 26.1% respectively. Axillary disease extent was not a significant predictor of axillary pCR (OR 0.75 (95% c.i. 0.38 to 1.46) (P = 0.404)). There were significant differences in axillary pCR rates between breast cancer molecular subtypes. The lowest probability (7%) was found for hormone receptor+/human epidermal growth factor receptor 2- tumours. Using this category as a reference group, significantly increased ORs of 14.82 for hormone receptor+/human epidermal growth factor receptor 2+ tumours, 40 for hormone receptor-/human epidermal growth factor receptor 2+ tumours, and 6.91 for triple-negative tumours were found (P < 0.001). CONCLUSION: Molecular subtype is a significant predictor of axillary pCR after neoadjuvant systemic therapy, whereas axillary disease extent according to baseline [18F]fluorodeoxyglucose PET/CT is not.

Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 × 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images.

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Initial experience with 3T breast MRI in Ukraine.

OBJECTIVE: Aim: To assess the initial results of using 3 Tesla contrast-enhanced breast magnetic resonance imaging in Ukraine. PATIENTS AND METHODS: Materials and Methods: Our study included 498 diagnostic breast magnetic resonance imaging performed in Neuromed medical center in Kyiv, between March 2020 and December 2022. Patients were positioned prone, with breasts suspended in a dedicated 7-channel bilateral breast coil. MR-images were acquired with the PHILIPS Achieva 3.0Tesla x-series scanner. All studies were made by standard protocol: localizer, morphological and dynamic studies were performed. RESULTS: Results: Our study revealed a statistically significant increase in problem-solving contrast-enhanced breast magnetic resonance examinations compared to other indications. Additionally, we observed a higher incidence of women with a greater amount of fibroglandular tissue (p-value<0.05). CONCLUSION: Conclusions: The utilization of 3Tesla contrast-enhanced breast magnetic resonance imaging has become prevalent in Ukraine as a problem-solving tool for inconclusive findings in ultrasound (US) or/and mammography (MG). It is particularly useful in preoperative local breast cancer staging for women with a significant amount of fibroglandular breast tissue. However, the implementation of breast magnetic resonance imaging in Ukraine is in its nascent stages and requires further investigation, especially in middle-income country settings.

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.

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task's performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model's performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework.

MR Imaging-Guided Focused Ultrasound for Breast Tumors.

Breast tumors remain a complex and prevalent health burden impacting millions of individuals worldwide. Challenges in treatment arise from the invasive nature of traditional surgery and, in malignancies, the complexity of treating metastatic disease. The development of noninvasive treatment alternatives is critical for improving patient outcomes and quality of life. This review aims to explore the advancements and applications of focused ultrasound (FUS) technology over the past 2 decades. FUS offers a promising noninvasive, nonionizing intervention strategy in breast tumors including primary breast cancer, fibroadenomas, and metastatic breast cancer.

Automated dose evaluation on daily cone-beam computed tomography for breast cancer patients.

BACKGROUND AND PURPOSE: Our goal was to develop a workflow to automatically evaluate delivered dose on daily cone beam computed tomography (CBCT) in all breast cancer patients to assess dosimetric impact of anatomical changes and guide decision-making for offline plan adaptation. MATERIALS AND METHODS: The workflow automatically processes the daily CBCTs of all breast cancer patients receiving local and locoregional radiotherapy. The planning-CT is registered to the CBCT to create a synthetic CT and propagate contours. A forward dose calculation is performed, and DVH parameters are extracted and printed in a report. We evaluated the workflow on a group level and in a subset of 30 patients on a patient-specific level, including comparison to clinical evaluation on additional planning-CT in 10 patients. RESULTS: 7454 fractions in 647 patients were analyzed over a period of seven months. Median breast clinical target volume V95% was ≥ 95 % for 97 % of the patients. The workflow would have provided useful additional insights for decision-making for the requirement of plan adaptation, based on debatable disagreement with the clinical decision in half of the cases with an additional planning-CT. The workflow also identified cases with suboptimal coverage not identified in the clinical procedure. CONCLUSION: We developed a fully automated workflow for dose evaluation on daily CBCT for local and locoregional breast radiotherapy. We have demonstrated its potential for aiding decision-making for plan adaptation in patients with changing anatomy and its capability to highlight patients that may receive suboptimal treatment and require closer clinical evaluation of treatment quality.

A hybrid features fusion-based framework for classification of breast micronodules using ultrasonography.

BACKGROUND: Breast cancer is one of the leading diseases worldwide. According to estimates by the National Breast Cancer Foundation, over 42,000 women are expected to die from this disease in 2024. OBJECTIVE: The prognosis of breast cancer depends on the early detection of breast micronodules and the ability to distinguish benign from malignant lesions. Ultrasonography is a crucial radiological imaging technique for diagnosing the illness because it allows for biopsy and lesion characterization. The user's level of experience and knowledge is vital since ultrasonographic diagnosis relies on the practitioner's expertise. Furthermore, computer-aided technologies significantly contribute by potentially reducing the workload of radiologists and enhancing their expertise, especially when combined with a large patient volume in a hospital setting. METHOD: This work describes the development of a hybrid CNN system for diagnosing benign and malignant breast cancer lesions. The models InceptionV3 and MobileNetV2 serve as the foundation for the hybrid framework. Features from these models are extracted and concatenated individually, resulting in a larger feature set. Finally, various classifiers are applied for the classification task. RESULTS: The model achieved the best results using the softmax classifier, with an accuracy of over 95%. CONCLUSION: Computer-aided diagnosis greatly assists radiologists and reduces their workload. Therefore, this research can serve as a foundation for other researchers to build clinical solutions.

WHO-recommended levels of physical activity in relation to mammographic breast density, mammographic tumor appearance, and mode of detection of breast cancer.

BACKGROUND: Despite known benefits of physical activity in reducing breast cancer risk, its impact on mammographic characteristics remain unclear and understudied. This study aimed to investigate associations between pre-diagnostic physical activity and mammographic features at breast cancer diagnosis, specifically mammographic breast density (MBD) and mammographic tumor appearance (MA), as well as mode of cancer detection (MoD). METHODS: Physical activity levels from study baseline (1991-1996) and mammographic information from the time of invasive breast cancer diagnosis (1991-2014) of 1116 women enrolled in the Malmö Diet and Cancer Study cohort were used. Duration and intensity of physical activity were assessed according to metabolic equivalent of task hours (MET-h) per week, or World Health Organization (WHO) guideline recommendations. MBD was dichotomized into low-moderate or high, MA into spiculated or non-spiculated tumors, and MoD into clinical or screening detection. Associations were investigated through logistic regression analyses providing odds ratios (OR) with 95% confidence intervals (CI) in crude and multivariable-adjusted models. RESULTS: In total, 32% of participants had high MBD at diagnosis, 37% had non-spiculated MA and 50% had clinical MoD. Overall, no association between physical activity and MBD was found with increasing MET-h/week or when comparing women who exceeded WHO guidelines to those subceeding recommendations (ORadj 1.24, 95% CI 0.78-1.98). Likewise, no differences in MA or MoD were observed across categories of physical activity. CONCLUSIONS: No associations were observed between pre-diagnostic physical activity and MBD, MA, or MoD at breast cancer diagnosis. While physical activity is an established breast cancer prevention strategy, it does not appear to modify mammographic characteristics or screening detection.

Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.

Breast cancer is the most common malignant tumor among women worldwide and remains one of the leading causes of death among women. Its incidence and mortality rates are continuously rising. In recent years, with the rapid advancement of deep learning (DL) technology, DL has demonstrated significant potential in breast cancer diagnosis, prognosis evaluation, and treatment response prediction. This paper reviews relevant research progress and applies DL models to image enhancement, segmentation, and classification based on large-scale datasets from TCGA and multiple centers. We employed foundational models such as ResNet50, Transformer, and Hover-net to investigate the performance of DL models in breast cancer diagnosis, treatment, and prognosis prediction. The results indicate that DL techniques have significantly improved diagnostic accuracy and efficiency, particularly in predicting breast cancer metastasis and clinical prognosis. Furthermore, the study emphasizes the crucial role of robust databases in developing highly generalizable models. Future research will focus on addressing challenges related to data management, model interpretability, and regulatory compliance, ultimately aiming to provide more precise clinical treatment and prognostic evaluation programs for breast cancer patients.