Search Results (2,811)

Deep learning has substantially advanced the automated classification and segmentation of breast ultrasound images. However, many existing methods do not fully exploit task correlations, which weakens information exchange and limits the delineation of fine structures. In addition, commonly used loss functions often fail to balance classification and segmentation objectives effectively. To address these issues, we propose N-Unet, a multi-task learning framework that combines adaptive optimization with feature-enhancement modules. Specifically, the Adaptive Multi-Task Loss (AMTL) dynamically balances the two task objectives to promote stable joint learning. The Adaptive Feature Fusion (AFF) and Cross-Level Attention Enhancement (CLAE) modules improve feature representation through multi-scale integration and semantic refinement. The Conditional Segmentation Boosting (CSB) module further refines segmentation outputs according to the classification result, improving inference-stage consistency. Together, these components form a unified multi-task framework with a shared encoder, a segmentation branch, and an integrated classification branch whose output further supports segmentation-consistency refinement. Experiments on the BUSI and BUS-UCLM datasets demonstrate the superiority of N-Unet. The model achieves classification accuracies of 96.54% on BUSI and 95.83% on BUS-UCLM, with corresponding Dice scores of 80.70% and 92.16%. It reaches this performance with only 8.95 M parameters and 14.74 GFLOPs, showing a favorable performance-efficiency trade-off. These results confirm the effectiveness of N-Unet and its robustness across the two BUS datasets studied here, supporting its potential for practical breast nodule assessment, while broader external generalization remains to be validated. Full article

Background: Radiologic–pathologic discordance remains an important concern owing to the absence of standardized guidelines. This systematic review aimed to summarize the prevalence of discordant benign outcomes, defined as suspicious imaging findings with benign biopsy histology insufficient to explain the imaging abnormality, and to quantify malignancy upgrades subsequent to additional tissue assessment. Methods: This review was conducted in accordance with PRISMA 2020 guidelines and was prospectively registered. Eligible studies reported primary patient-level or aggregated data on radiologic–pathologic correlations post-image-guided breast biopsy and provided extractable data on discordant benign prevalence and/or subsequent malignancy upgrades. Results: Twenty-three studies were included. Lesion-/biopsy-based cohorts focused on biopsied abnormalities for analysis. Twelve studies directly estimated discordant-benign prevalence, whereas 11 studies did not, as study designs were discrepant-only, lesion-defined, or excision-restricted. Unselected cohorts with a cohort-wide correlation reported 1.2–5.3% discordant benign prevalence for all biopsies. When restricted to excised lesions, the discordant benign ascertainment rate was 7.4%, representing an excision-ascertained subset rather than the cohort-wide prevalence. Using benign-biopsy denominators, the discordance rate was 1.5–19.2%. Malignancy upgrades among discordant benign lesions ranged from 0 to 100% in selected subsets; however, several clinically relevant cohorts reported representative rates of approximately 20–40%, with some high-risk cohorts exceeding 50%. Conclusions: Discordant benign biopsy results are rare in unselected biopsy populations but carry a clinically meaningful upgrade risk, which warrants structured radiologic–pathologic correlation and prompt diagnostic resolution through repeat sampling or excision. Improvements in comparability and management algorithms require standardized definitions, uniform denominators aligned with all biopsied lesions, and prospective multicenter designs. Full article

New anticancer therapeutic strategies, including targeting of iron dysregulation in affected cancer types and stages, are urgently needed to decrease the associated annual cancer death rate of about 10 million worldwide. Many tumours evade treatment and support metastatic potential by effluxing iron and upregulating antioxidant systems, leading to suppression of lipid peroxidation and ferroptotic cell death. Similarly, many tumours manipulate the tumour microenvironment (TME) by ensuring the continuous supply of iron. This involves phenotypic modulation of immune cells, including macrophages, neutrophils, regulatory T lymphocytes, and natural killer cells, as well as fibroblasts, contributing to immune evasion and tumour growth. In particular, tumour-associated macrophages (TAMs), which may account for about half of the tumour’s bulk, become progressively heavily loaded with iron and can be detected by magnetic resonance imaging (MRI) technologies. Clinically effective iron chelation therapy protocols in iron-overloaded conditions using the chelating drugs deferoxamine, deferasirox, and especially deferiprone can also potentially remove excess iron from TAMs and may decrease tumour malignancy. Deferiprone can also remove excess iron from iron-loaded renal cancer cells and potentially prevent metastasis in renal carcinoma. The anticancer potential of deferiprone has also been shown in other cancers, including iron removal in prostate cancer and through cancer stem cell inhibition in breast cancer. Many ongoing clinical trials using different drugs and experimental agents for inducing or modulating ferroptosis also support the translational potential of ferroptosis-based therapeutic strategies in selected categories of cancer patients. These advances highlight ferroptosis as a potential key metabolic vulnerability with relevance for treatment-resistant and metastatic tumours. Overall, iron chelation therapeutic approaches and ferroptosis-targeting may be considered for significant use as monotherapies or in combination with other anticancer drugs and could potentially improve therapeutic outcomes and limit disease progression and mortality in many cancers. Full article

Background/Objectives: Circumscribed oval, hypoechoic masses are common on screening breast ultrasound (US), and the vast majority are benign. Triple-receptor negative invasive breast cancer (TNBC) can appear similar, resulting in both human and artificial intelligence (AI) interpretive errors. Purpose: We sought to improve AI performance in distinguishing common benign masses from TNBC through a retrospective model refinement and validation study. Materials and Methods: In an Institutional Review Board-approved HIPAA-compliant protocol, from five academic medical centers, orthogonal ultrasound images of 1771 breast masses 2 cm or smaller were acquired, consisting of cysts, complicated cysts, other benign, and malignancies. Cases were randomized, controlling for lesion class, site, and patient age, with 1446 (including 402, 27.8%, malignancies) used for training and 325 (including 95, 29.2% malignancies) for validation using Koios DS^® (decision support, KDS) software version 2.0. A breast imaging radiologist from each center reviewed images and recorded BI-RADS features and assessment. Demographics, symptoms, and pathology or at least one-year follow-up was recorded. The KDS score was evaluated standalone and in combination with BI-RADS using logistic regression and ROC analysis with focus on specificity at sensitivity of 98%. Results: In training, KDS standalone performed comparably to BI-RADS, and significantly improved BI-RADS malignancy risk prediction (p < 0.001). The 98%–sensitivity threshold for combined KDS + BI-RADS was estimated and kept fixed during validation. In validation, KDS standalone performed similar to BI-RADS with AUC = 0.97 (CI: 0.95–0.98) versus 0.95 (p = 0.22), with sensitivity of 98% (93/95, CI: 95–100%) for both and specificity of 70.9% (163/230, CI: 65.0–76.7%) for KDS versus 63.9% for BI-RADS (147/230, p = 0.10). Combining KDS + BIRADS significantly improved overall performance (AUC 0.98, p < 0.001) and specificity (74.4%, 171/230, p < 0.001) while maintaining sensitivity at 98% (93/95). Conclusions: While KDS alone should not replace BI-RADS, when used in combination with BI-RADS, it can significantly improve specificity for highly accurate (98% sensitivity) triaging management of masses representative of those seen on screening US. Full article

Tumor-associated macrophages (TAMs) are among the most abundant immune cell populations in breast cancer and have emerged as central regulators of tumor progression, metastatic dissemination, immune evasion, and therapeutic resistance. While TAMs were historically described using a simplified M1/M2 polarization framework, accumulating evidence indicates that TAMs in breast cancer comprise a continuum of phenotypic and functional states shaped by ontogeny (tissue-resident vs monocyte-derived), spatial localization (including hypoxic, perivascular, and invasive niches), tumor-intrinsic programs, and therapy-induced selective pressures. In breast cancer, mechanistic studies integrating lineage tracing, intravital imaging, single-cell and spatial profiling, and clinical analyses have established that TAMs actively coordinate rate-limiting steps of the metastatic cascade. These include promotion of angiogenesis and vascular permeability, orchestration of tumor cell invasion and TMEM-mediated intravasation, facilitation of metastatic seeding and niche formation, and suppression of anti-tumor immunity. TAMs also critically influence therapeutic response by modulating chemotherapy efficacy and limiting the activity of immune checkpoint blockade. Therapeutic strategies targeting TAMs in breast cancer have evolved from depletion approaches (CSF1/CSF1R blockade) to inhibition of monocyte recruitment (CCL2/CCR2 axis), functional reprogramming (CD40 agonism, PI3Kγ inhibition), and macrophage-directed checkpoint modulation (CD47–SIRPα axis). Early clinical studies demonstrate clear pharmacodynamic activity but highlight the need for context-specific and combination-based approaches. This review focuses on TAM biology in breast cancer progression and metastasis, synthesizing key mechanistic and translational evidence and proposing a framework in which spatially and functionally defined macrophage states act as rate-limiting regulators of dissemination and therapy response. We further outline principles for rational TAM-targeting strategies that integrate tumor stage, metastatic niche, and treatment context. Full article

Background/Objectives: While human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is pivotal for breast cancer management, its reliance on additional tissue processing beyond routine H&E staining remains a clinical burden. Although virtual staining offers a potential solution, current methods often fail to explicitly account for HER2 score-specific expression patterns. To address this gap, we developed a score-aware framework designed for the precise generation of virtual HER2 IHC images. Methods: We introduce the non-contrastive multi-task (NCMT) framework, which integrates negative-free patch alignment, style–content constraints, and auxiliary HER2 score supervision for high-fidelity H&E-to-IHC translation. For rigorous evaluation, the model was validated on the BCI dataset, utilizing an official split of 3896 training and 977 independent test images derived from 51 whole-slide images. Results: NCMT demonstrated superior virtual staining performance, achieving a Fréchet Inception Distance (FID) of 38.8, a Kernel Inception Distance (KID) of 5.6, and an average Perceptual Hash Value (PHV) of 0.439. In downstream HER2 scoring tasks, while virtual IHC images alone yielded an accuracy of 83.01%, the fusion of H&E and virtual IHC further elevated performance to 97.85% accuracy and a 98.23% F1 score. These findings suggest that our framework effectively preserves diagnostic features while providing complementary information to H&E-based morphological analysis. Conclusions: NCMT enables HER2 score-aware virtual IHC generation from H&E and can serve as a complementary tool for HER2 assessment in digital pathology. Full article

Background: This study evaluated the performance of a commercial offline adaptive radiotherapy system for systematic monitoring of breast cancer treatment with nodal irradiation using helical tomotherapy. Methods: Thirty patients treated for invasive unilateral breast carcinoma were analysed. For each patient, three megavoltage CT scans acquired at the first, middle, and last treatment sessions were processed through the PreciseART (Accuray, US) offline ART workflow. Automatically deformed structures were compared with manually delineated reference structures. Geometric accuracy was assessed using the Dice similarity coefficient (DSC), Hausdorff distance (HD95), mean distance to agreement (MDA), and barycentre distance (BD). The dosimetric parameters included D2% and V95% for targets and Dmean/Dmax/V20Gy for organs at risk. Results: Median DSCs exceeded 0.9 for the CTVbreast, PTVbreast, heart, and ipsilateral lung and were above 0.8 for the remaining structures, except the CTVn and oesophagus. Dosimetric differences between deformed and reference structures were within 5% for D2% across all targets and for V95% of the CTVbreast and PTVbreast in 90% of the sessions. The ipsilateral lung V20Gy differed by less than 5% in more than 90% of the sessions. Larger deviations (up to 10%) were observed for the nodal PTVs and mean heart dose, while the greatest inconsistencies were found for the oesophagus and spinal canal. Conclusions: The evaluated offline ART system demonstrates sufficient accuracy for automated monitoring of breast and lung structures. However, cautious interpretation remains necessary for nodal targets, heart, and oesophagus dosimetry prior to clinical implementation. Full article

Background: Progesterone receptor (PR) status plays an important role in guiding hormone therapy decisions in breast cancer. In current practice, PR expression is assessed manually from immunohistochemistry (IHC) slides, which can be time-consuming and may vary between pathologists. This study aims to develop an automated and interpretable framework for PR-IHC analysis to improve consistency and efficiency. Methods: In this work, we developed an AI-assisted pipeline that combines nuclei segmentation, classification, and scoring for PR-IHC images. A fine-tuned Cellpose model was used to segment individual nuclei. The segmented nuclei were then analyzed using a DAB intensity-based approach to classify them into four categories: negative, weak, moderate, and strong. These results were further combined to generate Allred scores. The system was evaluated on 250 PR-IHC images with annotations provided by expert pathologists. Results: The framework achieved strong segmentation performance (F1-score = 0.85, IoU = 0.74) and high classification accuracy (macro F1-score = 0.95). The method also performed well when applied to ER-IHC images without additional retraining. Conclusions: The proposed framework provides a reliable and interpretable approach for automated PR-IHC scoring. It helps reduce manual effort, improves consistency in evaluation, and shows potential for practical use in digital pathology settings. Full article

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0 $\mathsf{\mu }\mathrm{g}$ of iron, below the estimated 4.8 $\mathsf{\mu }\mathrm{g}$ sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications. Full article

Breast reconstruction represents an integral component of contemporary breast cancer management, with substantial impact on patients’ psychological well-being, body image, and overall quality of life. Given the profound symbolic and personal significance of the breast, mastectomy—whether total or partial—extends beyond oncologic resection and may result in considerable aesthetic, functional, and psychosocial consequences. For this reason, reconstructive planning should be incorporated into the initial multidisciplinary treatment strategy while ensuring that oncologic safety and adjuvant therapies are never compromised. Breast reconstruction may be achieved using autologous tissue, implant-based techniques, or a combination of both approaches. Each method carries specific advantages, limitations, and potential complications and must be tailored to the individual patient’s oncologic status, anatomy, and expectations. This article provides a historical overview of the evolution of breast cancer treatment and reconstructive techniques. It further examines the principles, benefits, and challenges associated with different reconstructive modalities, highlighting key considerations in clinical decision-making and long-term outcomes. Full article

Objective: We evaluated whether a deep-learning model could predict the response to neoadjuvant chemotherapy (NAC) in breast cancer using the pre-treatment B-mode ultrasound. Methods: This retrospective study included 245 female patients (253 lesions) treated with NAC between 2017 and 2019. Lesions were categorized as complete response (CR; 103) or non-CR (150) based on postoperative pathology. We trained ResNet18-based models using pre-treatment B-mode ultrasound images (Image) and clinical features. Three training strategies were evaluated: training from scratch (SC); transfer learning (TL); and domain-specific pretraining (USP). Predictive performance was assessed using descriptive statistics. Results: The best-performing model (USP Image) achieved 0.76 accuracy (specificity: 0.80; sensitivity: 0.72), significantly outperforming all other models, including those that used additional clinical features ($p<0.05$). USP improved performance across most model types compared to SC and TL, highlighting the value of domain-specific pretraining. Clinical features added value with SC or TL, but not with USP, suggesting that pretrained models can extract the most relevant information directly from images. Grad-CAM analysis revealed that non-CR predictions focused on the tumor and posterior shadowing—features linked to chemoresistant subtypes. CR predictions focused mainly on more heterogeneous, peritumoral regions. Conclusion: This finding underscores ultrasound’s potential as a low-cost, accessible tool for predictive oncology in personalized, AI-driven treatment planning. Full article

The development of realistic breast phantoms is critical for the evaluation of imaging systems and quantitative image analysis methods. In this work, breast samples derived from the same digital model were produced using 3D printing technology and evaluated for structural similarity and reproducibility. Four independently manufactured phantoms were imaged using mammography and breast tomosynthesis. Radiomic features were extracted from regions of interest in order to assess inter-phantom variability. The results showed very good agreement between the four printed phantoms. Most first-order and GLCM radiomic features exhibited very low inter-phantom variability, indicating consistent structural and intensity characteristics. Neighborhood-based texture features showed slightly higher variability, reflecting their sensitivity to local structural differences. Fractal and power spectrum analyses also confirmed the high structural similarity of the phantoms. These results indicate that the proposed manufacturing approach can produce reproducible breast imaging phantoms suitable for mammography and tomosynthesis imaging studies, with potential applications in imaging system evaluation and radiomic research. Full article

Breast mastitis is a common condition that can be found during clinical practice, challenging the clinician, who must reach the correct diagnosis among the many differentials, to properly treat the underlying pathology. In this review, we aim to provide clinicians and radiologists with an overview of the various forms of mastitis, focusing on clinical presentation, etiological subtypes, imaging appearances across modalities (e.g., ultrasound, mammography/tomosynthesis, contrast enhanced techniques, MRI), related complications, and the typical imaging takeaways. Our goal is also to provide tools for the correct differential diagnosis between various forms of mastitis, breast cancer and other inflammatory breast pathologies. A computerized literature search using PubMed and Google Scholar was performed by authors, entering various keywords (e.g., “mastitis”, “breast infections”, “breast abscess”, “breast cancer mimickers”, “lactational mastitis”, “non lactational mastitis”, “mastitis imaging”, “rare forms of mastitis”). Articles published between 2002 and 2025 were taken into consideration. The authors selected various eligible studies, scientific articles and extracted data to cover the whole spectrum of mastitis clinical presentation and underlying pathology. Authors divided the mastitis spectrum into “lactational” and “non-lactational” forms. Between the second group, periductal mastitis, idiopathic granulomatous mastitis, and rarer forms are taken into consideration. Our review has several limitations: it is a narrative and not systematic review and has limited generalizability of rare subtypes because of the case report driven evidence, heterogeneity of selected studies and potential selection bias. It supplies imaging from various clinical cases, which can be useful to familiarize with the pathology spectrum. In conclusion, breast mastitis is a challenge for breast radiologists and clinicians, familiarity with this condition is crucial to make a correct differential diagnosis. Further studies are needed on rarer subtypes. Full article

Breast-conserving therapy, consisting of lumpectomy followed by adjuvant radiotherapy, is the standard of care for early-stage breast cancer, providing oncologic outcomes equivalent to mastectomy while preserving breast anatomy and quality of life. Radiotherapy remains a cornerstone of treatment across disease stages, significantly reducing local recurrence rates and improving long-term survival. Advances in radiotherapy techniques—including conventional fractionation, hypofractionation, tumor-bed boost delivery, and regional nodal irradiation—have optimized oncologic efficacy while inducing a broad spectrum of time-dependent morphological changes in breast tissue. Accurate imaging surveillance is therefore essential to distinguish expected post-radiotherapy changes from tumor recurrence and to avoid unnecessary diagnostic or therapeutic interventions. This review provides a comprehensive overview of contemporary breast radiotherapy protocols, their impact on post-treatment imaging appearances, and current recommendations for imaging surveillance. Characteristic findings across mammography, ultrasound, magnetic resonance imaging, and nuclear medicine modalities are discussed, with emphasis on their temporal evolution from acute inflammatory changes to chronic fibrosis, fat necrosis, and architectural distortion. Recognition of these imaging patterns, together with integration of radiotherapy-related parameters into image interpretation, is crucial for accurate diagnosis, early detection of recurrence, and informed clinical management of breast cancer survivors. Full article