Search Results (695)

Background: Sclerosing adenosis (SA) is a benign, proliferative breast lesion that frequently mimics malignancy on multimodality imaging. The appropriate clinical management of SA diagnosed via core needle biopsy (CNB) remains a highly debated topic in breast oncology. This study aimed to evaluate the clinical, imaging, and histopathological characteristics of CNB-diagnosed SA-spectrum lesions and to determine the rate of, and factors associated with, pathological upgrade at the time of surgical excision in a symptomatic cohort. Methods: This retrospective, single-center study evaluated 34 symptomatic female patients who received a CNB diagnosis of SA. Patients were assessed using targeted ultrasound (US, n = 34), digital mammography (n = 19), and/or dynamic contrast-enhanced breast MRI (n = 17). Based on CNB findings, lesions were classified as isolated SA, complex/accompanied SA, or atypical SA. Patients subsequently underwent either definitive surgical excision or long-term imaging surveillance. Upgrades were defined as the presence of a high-risk B3 lesion not identified on CNB (Level 1) or overt malignancy, such as ductal carcinoma in situ or invasive carcinoma (Level 2), at final surgical pathology. Results: Twenty-one patients (61.8%) underwent surgical excision, while thirteen (38.2%) were managed with radiological follow-up (median surveillance 16 months, range 7–84). Within the surgical cohort, 9 of 21 patients (42.9%; 95% CI: 24.5–63.5%) demonstrated an upgrade: 6 (28.6%; 95% CI: 13.8–50.0%) to a B3-level lesion and 3 (14.3%; 95% CI: 5.0–34.6%) to malignancy. Notably, two of the three malignant upgrades originated from lesions initially classified as isolated SA without atypia on CNB. Due to the limited sample size, no single clinical or imaging variable (BI-RADS category, lesion size, or age) reached statistical significance as an independent predictor of upgrade (all p > 0.10). All patients managed with imaging surveillance remained radiologically stable. Conclusions: In stark contrast to the 1–2% upgrade rates traditionally reported in asymptomatic screening populations, symptomatic SA-spectrum lesions selected for surgical excision following CNB exhibited a substantially higher overall upgrade rate (42.9%) and malignant upgrade rate (14.3%) in our cohort, albeit with wide confidence intervals reflecting the modest surgical subgroup size. The occurrence of malignant upgrades from presumed isolated SA underscores the limitations of CNB sampling and highlights the necessity of multimodality imaging–pathology concordance. Because this estimate derives exclusively from patients already selected for surgery on the basis of clinical and imaging suspicion, it reflects the upgrade risk of this pre-selected, imaging-discordant subgroup and should not be extrapolated to the baseline risk of all CNB-diagnosed SA. Accordingly, these findings caution against extending non-operative management without further scrutiny to symptomatic SA cases showing a similar degree of imaging–pathology discordance: in such cohorts, surgical excision or large-volume vacuum-assisted excision may still merit consideration despite the absence of atypia on CNB, although this observation is drawn from a small, non-randomly selected surgical subgroup and should be interpreted with corresponding caution. Full article

Background/Objectives: The prognostic significance of young age in breast cancer (BC) remains controversial, with varying findings across studies. This study aimed to evaluate the imaging characteristics, pathological features, and survival outcomes of BC in young patients aged ≤ 35 years by dividing them into two groups and to compare outcomes between these subgroups to determine whether very young age alone is associated with worse prognosis. Methods: This retrospective study included patients aged ≤ 35 years with malignant breast lesions who underwent image-guided interventions at a single center between January 2011 and December 2024. Patients were divided into two groups: Group 1 (≤30 years) and Group 2 (31–35 years). Demographic, imaging, and pathological data, as well as survival outcomes, were analyzed. Results: A total of 34 patients (Group 1: n = 14, Group 2: n = 20) with malignant breast lesions underwent 40 image-guided procedures. No significant differences were observed between groups in imaging features, pathological characteristics, or pTNM stage distribution. Median follow-up was 68 months. One-year overall survival (OS) was 100% in both cohorts; 5-year OS rates were 100% and 92.3%, respectively, with no statistically significant difference (p = 0.784). Median event-free survival (EFS) was 60.5 months, also without a significant difference between groups (p = 0.897). None of the clinicopathological factors assessed were significantly associated with OS in this exploratory analysis. Conclusions: In women ≤ 35 years, tumor characteristics and patient outcomes were similar between those aged ≤ 30 and 31–35 years. OS and EFS were favorable in both groups, indicating that within the ≤35-year population studied, being ≤30 years old was not associated with worse outcomes than being 31–35 years old. Although advanced stage disease was more frequently observed in the younger group, this difference was not statistically significant. Full article

Background/Objectives: Early identification of breast cancer patients unlikely to benefit from neoadjuvant chemotherapy (NAC) remains a pressing clinical problem because ineffective therapy delays definitive surgery and exposes patients to unnecessary toxicity. Quantitative ultrasound (QUS) and shear wave elastography (SWE) probe complementary tissue properties—scatterer microstructure and mechanical stiffness—that may change before macroscopic tumor shrinkage. This study aimed to evaluate whether multi-parametric ultrasound (mpUS) radiomic kinetics, analyzed with a machine learning ensemble and interpreted with SHAP, could predict pathologic response to NAC. Methods: A prospective observational cohort enrolled 135 women with biopsy-proven stage II–III breast cancer treated with NAC within the multidisciplinary breast pathway shared between Vasile Goldis Western University of Arad and Victor Babes University of Medicine and Pharmacy Timisoara (Pius Brinzeu County Emergency Hospital). All patients underwent standardized QUS and SWE acquisitions at baseline, week 1, and week 3. Response was defined pathologically at surgery as residual cancer burden (RCB) class 0/I versus II/III. Group comparisons used Welch’s t-test, Mann–Whitney U, chi-square, and Fisher’s exact tests; correlations used Spearman’s rho. A stacked machine learning ensemble (four base learners—XGBoost, random forest, support vector machine, and L2-penalized logistic regression—combined by a separate second-stage logistic meta-learner) was trained with nested 10-fold cross-validation, bootstrap stability assessment, SHAP-based interpretability, and decision curve analysis. Results: Sixty patients (44.4%) were responders and 75 (55.6%) were non-responders. Responders showed greater week 3 increases in mid-band fit (3.4 ± 0.9 vs. 1.2 ± 0.8 dB, p < 0.001), entropy (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), and more pronounced SWE mean stiffness reduction (−44.1 ± 9.7 vs. −12.1 ± 8.6 kPa, p < 0.001). The stacked ensemble integrating clinical, QUS, and SWE kinetic features reached an AUC of 0.93 (95% CI 0.88–0.97) versus 0.71 for the clinical-only model (all reported performance figures represent internal cross-validation only). SHAP analysis identified Δ MBF and Δ entropy at week 3 as the dominant features, with high bootstrap stability. Conclusions: Multi-parametric ultrasound radiomic kinetics integrated through a machine learning ensemble may provide an interpretable early-response biomarker for NAC in breast cancer, pending external validation. Full article

Breast cancer is the most commonly diagnosed cancer among women worldwide and remains a leading cause of cancer-related mortality. The most frequent histological subtypes are invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), which demonstrate different biological behaviours and heterogeneous imaging features. ILC is more likely to produce subtle radiological findings such as architectural distortion or asymmetry, whereas IDC commonly presents as a well-defined mass with spiculated margins and associated calcifications. This retrospective cohort study was conducted at Sur Hospital, Oman, and included Omani female patients diagnosed with histologically confirmed IDC or ILC between 2017 and 2025. Mammographic and ultrasound features were reviewed in relation to age, breast density, tumour size, and BI-RADS classification. A total of 46 cases were included, of which 91.3% were IDC and 8.7% were ILC. The median age at diagnosis was 50 years, and the median tumour diameter was 3.0 cm. On mammography, masses with calcifications and architectural distortion were the most common findings. Ultrasound demonstrated predominantly irregular lesions with hypoechoic or heterogeneous echotexture. Most lesions were classified as BI-RADS IV or V. This study documents the mammographic and ultrasound patterns encountered in routine clinical practice. Because only four ILC cases were included, the ILC observations should be interpreted descriptively and should not be regarded as evidence of definitive subtype-specific differences. Full article

The 70-gene signature (70-GS; MammaPrint) assay is useful for prognosis assessment in HR+/HER2− early breast cancer, but limited accessibility motivates development of noninvasive alternatives. We retrospectively enrolled 219 women with preoperative grayscale ultrasound and 70-GS results, including a development cohort (n = 125), an internal validation cohort (n = 53), and a temporally independent validation cohort (n = 41). Radiomic features were extracted from manually delineated ROIs using PyRadiomics, and a radiomics score was derived after LASSO selection. Candidate radiomics-only, clinicopathologic-only, and full clinicoradiomic models were explored. To reduce overfitting, we selected a parsimonious model combining the radiomics score and Ki67 as the primary model. The simplified model achieved AUCs of 0.878, 0.816, and 0.831 in the development, internal validation, and temporally independent validation cohorts, respectively. In 1000 bootstrap resamples, the optimism-corrected AUC was 0.872 and the corrected calibration slope was 0.953. Adding the radiomics score to a Ki67-only model significantly improved model fit (likelihood-ratio chi-square = 17.14, df = 1, p < 0.001). An ultrasound radiomics and Ki67 model may provide a noninvasive reference for estimating MammaPrint risk categorization, but it should be considered only as a supportive adjunct and not as a replacement for genomic testing. Full article

Machine learning using supervised approaches has been widely applied to ultrasound image analysis. In contrast, reinforcement learning (RL), which is well suited for sequential decision-making, is underexplored. Ultrasound workflows involve sequential subtasks like image acquisition, quality assessment, summarization, and interpretation that can be integrated into RL frameworks. This scoping review examined RL applications in ultrasound. A comprehensive search was conducted in Scopus, PubMed, Embase, and MEDLINE for studies published between 2015 and 2026. Eligible studies used RL with clinical ultrasound data, and the data were summarized by application area, methods, and anatomical targets. The review also provides a brief overview of RL concepts as foundational knowledge for understanding various RL formulations used. From 326 records retrieved, 39 studies were included. Most studies used model-free deep RL, with value-based methods being the most common, particularly Deep Q-Network (DQN) and its variants, on retrospective data across diverse anatomical targets, with breast, fetal, and uterine ultrasound being the most frequently represented categories. RL was used to automate tasks including navigation, plane localization, landmark detection, and video summarization. RL in ultrasound imaging is an emerging field of research and has advantages for sequential workflow optimization tasks. Most approaches are at an early stage and have been tested on small datasets, lack consistent evaluation protocols, and report limited clinical translation. Full article

Breast cancer is a highly heterogeneous disease characterized by distinct molecular subtypes, dynamic tumor–microenvironment interactions, and variable therapeutic responses. Despite the availability of multiple preclinical platforms, a major challenge remains the lack of a coherent multiscale framework capable of integrating biological complexity across experimental systems. This limitation reduces the predictive power of individual models and highlights the need for complementary strategies that reproduce disease progression across multiple biological scales. In this context, advanced imaging technologies have emerged as essential tools for linking preclinical platforms and enhancing their translational relevance. This review examines how multimodal imaging supports the integration of in vitro, ex vivo, and in vivo breast cancer models. We discuss how optical imaging, high-frequency ultrasound, magnetic resonance imaging, positron emission tomography/computed tomography, and intravital microscopy provide complementary molecular, functional, anatomical, and cellular information for the longitudinal assessment of tumor growth, metastatic dissemination, microenvironment remodeling, and therapeutic response. Particular attention is given to emerging translational workflows that combine patient-derived models with advanced imaging to investigate drug sensitivity, treatment resistance, and tumor progression within a precision oncology perspective. We also highlight the role of multimodal imaging in biomarker validation across platforms and in the development of clinically relevant preclinical pipelines. Overall, advanced imaging represents a critical translational bridge across breast cancer model systems, improving the predictive value of preclinical studies and supporting imaging-guided precision oncology from bench to bedside. Full article

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, where early and accurate diagnosis is critical for effective treatment. Although recent advances in deep learning have enabled automated radiology report generation from breast ultrasound images, most existing approaches function as black-box systems, limiting clinical trust and interpretability. This study proposes a trustworthy and explainable framework for automated breast ultrasound report generation that combines Vision-Language Modelling (VLM) with multi-level Explainable Artificial Intelligence (XAI). The proposed architecture integrates a Swin Transformer for visual feature extraction, BioBERT/ClinicalBERT for clinical text representation, and a GPT-2-based decoder for report generation through a dual cross-attention fusion mechanism. The framework is evaluated on benchmark breast ultrasound datasets paired with expert-annotated radiology reports using standard natural language generation metrics, including BLEU, ROUGE-L, METEOR, and CIDEr. Experimental results demonstrate that the multimodal architecture significantly improves report quality, clinical consistency, and semantic accuracy compared with conventional image-only and single-modal baselines. To address transparency and trustworthiness, the framework provides dual-level explanations through Grad-CAM visual heatmaps and LIME/SHAP-based token attribution analysis, enabling clinicians to understand both image regions and textual features influencing generated reports. Qualitative assessment further indicates strong alignment between model explanations and radiologist-identified diagnostic findings. Full article

This review provides an updated and comprehensive assessment of the current literature on quinoa (Chenopodium quinoa Willd.) saponins, with particular emphasis on pharmacological effects. Quinoa (Chenopodium quinoa Willd.) is an annual plant native to South America. Quinoa seeds are characterized by high nutritional value and are rich in proteins, lipids, carbohydrates, minerals, and saponins. Saponins are found in quinoa seeds and impart a bitter taste, which is why they are typically removed before seed consumption. Saponins have been characterized as antinutritional agents, but in recent years they have been investigated for their pharmaceutical applications. Protocols have been developed for the extraction of saponins from seeds, such as conventional solid–liquid extraction (maceration), ultrasound-assisted extraction, microwave-assisted extraction, enzyme-assisted extraction, and pressurized liquid extraction. Ultrasound-assisted extraction and microwave-assisted extraction are considered the most suitable methods for quinoa saponins. Quinoa saponins have shown promise as vaccine adjuvants. Furthermore, they have demonstrated direct anticancer activity, inducing apoptosis and inhibiting the proliferation of breast and colon cancer cells. In general, saponins can induce hemolysis at high concentrations through membrane disruption, primarily via lipid solubilization or pore formation. However, hemolytic activity varies significantly among different saponins and depends on their chemical structure and concentration. In vivo, they have not recorded adverse side effects at doses below 50 mg/kg body weight per day. More than 40 triterpenoid saponins, mainly derived from oleanolic acid, ederagenin, phytolaccagenic acid, and sergianic acid, have been identified in quinoa. Overall, although quinoa saponins have traditionally been considered antinutritional compounds, accumulating evidence indicates that they possess promising pharmacological properties, particularly as anticancer agents. Full article

Axillary lymph node management in early-stage breast cancer has undergone a fundamental transformation over the past three decades, shifting from routine radical clearance to precision-guided de-escalation. Ultrasound (US) has emerged as the central imaging modality in this paradigm shift, serving as the first-line tool for preoperative nodal assessment, guidance for biopsy and clip placement, monitoring of response to neoadjuvant chemotherapy (NAC), and localization of marked nodes for targeted axillary dissection (TAD). This review systematically examines the evolution of axillary management in early-stage breast cancer through a US-centric lens. We summarize the historical transition from Halstedian axillary lymph node dissection (ALND) to sentinel lymph node biopsy (SLNB), critically appraise the landmark trials—Z0011, AMAROS, OTOASOR, and SOUND—that have progressively de-escalated axillary surgery, and detail the role of US in preoperative staging (including conventional B-mode, superb microvascular imaging, contrast-enhanced US, elastography, and AI-assisted diagnostics), US-guided biopsy and node marking, and post-NAC TAD. We further explore the integration of US with liquid biopsy, multigene profiling, and molecular subtype-guided systemic therapy to enable individualized decision-making. Finally, we propose a stepwise US-centric clinical algorithm and discuss future directions, including AI-powered real-time interpretation, imaging-omics, and US-guided targeted therapy. This review provides a comprehensive framework for incorporating ultrasound as a key component of multidisciplinary decision-making—alongside pathological, surgical, radiotherapeutic, and molecular inputs—in contemporary axillary management. Full article

Background: Reliable axillary restaging after neoadjuvant chemotherapy (NAC) is essential for patients with initially cN1 breast cancer considered for surgical de-escalation. Although radioisotope plus blue dye dual-tracer mapping can reduce the false-negative rate (FNR) of sentinel lymph node biopsy (SLNB), its routine use is limited by radioisotope access, infrastructure requirements, and procedural complexity. We evaluated contrast-enhanced ultrasound (CEUS) combined with methylene blue as a non-radioactive dual-mapping strategy. Methods: In this two-center prospective cohort study, patients undergoing surgery after NAC were enrolled if they received preoperative CEUS, intraoperative methylene blue mapping, SLNB, and axillary lymph node dissection. The primary analysis focused on patients with initially cN1 disease, whereas cN2–3 patients were included only for exploratory analyses. Detection rate (DR), FNR, predictive values, CEUS–methylene blue concordance, and CEUS performance for predicting SLN metastasis were assessed. Results: A total of 269 patients were enrolled, including 208 with initial cN1 disease, 45 with cN2 disease, and 16 with cN3 disease. In the cN1 cohort, CEUS plus methylene blue achieved the highest SLN DR (99.04%; 95% CI, 96.56–99.74), followed by CEUS alone (98.08%) and methylene blue alone (71.63%). The FNR was 9.38% (95% CI, 4.37–18.98) for both CEUS plus methylene blue and CEUS alone, versus 12.50% for methylene blue alone. CEUS–methylene blue concordance was 91.16%. For predicting SLN metastasis, CEUS showed 65.52% sensitivity, 86.30% specificity, and 80.39% accuracy. No immediate procedure-related adverse events occurred. Conclusions: CEUS combined with methylene blue showed high SLN detection and a promising false-negative profile in patients with initially cN1 breast cancer after NAC. These findings support further evaluation of this feasible non-radioactive strategy with larger comparative studies, particularly in settings with limited access to radioisotope-guided mapping. Full article

Background/Objectives: Pancreatic metastases are uncommon, and local treatment is reserved for selected patients with limited disease. Surgery remains the standard option when feasible, but may be inappropriate in patients with small, multifocal, anatomically challenging, or medically high-risk lesions. This study evaluated endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) as an organ-preserving local treatment for pancreatic metastases. Methods: Consecutive adult patients undergoing EUS-RFA for histologically confirmed pancreatic metastases at a tertiary endoscopy center between February 2021 and December 2025 were included. All cases were discussed in a multidisciplinary setting. EUS-RFA was performed using a 19G internally cooled RFA needle under real-time EUS guidance. The primary endpoint was initial complete radiological response, defined as complete ablation on first follow-up imaging. Secondary endpoints included technical success, radiological response, repeat EUS-RFA, adverse events, follow-up, and survival. Results: Eleven patients were treated; the mean age was 72.4 ± 6.4 years, and 7/11 (63.6%) were women. Primary tumors included renal cell carcinoma in 7/11 (63.6%), breast cancer in 2/11 (18.2%), melanoma in 1/11 (9.1%), and colorectal cancer in 1/11 (9.1%). Median lesion size was 10.5 mm. Technical success was achieved in 11/11 patients (100%). Initial complete radiological response at first follow-up was observed in 10/11 patients (90.9%); the remaining patient achieved complete response after repeat EUS-RFA. Adverse events occurred in 3/11 patients (27.3%), all mild and conservatively managed. No bleeding, perforation, pancreatic duct stenosis, pancreatic collection, or procedure-related death occurred. Median follow-up was 338 days. Conclusions: EUS-RFA appears feasible and generally well tolerated in carefully selected patients with pancreatic metastases, particularly small, well-visualized lesions when surgery is not feasible or an organ-preserving strategy is preferred. Larger prospective multicenter studies are needed. Although formal subgroup comparisons were not possible in this small cohort, lesion size, anatomical location, and primary tumor biology are likely to influence technical difficulty, local response, and the need for staged or repeat treatment. Full article

Breast cancer remains one of the most prevalent malignancies affecting women worldwide and continues to be a leading cause of cancer-related morbidity and mortality. Patients may present with either localized or advanced disease, with clinical outcomes increasingly influenced by molecular subtype and genetic profile. This review highlights the key genetic factors involved in breast cancer, current diagnostic and therapeutic strategies, and promising emerging approaches that may shape future clinical management. Breast cancer diagnosis typically involves clinical breast examination, imaging techniques such as mammography and ultrasound, and confirmatory biopsies. Genetic mutations in specific genes are strongly linked to the development, progression, and metastasis of the disease. Treatment options for localized breast cancer continue to include surgery (lumpectomy or mastectomy) and radiotherapy, combined with systemic therapies tailored to tumor biology, such as endocrine therapy, human epidermal growth factor receptor 2 (HER2)-targeted therapy, and cyclin-dependent kinase (CDK)4/6 inhibitors. For advanced or metastatic breast cancer, recent therapeutic advances include the use of immunotherapy (e.g., immune checkpoint inhibitors), Poly (ADP-ribose) polymerase (PARP) inhibitors for Breast Cancer gene (BRCA)-mutated cancers, antibody–drug conjugates, and novel targeted agents, which have significantly improved patient outcomes in selected populations. Recent findings in breast cancer genetics have highlighted the critical role of germline and somatic mutations, particularly in genes such as BRCA1, BRCA2, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and TP53, in driving tumor initiation, progression, and therapeutic response. Molecular profiling and next-generation sequencing technologies have enabled more precise tumor classification and facilitated the development of personalized treatment strategies. Despite these advances, treatment resistance and disease recurrence remain major challenges, particularly in aggressive subtypes such as triple-negative breast cancer. Consequently, ongoing research is exploring alternative and complementary approaches, including nanotechnology-based drug delivery systems, gene editing techniques such as clustered regularly interspaced short palindromic repeats-Cas9 (CRISPR-associated protein 9) (CRISPR-Cas9), cancer vaccines, and the integration of traditional and plant-derived compounds. These strategies aim to enhance therapeutic efficacy, reduce systemic toxicity, and overcome resistance mechanisms. Full article

Background: Conventional axillary ultrasound (US) is least reliable in lymph nodes with diffusely thickened cortex. We evaluated whether contrast-enhanced ultrasound with ultra-resolution microvascular imaging (CEUS-URM) improves discrimination of metastatic nodes in this subgroup. Materials and methods: This was a prospective single-center study of patients with histologically confirmed breast cancer; one index (most suspicious) node per patient (unit of analysis = index node). Two separately recruited consecutive cohorts were analyzed: a US-only cohort (n = 181; diffuse-thickening subgroup with histology, n = 52) and a subsequent CEUS-URM cohort (n = 42; 15 metastatic). Surgical histopathology (SLNB/ALND) was the reference standard. A CEUS-URM score was built from data-driven, Youden-optimized cut-offs (URM vessel count ≥8; DV mean density ≥ 13.05) and internally validated by bootstrap with optimism correction. As the cohorts were separate, the US-versus-CEUS comparison is cross-cohort and exploratory. Results: Within the diagnostically challenging subgroup of lymph nodes with diffusely thickened cortex, conventional axillary US alone demonstrated limited discriminatory performance for metastatic involvement (AUC 0.43). CEUS-derived quantitative parameters significantly improved diagnostic accuracy, with the best individual parameter achieving an AUC of 0.68. A simple CEUS score combining hypervascular vessel count and vascular density provided the highest diagnostic performance (AUC 0.81, 95% CI 0.68–0.92). At a low threshold, the CEUS score showed high sensitivity (93%), suitable for screening and exclusion of nodal metastases, while at a higher threshold it achieved high specificity (96%), allowing reliable confirmation of metastatic disease. Conclusions: In this exploratory study, a simple CEUS-URM score improved discrimination of diffusely thickened axillary nodes and may serve as an adjunct to conventional US. The findings are preliminary—derived and tested in the same small cohort—and require external, within-patient paired validation. Full article