Search Results (103)

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

Medical image classification in clinical settings is frequently constrained by High-Dimensional, Low-Sample Size (HDLSS) conditions, rendering conventional Support Vector Machines (SVM) geometrically susceptible to the data piling phenomenon. This study proposes a hybrid CNN-DWD framework to address this geometrical instability by integrating multi-architecture convolutional feature extraction with Distance-Weighted Discrimination (DWD). Pre-trained ResNet50 and DenseNet121 backbones act as frozen feature extractors, generating a highly descriptive 3072-dimensional fused representation. To resolve the computational bottleneck of deploying DWD directly on massive feature spaces, a supervised Multi-Layer Perceptron (MLP) bottleneck progressively compresses this space into a 32-dimensional latent manifold. Evaluated across breast ultrasonography, breast mammography, and chest X-ray datasets under varying training allocations, the proposed architecture drastically accelerates DWD training—achieving over a 130-fold speedup. The proposed CNN-MLP-DWD framework demonstrates highly competitive diagnostic performance, achieving 93.16% accuracy on the breast ultrasonography dataset and a macro-AUC of 99.69% on the chest X-ray benchmark, comparing favorably against the evaluated baseline methods. Full article

Breast imaging reports are typically stored as unstructured free-text documents, which limits their use in clinical analytics, research, and downstream computational applications. These challenges are particularly pronounced in Turkish because of its agglutinative linguistic structure, orthographic variability, and the limited availability of standardized clinical corpora. This study presents a data curation framework for transforming heterogeneous real-world Turkish breast imaging reports into structured and machine-readable datasets, with a specific focus on the extraction and standardization of BI-RADS labels already recorded in routine clinical reports. The proposed pipeline integrates domain-specific preprocessing, text normalization, hybrid conclusion-section segmentation, multi-phase BI-RADS label extraction, duplicate and near-duplicate report handling, and modality-based separation within a transparent rule-guided workflow. The framework was applied to two real-world breast imaging datasets comprising 35,104 reports in Dataset 1 and 27,215 reports in Dataset 2 after overlap and duplicate control around the May 2023 transition period. The datasets included ultrasonography, mammography, and magnetic resonance imaging records. The framework achieved conclusion-section segmentation coverage rates of 96.71% and 99.94%, respectively, and BI-RADS label extraction coverage rates of 95.04% and 100.00%, respectively. These coverage values indicate the proportion of reports for which the rule-guided pipeline produced extractable outputs and should not be interpreted as precision, recall, F1-score, or accuracy against an independent gold-standard corpus. The curation process reduced report-level textual redundancy, improved structural consistency, and enabled systematic separation of report narratives from diagnostic assessment labels. Manual review of selected subsets was used as an internal plausibility check rather than as a substitute for independent radiologist-annotated gold-standard validation. By addressing the challenges of structuring free-text breast imaging reports in a low-resource language setting, this study provides a transparent and adaptable methodological basis for future clinical NLP, machine learning, and real-world healthcare analytics. Full article

Background/Objectives: Breast fibromatosis (desmoid-type fibromatosis) is a rare, locally aggressive neoplasm that may closely mimic breast carcinoma on imaging. Occurrence in male patients is exceptionally uncommon, particularly in association with gynecomastia. We report a rare case of male breast fibromatosis and discuss the diagnostic challenges, surgical decision-making, and reconstructive management. Methods: A 35-year-old male presented with a palpable right breast mass and bilateral grade II gynecomastia. Histopathological and immunohistochemical analyses were performed. Following multidisciplinary evaluation, the patient underwent wide local excision of the lesion with immediate glandular-adipose tissue rearrangement and simultaneous contralateral gynecomastia correction. Results: Ultrasonography demonstrated a hypoechoic lesion of 12 × 11 mm, whereas mammography revealed an irregular mass measuring up to 34 mm. Core needle biopsy showed a fibroblast-rich spindle-cell proliferation with nuclear β-catenin positivity, consistent with desmoid-type fibromatosis. Final histopathology confirmed a 2.5 cm lesion with negative resection margins (minimum margin 5 mm). Immediate reconstruction achieved a satisfactory chest contour and symmetry. No evidence of recurrence was observed during 18 months of follow-up. Conclusions: Breast fibromatosis should be considered in the differential diagnosis of suspicious male breast lesions because radiological findings may closely resemble breast carcinoma. Histopathological confirmation remains essential for appropriate management. In selected patients, immediate glandular-adipose tissue rearrangement may facilitate single-stage defect reconstruction and preservation of chest contour following oncologic excision. Full article

Twelve years after the 2013 fifth edition, the American College of Radiology has released BI-RADS^® v2025, with substantial revisions across mammography, ultrasonography, MRI, and the newly independent contrast-enhanced mammography (CEM) section. This review compares the two editions and reads the main changes against the available evidence on diagnostic performance and reader agreement, rather than only cataloguing them. Examples featuring digital breast tomosynthesis, synthetic mammography, and automated whole-breast ultrasonography now appear throughout the modality sections. “Lobulated” has been added as a shape descriptor across all modalities, while “microlobulated” was dropped from the mammography margin list. Calcification terms shifted from etiology to morphology: “milk of calcium” became “layering,” “punctate” was folded into “round,” and “dystrophic” moved under “coarse.” Ultrasonography gained two new entries, “non-mass lesion” and “echogenic rind,” and on MRI, “initial phase” was renamed “early phase.” Category 6 was rewritten; therefore, surgical excision is recognized as one of several definitive local treatments, and an “uncoupled” principle now separates assessment from management. The auditing section folds Category 3 follow-up into basic auditing and adds a Method of Detection data field. Most supporting data, however, predate v2025, and reader agreement remains lower for newer descriptors such as non-mass enhancement; whether the revisions measurably improve reproducibility is still unproven. Integrating every high-sensitivity tool also brings more false positives, overdiagnosis, and cost; artificial intelligence and radiomics may help close the reproducibility gap, but resource-stratified, equitable implementation will be essential. With v2025, BI-RADS becomes a multimodality framework rather than a reporting lexicon alone. Full article

Background: Preoperative identification of invasive carcinoma in patients with ductal carcinoma in situ (DCIS) remains challenging. While individual imaging findings have been widely investigated, the significance of mass findings identified on both mammography and ultrasonography remains less clearly defined. Methods: This retrospective study included 125 patients with DCIS who underwent both mammography and ultrasonography (US). Imaging findings were categorized as no mass on either modality, mass detected on a single modality, or mass findings identified on both mammography and ultrasonography. Associations between imaging patterns and invasive carcinoma were evaluated using descriptive analyses. Results: The proportion of invasive carcinoma was highest among patients with mass findings identified on both mammography and ultrasonography. Invasive carcinoma was observed in 6 of 71 patients (8.5%) with no mass on either modality, 5 of 37 patients (13.5%) with a mass detected on a single modality, and 8 of 17 patients (47.1%) with mass findings identified on both mammography and ultrasonography. However, substantial overlap between imaging categories remained present. Conclusions: Mass findings identified on both mammography and ultrasonography were more frequently observed in DCIS cases with invasive carcinoma. However, substantial overlap between imaging categories remained present, limiting reliable distinction at the individual patient level. Full article

Background: The sensitivity of mammography screening is compromised in women with dense breast tissue. Supplemental ultrasound (S-US) can enhance cancer detection, but evidence regarding its feasibility, harms, and benefits within Western population-based screening programs remains limited. Methods: This pragmatic, prospective controlled trial was integrated into the German national mammography screening program across 16 sites. Breast density was assessed using automated AI software. Women with “critically dense” breasts (top 15–20%) were offered handheld supplemental S-US in addition to mammography (MXUS). The control group (MX-only) comprised women with comparable densities who did not receive S-US. Primary outcomes included cancer detection rate (CDR), recall rate, biopsy rate, and short-term follow-up recommendations. Results: From May 2020 to March 2024, 25,341 women underwent MXUS, while 38,529 received MX-only. The CDR was significantly higher in the MXUS group, at 10.7 per 1000 (95% CI: 9.4–12.0) compared to 7.2 per 1000 (95% CI: 6.4–8.1) in the MX-only group, yielding an incremental CDR of 3.5 per 1000 (95% CI: 1.9–5.0). However, MXUS led to higher rates of recall (6.6% vs. 5.4%), biopsies (3.2% vs. 1.5%), and short-term follow-up recommendations (0.9% vs. 0.5%). Conclusions: Implementing quality-assured S-US for women with critically dense breasts substantially increases the detection of invasive cancers, but also raises false positives. While these results support density-adapted screening, the high resource intensity suggests that future strategies should optimize risk stratification to target S-US more selectively. Long-term data are required to confirm clinical benefits. Full article

Background/Objectives: Early identification of breast cancer patients who are likely or unlikely to benefit from neoadjuvant chemotherapy (NAC) remains clinically important because ineffective treatment may delay definitive surgery and expose patients to unnecessary toxicity. Quantitative ultrasound (QUS) radiomics offers a contrast-free and repeatable method for extracting tissue-sensitive imaging biomarkers from raw ultrasound data. This study aimed to evaluate whether baseline QUS radiomic features and early treatment-induced changes could predict a pathologic response to NAC in a real-world single-center cohort. Methods: We designed a prospective observational study including 96 consecutive women with biopsy-proven stage II–III breast cancer treated with NAC at Victor Babes University of Medicine and Pharmacy Timisoara. All patients underwent standardized QUS examinations before treatment and again at week 2. The response was defined pathologically at surgery as residual cancer burden class 0/I versus II/III. Clinical, histopathologic, and QUS variables were compared between responders and non-responders. Group comparisons used Student’s t test, Mann–Whitney U test, chi-square testing, and Fisher’s exact test where appropriate. Multivariable logistic regression was used to identify independent predictors of response. Model discrimination was summarized using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Forty-three patients (44.8%) were classified as responders and 53 (55.2%) as non-responders. Responders had higher baseline Ki-67 values (47.8 ± 13.1% vs. 41.9 ± 13.0%, p = 0.033), lower baseline homogeneity (0.3 ± 0.1 vs. 0.4 ± 0.1, p = 0.010), and higher peritumoral heterogeneity (0.9 ± 0.1 vs. 0.8 ± 0.2, p = 0.027). At week 2, responders showed larger increases in mid-band fit (3.0 ± 0.8 vs. 1.2 ± 0.8 dB, p < 0.001), greater entropy change (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), more pronounced spectral intercept reduction (−3.5 ± 1.4 vs. −1.2 ± 1.3, p < 0.001), and greater tumor shrinkage (−24.3 ± 7.0% vs. −11.1 ± 5.7%, p < 0.001). In multivariable analysis, Δ MBF and Δ entropy remained independent predictors of pathologic response. The combined clinical-plus-QUS model achieved an AUC of 0.89. Conclusions: Baseline microstructural heterogeneity and very early QUS-derived treatment changes were strongly associated with the pathologic response to NAC. These findings support the potential role of QUS radiomics as a low-cost, repeatable early-response biomarker in breast cancer. Full article

Background: Breast cancer–related lymphoedema (BCRL) is a frequent chronic complication associated with structural and functional changes in subcutaneous tissue. This study evaluated subcutaneous echogenicity, entropy, and thickness using ultrasound and examined their relationship with skin temperature measured by infrared thermography in breast cancer survivors. Results: Forty-five women were included (mean age 54.0 ± 7.6 years), of whom 44.4% had BCRL. After statistical adjustment, greater STT in the TB region of the homolateral limb compared with the contralateral limb remained supported, whereas other ultrasound differences were region-specific and exploratory. Participants with BCRL consistently exhibited lower Tmax across all ROIs, representing the most robust finding. Associations between ultrasound parameters and Tmax were limited and region-specific, with a statistically supported association observed only between ENT and Tmax in one forearm region. Methods: This cross-sectional exploratory study included women aged 40–70 years with a history of mastectomy. BCRL was diagnosed by indirect volumetry. Six regions of interest (ROIs) were assessed per limb (C1, C2, C3, TA in the forearm; C4 and TB in the upper arm). Ultrasound images were analysed using ImageJ version 1.54I to quantify subcutaneous tissue thickness (STT), echogenicity (ECHO), and entropy (ENT), while maximum skin temperature (Tmax) was extracted from thermographic images. Analyses accounted for repeated measurements within participants using generalized estimating equations, with adjustment for multiple comparisons. Conclusions: Ultrasonography and thermography capture partially overlapping but distinct structural and functional dimensions of tissue alteration in BCRL. Full article

Introduction and Aim: Assessment of antitumor activity in preclinical models remains challenging when relying solely on conventional size-based imaging, particularly for complex agents such as cannabinoids, whose biological effects may not translate into early volumetric tumor changes. Cannabinoid formulations, including the synthetic cannabinoid JWH-182, Cannabixir^® Medium dried flowers, and Cannabixir^® THC full extract, exhibit diverse and potentially subtle effects on tumor biology. Radiomics enables high-throughput extraction of quantitative imaging features that capture intratumoral heterogeneity beyond gross tumor volume. The primary aim of this study was to evaluate the utility of MRI-based radiomics as a sensitive tool for detecting cannabinoid-associated tumor phenotypic modulation in a preclinical breast cancer model. Methods: Orthotopic breast tumors were induced in mice using the 4T1 cell line. Animals received cannabinoid formulations in combination with chemotherapy according to a predefined protocol. Tumor burden was assessed at baseline and post-treatment using ultrasonography and whole-body MRI to calculate tumor doubling time. T1- and T2-weighted MRI datasets were segmented and analyzed using radiomics to extract morphometric and signal-based features. Results: Conventional imaging revealed no significant differences in tumor doubling time between most cannabinoid-treated groups and controls, except for accelerated growth in animals treated with Cannabixir^® THC full extract. In contrast, radiomics identified distinct, compound-specific tumor phenotypes, including structural features consistent with reduced aggressiveness, in JWH-182-treated tumors, despite similar volumetric growth patterns. Conclusion: MRI-based radiomics sensitively captures cannabinoid-associated tumor phenotype alterations beyond volumetric assessment, supporting its value as a pharmaco-imaging tool for characterizing treatment-related tumor biology in preclinical oncology. Full article

Background: Acromegaly is a rare, chronic, systemic, and progressive disease characterized by an excess secretion of growth hormone (GH) and increased circulating insulin-like growth factor 1 (IGF-1) concentrations, typically due to a macroadenoma in the pituitary gland. Both GH and IGF-1 are implicated in cancer promotion based on experimental and epidemiological data, but research findings remain conflicting and population-based data are scarce. Although there is a high mortality rate among acromegalic patients due to cardiovascular diseases, cancer is the third leading cause of death. Aim: The aim of the present study was to assess the risk of different types of cancer in acromegaly and the impact of changes in disease control and patient outcomes over time. Methods: Patients diagnosed with acromegaly at the Ankara University Ibn-i Sina Hospital Endocrinology and Metabolic Diseases Department between 2015 and 2019 were included in this study. Data including demographic data, history of cancer, size of adenoma (micro or macro), serum IGF-1 and GH levels at the time of diagnosis, serum prostate-specific antigen (PSA), thyroid ultrasonography, and, if needed, thyroid fine needle aspiration cytology (TFAC), colonoscopy, and mammography results were collected from patient records retrospectively. Results: We screened 83 patients, and 78 patients with the compensatory data (female/male: 39/39, 50%/50%) were included. The mean age of patients was 49.4 ± 11.9 years and 41.7 ± 12.1 years at the time of diagnosis. The median duration of follow-up was 72 (12–420) months. Periodic thyroid ultrasonography was performed in 65/78 (83.3%) of the patients, and a colonoscopy and mammography were also conducted in 27/78 (34.6%) and 32/39 (82%) of the patients at least once over the course of the disease, respectively. Cancer was detected in 17/78 (21.7%) of the patients; 11/78 (14.1%) of them had well-differentiated thyroid cancer and 2/39 (5.1%) had breast cancer. Prostate cancer, renal cell carcinoma, pancreatic cancer, malignant chordoma, schwannoma, and colon cancer were detected in one patient each. The increased cancer risk in acromegalic patients did not correlate with age, sex, age at diagnosis, time to diagnosing acromegaly, duration of acromegaly, GH and IGF-1 levels at diagnosis, pituitary adenoma size, or Ki-67 levels. Conclusions: Cancer was detected in 21.7% of the acromegaly patients, 14.1% of whom had well-differentiated thyroid cancer. In this study, we demonstrated that thyroid cancer is the most common malignancy in Turkish acromegalic patients, consistent with the results of previous studies. The increased cancer risk in acromegalic patients did not correlate with age, sex, age at diagnosis, time to diagnosing acromegaly, duration of acromegaly, or GH and IGF-1 levels at diagnosis. Full article

Background: Artificial intelligence-based computer-aided diagnosis (AI-CAD) systems are increasingly used in breast ultrasonography; however, their diagnostic performance may vary with breast density. Given that dense breasts are highly prevalent among Asian women, understanding this relationship is essential for optimizing AI-assisted imaging strategies. Therefore, this study aims to evaluate the effect of breast density on the diagnostic accuracy of an AI-CAD ultrasound system in BI-RADS category 4 (C4) breast lesions. Methods: Overall, 110 consecutive BI-RADS C4 lesions were reviewed between January and December 2023. An AI-CAD ultrasound system automatically assigned BI-RADS categories and calculated the probability of malignancy (POM) using static ultrasound images. Histopathology served as the reference standard, with atypia and malignancy combined into a non-benign category. Diagnostic performance—including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy—was analyzed based on breast density (BI-RADS B–D), determined using AI-assisted mammography. Results: Overall, the sensitivity and NPV were 81.3% and 87.5%, respectively, while the specificity and PPV were lower at 53.8% and 41.9%. All diagnostic performance metrics improved with increasing breast density. In the density D category, sensitivity (92.3%), specificity (61.5%), NPV (96.0%), and accuracy (69.2%) were highest. Additionally, concordance between AI-assigned BI-RADS categories and histopathologic diagnoses increased with density (B: 50.0%, C: 57.5%, D: 67.3%). Across all density groups, non-benign lesions consistently demonstrated higher POM values. Conclusions: Breast density significantly affects the diagnostic performance of AI-CAD ultrasound in BI-RADS C4 lesions. The AI system demonstrates higher accuracy and concordance in dense breasts, suggesting more consistent lesion interpretation in high-density environments. These findings highlight the potential utility of AI-assisted ultrasound as a diagnostic adjunct, particularly for Asian women, who commonly have dense breast composition. Further multicenter, real-time validation studies are warranted to validate these findings. Full article

Background & Objectives: Quantitative ultrasound (QUS) radiomics extracts microstructure-sensitive spectral features from radiofrequency data and may provide contrast-free, early indicators of neoadjuvant chemotherapy (NAC) response in breast cancer. This review synthesized open access human studies evaluating QUS radiomics for a priori prediction and early on-treatment monitoring. Methods: Following PRISMA-2020, we included English, free full-text clinical studies of biopsy-proven breast cancer receiving NAC that reported QUS spectral parameters (mid-band fit, spectral slope/intercept) ± textures/derivatives and machine learning models against clinical/pathologic response. Data on design, RF acquisition/normalization, features, validation, and performance (area under the curve (AUC), accuracy, sensitivity/specificity, balanced accuracy) were extracted. Results: Twelve cohorts were included. A priori baseline models achieved accuracies of 76–88% with AUCs 0.68–0.90; examples include 87% accuracy in a multi-institutional study, 82% accuracy/AUC 0.86 using texture-derivatives, 86% balanced accuracy with transfer learning, 88% accuracy/AUC 0.86 with deep learning, and AUC 0.90 in a hybrid QUS and molecular-subtype model. Early monitoring improved discrimination: week-1 results ranged from AUC 0.81 to 1.00 and accuracy 70 to 100%, noting that the upper bound was reported in a small cohort using combined QUS and diffuse optical spectroscopy features, while week 4 typically peaked (AUC 0.87–0.91; accuracy 80–86% in observational cohorts), and one series reported week-8 accuracy of 93%. Across reporting cohorts, mean AUC increased with a 0.05 absolute gain. A randomized feasibility study reported prospective week-4 model accuracy of 98% and demonstrated decision impact. Conclusions: QUS radiomics provides informative a priori prediction and strengthens by weeks 1–4 of NAC, supporting adaptive treatment windows without contrast or radiation. Standardized radiofrequency (RF) access, normalization, region of interest (ROI)/margin definitions, and external validation are priorities for clinical translation. Full article

Background/Objectives: We compared the visibility of breast cancer using the newly developed standing automated breast ultrasound system (MammouS-N) and digital breast tomosynthesis (DBT), and identified factors influencing lesion visibility. Methods: We prospectively enrolled 100 women (mean age: 51.6 years; range: 26–76 years) who were diagnosed with breast cancer and were scheduled to undergo DBT between January and July 2024. They underwent DBT and an ultrasound on the same day. Two radiologists evaluated the visibility scores (0–5) of lesions corresponding to biopsy-confirmed breast cancers identified using magnetic resonance imaging. The Wilcoxon signed-rank test was used to compare the visibility scores of cancers identified on DBT and/or MammouS-N images. Results: Among the 100 women, invasive ductal carcinoma was the most common malignancy (73%). DBT findings included negative findings (7%), masses (46%), masses with calcification (29%), calcifications only (15%), and architectural distortions (3%). On MammouS-N ultrasound, most lesions were classified as masses (93%), whereas 7% were non-mass lesions. For Reviewer 1, MammouS-N demonstrated significantly higher visibility scores (higher scores: 26 on MammouS-N, seven on DBT; equal scores: 67, z = −3.234, p = 0.001). For Reviewer 2, the two modalities showed no significant difference in visibility (higher scores: 27 on MammouS-N, 28 on DBT, equal scores: 45, z = −0.040, p = 0.968). Noncalcified lesions that were obscured on DBT were better visualized on MammouS-N (p < 0.001) by both reviewers. Conclusions: MammouS-N holds promise as an imaging modality complementary to DBT in women with dense breast tissue, particularly for non-calcified lesion detection. Full article