Search Results (78)

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making. Full article

Background: The PROSPET-BX trial compared [⁶⁸Ga]PSMA-11 PET/CT (PSMA-PET) with multiparametric MRI (mpMRI) in parallel in men with suspicion of prostate cancer (PCa) after at least one previously negative biopsy (ClinicalTrials.gov: NCT05297162; GR-2018-12366240). In this study, we performed the cost analysis of the two imaging modalities with respect to the detection of clinically significant PCa (csPCa). Methods: We analyzed the data from patients enrolled in the trial who met the inclusion criteria. For the cost analysis, we identified six competing triage strategies, each defined as a binary decision rule for referral to prostate biopsy: (1) biopsy-all; (2) elevated PSA-density (PSAD; biopsy if PSAD > 0.15 ng/mL/cc; (3) mpMRI positive (PIRADS 3–5); (4) PSMA-PET positive (PRIMARY 3–5); (5) mpMRI or PSMA-PET positive; (6) PSAD and mpMRI. For each strategy, we yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for csPCa. Direct hospital costs were modeled from a provider perspective, incorporating testing and procedural costs. Unit costs (in EUR) were sourced from our institutional accounting records. Pairwise cost-effectiveness comparisons were performed using incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB). Results: Among the six triage strategies evaluated, the “biopsy-all” approach achieved perfect sensitivity, whereas the PSAD + mpMRI pathway was the most parsimonious strategy but missed 14 csPCa cases (53.8%). The combined “mpMRI or PSMA-PET” strategy maximized detection (22 cPCa, missing only 4) at an intermediate cost (EUR 81.991 total; EUR 3.727 per csPCa). The pairwise comparison of each strategy with mpMRI alone showed for the mpMRI or PSMA-PET pathway a low ICER (~EUR 2.900/extra csPCa), with consistently positive and increasing INB across higher WTP (willingness-to-pay). Therefore, this combination provided the most favorable cost-effectiveness profile, balancing detection, efficiency, and cost. Conclusions: To the best of our knowledge, this is the first cost analysis study to compare different strategies incorporating PSMA-PET in the re-biopsy setting, demonstrating that the combined “mpMRI or PSMA-PET” pathway is the most cost-effective diagnostic pathway for csPCa detection. Full article

Incomplete or faulty MRI sequences are common in clinical practice and can impair AI-based analyses that rely on complete multi-contrast data. The relative effectiveness of classical generative adversarial networks (GANs) versus modern diffusion and transformer-based models for clinically usable MRI synthesis remains unclear. This study evaluates cross-modality MRI synthesis using the BraTS 2019 brain tumour dataset, focusing on T1-to-T2 translation. We assess paired and unpaired CycleGAN models and compare them with two stronger but computationally intensive baselines, a conditional denoising diffusion probabilistic model (DDPM) and a transformer-enhanced GAN, using identical data splits and preprocessing pipelines. Inter-modality correlation was evaluated to estimate the achievable similarity between modalities. Conceptually, modality synthesis may be viewed as a representation-learning approach that compensates for missing imaging information by reconstructing clinically relevant features from available contrasts. Paired CycleGAN achieved correlations of $r\approx 0.92–0.93$ and SSIM $\approx 0.90–0.92$, approaching natural T1–T2 correlation ($r\approx 0.95$) while maintaining very fast inference (<50 ms/slice). Unpaired CycleGAN achieved $r\approx 0.74–0.78$ and SSIM $\approx 0.82–0.85$, producing clinically interpretable reconstructions without voxel-level supervision. DDPM achieved the highest fidelity (SSIM $\approx 0.93–0.95$, $r\approx 0.94$) but required substantially greater computational resources, while transformer-enhanced GAN performance was intermediate. Qualitative analysis showed that CycleGAN and DDPM best preserved tumour and tissue boundaries, whereas unpaired CycleGAN occasionally over-smoothed subtle lesions. These findings highlight the trade-off between fidelity and efficiency in cross-modality MRI synthesis, suggesting paired CycleGAN for time-sensitive clinical workflows and diffusion models as a computationally expensive accuracy upper bound. Full article

Introduction: Rapid and reliable neurovascular imaging is critical for time-sensitive diagnosis in acute cerebrovascular disorders, yet conventional magnetic resonance imaging (MRI) workflows remain constrained by acquisition speed, motion sensitivity, and limited integration of physiological context. We introduce the Intelligent Neurovascular Imaging Engine (INIE), a sensor-informed, topology-aware framework that jointly optimizes accelerated data acquisition, physics-grounded reconstruction, and cross-scale physiological consistency. Methods: INIE combines adaptive sampling, structured low-rank (Hankel) priors, and topology-preserving objectives with multimodal physiological sensors and scanner telemetry, enabling phase-consistent gating and confidence-weighted reconstruction under realistic operating conditions. The framework was evaluated using synthetic phantoms, a translational porcine stroke recanalization model with repeated measures, and retrospective human datasets. Across $N_{\mathrm{runs}}=120$ acquisition–reconstruction runs derived from $N_{\mathrm{animals}}=18$ pigs with animal-level train/validation/test separation, performance was assessed using image quality, topological fidelity, and cross-modal consistency metrics. Multiple-comparison control was performed using Bonferroni/Holm–Bonferroni procedures. Results: INIE achieved acquisition acceleration exceeding 70% while maintaining high reconstruction fidelity (PSNR $\approx 35$–36 dB, SSIM $\approx 0.90$–0.92). Topology-aware analysis showed an approximately twofold reduction in Betti number deviation relative to baseline accelerated methods. Cross-modal validation in a PET subset demonstrated strong agreement between MRI-derived perfusion parameters and metabolic markers (Pearson $r\approx 0.9$). INIE improved large-vessel occlusion detection accuracy to approximately 93% and reduced automated time-to-decision to under three minutes. Conclusions: These results indicate that sensor-informed, topology-aware, closed-loop imaging improves the reliability and physiological consistency of accelerated neurovascular MRI and supports faster, more robust decision-making in acute cerebrovascular imaging workflows. Full article

Background: Laryngeal cancer constitutes a major burden. Accurate staging is important to determine the optimal treatment approach. However, a head-to-head comparison of diagnostic performance between CT and MRI in patients with laryngeal cancer is lacking. Furthermore, the performance of CT and MRI in detecting the invasion of different laryngeal structures is yet to be determined. Objective: To compare the diagnostic performance of CT and MRI in patients with laryngeal cancer. Methods: We searched PubMed, Scopus, and Web of Science in November 2025 for cohort studies that compared both index tests (CT and MRI) to histopathological examination of laryngeal cancer. We assessed the quality of the included studies using the QUADAS 2 tool. A bivariate meta-analysis was performed using R and RevMan software to compare pooled sensitivity and specificity, with summary receiver operating characteristic (SROC) curves generated for each outcome. Results: We included eight studies. The pooled data showed that CT was significantly less sensitive compared to MRI in detecting invasion of the thyroid cartilage with an absolute difference of −0.43 (95% CI −0.59 to −0.26), cricoid cartilage −0.65 (95% CI −1.14 to −0.15), paraglottic space −0.5 (95% CI −0.68 to −0.32), and anterior commissure −0.48 (95% CI −0.1 to −0.86). Specificity was similarly high for both modalities across all structures. For arytenoid cartilage and epiglottis invasion, MRI showed a non-significant trend towards higher sensitivity. Summary receiver operating characteristic (SROC) curves indicated superior overall diagnostic accuracy for MRI. In T-staging, MRI consistently demonstrated a lower rate of understaging compared to CT across five studies. Conclusions: MRI had superior diagnostic performance compared to CT in detecting laryngeal cartilage and deep space invasion; it had significantly higher sensitivity, comparable specificity, and lower risk of understaging, thus supporting the use of MRI for accurate pretreatment T-staging. However, given the limited number of studies, the results should be interpreted with caution, and further studies are needed to confirm our findings. Full article

Background/Objectives: Hydrocephalus is a complex neurological disorder marked by abnormal cerebrospinal fluid dynamics and ventricular enlargement. Despite breakthroughs in neuroimaging, diagnosis and longitudinal the application of imaging markers for the diagnosis and longitudinal monitoring of hydrocephalus remains challenging in routine clinical practice. The present study examines the behavior and cross-modality agreement of commonly used linear ventricular measurements under routine imaging conditions, at a single Romanian tertiary-care center characterized by heterogeneous acquisition protocols and limited availability of advanced volumetric techniques. Methods: We conducted a single-center retrospective observational study of 68 adults with hydrocephalus. Linear ventricular metrics, including Evans index and third-ventricle width, were measured on all available CT and MRI scans. CT–MRI agreement was assessed using paired examinations within a 90-day window. Longitudinal changes were analyzed using first–last and pre–post VP shunt comparisons. Associations between baseline imaging features and VP shunt placement were evaluated using rule-based and odds ratio analyses. Results: CT and MRI measurements demonstrated strong agreement for both Evans index (r = 0.93) and third-ventricle width (r = 0.90), with minimal systematic bias. Longitudinal analyses demonstrated small-magnitude changes in ventricular size following intervention, with substantial inter-individual variability. VP utilization increased across Evans index strata, reaching 100% in patients with values ≥0.50. Transependymal cerebrospinal fluid exudation showed the strongest association with subsequent VP shunting. Imaging-based rules exhibited expected trade-offs between sensitivity and specificity. Conclusions: Standard linear ventricular parameters exhibited adequate cross-modality agreement and clinically important longitudinal behavior in this cohort. While insufficient as standalone predictors, these readily available imaging markers remain important tools when combined with a comprehensive clinical assessment. Full article

Visual large language models (VLLMs) are discussed as potential tools for assisting radiologists in image interpretation, yet their clinical value remains unclear. This study provides a systematic and comprehensive comparison of general-purpose and biomedical VLLMs in radiology. We evaluated 180 representative clinical images with validated reference diagnoses (radiography, CT, MRI; 60 each) using seven VLLMs (ChatGPT-4o, Gemini 2.0, Claude Sonnet 3.7, Perplexity AI, Google Vision AI, LLaVA-1.6, LLaVA-Med-v1.5). Each model interpreted the image without and with clinical context. Mixed-effects logistic regression models assessed the influence of model, modality, and context on diagnostic performance and hallucinations (fabricated findings or misidentifications). Diagnostic accuracy varied significantly across all dimensions (p ≤ 0.001), ranging from 8.1% to 29.2% across models, with Gemini 2.0 performing best and LLaVA performing weakest. CT achieved the best overall accuracy (20.7%), followed by radiography (17.3%) and MRI (13.9%). Clinical context improved accuracy from 10.6% to 24.0% (p < 0.001) but shifted the model to rely more on textual information. Hallucinations were frequent (74.4% overall) and model-dependent (51.7–82.8% across models; p ≤ 0.004). Current VLLMs remain diagnostically unreliable, heavily context-biased, and prone to generating false findings, which limits their clinical suitability. Domain-specific training and rigorous validation are required before clinical integration can be considered. Full article

Glomerular filtration rate (GFR) is a key indicator of renal function. Traditional methods for GFR measurement have limitations including invasiveness, low spatial resolution, and lengthy protocols. Positron emission tomography (PET) radiotracers have emerged as promising tools for non-invasive, accurate, and dynamic renal function assessment. Objectives: This systematic literature review evaluates the clinical utility, and current evidence surrounding PET radiotracers used for GFR measurement in humans, emphasizing advances over conventional renal imaging modalities. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, from database inception to November 2024. The search identified studies evaluating PET-based measurement of glomerular filtration rate (GFR) and renal perfusion. Inclusion criteria encompassed human studies using PET radiotracers (e.g., ⁶⁸Ga, ¹⁸F) with comparisons to reference standards (estimated GFR or serum creatinine). Two authors independently screened titles/abstracts, extracted data, and assessed bias using Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). Exclusions included animal studies, reviews, and non-English articles. Results: Eleven studies met inclusion criteria, with ⁶⁸Ga-EDTA showing the highest validation against reference standards such as ⁵¹Cr-EDTA plasma clearance, demonstrating strong correlation. PET imaging offered superior spatial–temporal resolution, enabling accurate split renal function assessment and quantitative analysis of both filtration and perfusion. ⁶⁸Ga-somatostatin analogues exhibited moderate correlations between renal SUV and estimated GFR, with post-PRRT uptake changes indicating early nephrotoxicity. Among novel tracers, ⁶⁸Ga-FAPI showed a strong inverse SUV–GFR relationship, reflecting renal fibrosis and suggesting potential as a chronic kidney disease (CKD) biomarker but requires further clinical validation. Limitations across studies include small sample sizes, retrospective designs, and variability in reference standards. Conclusions: PET radiotracers, particularly ⁶⁸Ga-EDTA, represent a significant advancement for non-invasive, quantitative GFR measurement with improved precision and renal anatomical detail compared to traditional methods. Future prospective, large-scale human studies with standardized protocols are needed to establish these PET tracers as routine clinical tools in nephrology. Integration of hybrid PET/MRI and novel tracer development may further enhance renal diagnostic capabilities. Full article

This paper provides a comprehensive overview of the application of various radiological modalities, with a critical comparison between human and veterinary medicine. The modalities discussed include conventional radiography, dual-energy X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), quantitative ultrasound (QUS), positron emission tomography-computed tomography (PET-CT) and micro and nano computed tomography (micro-CT, nano-CT) in clinical practice and basic research of skeletal system. Radiological imaging plays a crucial role in the diagnosis, monitoring and research of skeletal system disorders in both human and veterinary medicine. In preclinical research, advanced diagnostic imaging modalities such as micro-CT and nano-CT allow for 3D quantification of trabecular and cortical bone microarchitecture for studies in bone biology, regenerative medicine and pharmacological research. Furthermore, the integration of artificial intelligence is advancing image interpretation, precision diagnostics and disease tracking. Despite their broad utility, imaging modalities must be selected based on clinical indication, species, age and anatomical region with consideration of radiation dose, cost and availability, especially in remote regions. For this reason, clinicians and radiologists remain an irreplaceable part of diagnostic imaging. Full article

Accurately predicting survival within patients diagnosed with diffuse glioma remains one of the most difficult issues in neuro-oncology. While most prior research has focused on multimodal fusion or clinical data, we introduce a modality-specific deep learning framework that employs preoperative MRI only to predict mortality outcomes using patient MRI scans. Using the UCSF-PDGM dataset containing structural, diffusion, and perfusion imaging of 495 glioma patients, we trained VGG16 models on every MRI modality individually, including T1, T2, FLAIR, SWI, DWI, ASL, HARDI-derived metrics, and segmentation maps. Our findings revealed that segmentation-based and diffusion-derived features, particularly FA or tensor eigenvalues, possessed the greatest predictive strength, surpassing those obtained from standard structural MRI in binary survival classifications. This approach of modality-specific model training allows for clearer explanations of the prediction process compared to fused approaches and is more practical in scenarios where not all types of MRI are performed on patients. This approach demonstrates the strong predictive power of individual MRI sequences for mortality in glioma cases, providing a modular, adaptable, and clinically actionable deep-learning framework. Additional enhancements can incorporate volumetric models, longitudinal imaging, and non-imaging datasets, including genomic and clinical information. Full article

Accurate brain tumor classification from MRI is often constrained by limited labeled data. We systematically compare conventional machine learning, deep learning, and few-shot learning (FSL) for four classes (glioma, meningioma, pituitary, no tumor) using a standardized pipeline. Models are trained on the Kaggle Brain Tumor MRI Dataset and evaluated across dataset regimes (100%→10%). We further test generalization on BraTS and quantify robustness to resolution changes, acquisition noise, and modality shift (T1→FLAIR). To support clinical trust, we add visual explanations (Grad-CAM/saliency) and report per-class results (confusion matrices). A fairness-aligned protocol (shared splits, optimizer, early stopping) and a complexity analysis (parameters/FLOPs) enable balanced comparison. With full data, Convolutional Neural Networks (CNNs)/Residual Networks (ResNets) perform strongly but degrade with 10% data; Model-Agnostic Meta-Learning (MAML) retains competitive performance (AUC-ROC ≥ 0.9595 at 10%). Under cross-dataset validation (BraTS), FSL—particularly MAML—shows smaller performance drops than CNN/ResNet. Variability tests reveal FSL’s relative robustness to down-resolution and noise, although modality shift remains challenging for all models. Interpretability maps confirm correct activations on tumor regions in true positives and explain systematic errors (e.g., “no tumor”→pituitary). Conclusion: FSL provides accurate, data-efficient, and comparatively robust tumor classification under distribution shift. The added per-class analysis, interpretability, and complexity metrics strengthen clinical relevance and transparency. Full article

Background/Objectives: Pelvic floor disorders affect up to 30% of adult females in the United States. Misdiagnosis occurs in nearly 45% to 90% of cases. Standardized pelvic anatomical measurements could improve diagnostic accuracy and treatment planning. We aimed to evaluate pelvic anatomical variations using magnetic resonance imaging (MRI). Methods: We analyzed MRI pelvic measurements from 250 women aged 20–90 years. Exclusion criteria included prior pelvic surgery (except hysterectomy), pelvic cancer, and use of alternative imaging modalities. Key measurements included anterior vaginal wall thickness (AVWT), bladder wall thickness (BWT), vaginal epithelium to bladder urothelium (VWBU), urethral length (UL), and inter-ureteral distances. A comprehensive statistical analysis was performed, including corrections for multiple comparisons. Results: While several anatomical measurements were correlated, a comprehensive analysis was performed to identify markers for clinical diagnoses. After applying Bonferroni correction for multiple comparisons, we found no statistically significant association between any of the measured anatomical parameters and a diagnosis of incontinence. Notably, an uncorrected difference in Bladder Wall Thickness (BWT) (p = 0.041) did not hold up to rigorous testing. To further assess its clinical utility, a Receiver Operating Characteristic (ROC) curve analysis for BWT as a predictor of incontinence yielded an aArea Under the Curve (AUC) of 0.19, indicating poor predictive validity. Conclusions: In this cohort, static anatomical measurements derived from MRI, including BWT, do not appear to be reliable markers for incontinence. Our findings suggest that the pathophysiology of this disorder is likely more dependent on functional or dynamic factors rather than simple static anatomical variations. Future research should focus on standardizing dynamic imaging parameters to better assess pelvic floor function. Full article

Background and Objectives: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication of antiresorptive and antiangiogenic therapies. Early and accurate imaging is crucial for diagnosis and management. This review summarizes the current evidence on MRONJ imaging findings across modalities and identifies gaps for future research. Materials and Methods: This narrative review analyzed 32 studies (2010–2024) retrieved from PubMed and EBSCO examining imaging findings and diagnostic patterns of medication-related osteonecrosis of the jaw across different modalities. Two independent reviewers screened all articles, extracted data, and assessed methodological quality. Results: Early-stage MRONJ findings included osteosclerosis, lamina dura thickening, and increased prominence of the inferior alveolar canal, while late-stage findings included periosteal reactions, sequestration, and cortical erosion. CBCT and MRI were most sensitive for early detection and lesion extent. However, substantial variability in imaging protocols limited direct comparisons between studies. Conclusions: This review highlights the variable imaging findings of MRONJ and the need for standardized protocols. Advanced imaging techniques and quantitative indices hold promise for improving early diagnosis, staging, and management. Full article

Background/Objectives: Accurate visualization of the Achilles tendon enthesis is critical for distinguishing mechanical, degenerative, and inflammatory pathologies. Although ultrasonography is the first-line modality for suspected enthesis disease, recent technical advances may expand the role of magnetic resonance imaging (MRI). This study evaluated the utility of ultra-short echo time (UTE) and zero echo time (ZTE) sequences versus proton density-weighted imaging (PD-WI) for depicting the enthesis organ in healthy volunteers. Methods: In this institutional review board (IRB)-approved prospective single-center study, 50 asymptomatic adult volunteers underwent 3-Tesla hindfoot MRI with fat-suppressed PD-WI, UTE, and ZTE between 2018 and 2023. Four radiologists assessed image quality, signal-to-noise ratio, visibility, and abnormal high signal intensities (SIs) of the periost, sesamoid, and enthesis fibrocartilages (PCa, SCa, and ECa, respectively). Statistical tests included Chi-square, McNemar, paired Wilcoxon, and Benjamini–Hochberg adjustments for multiple comparisons. Results: The median age was 36 years (range: 20–51); 58% women were included. PD-WI and ZTE sequences were always available while UTE was unavailable in 24% of patients. PD-WI consistently failed to concomitantly visualize all fibrocartilages. ZTE and UTE visualized all fibrocartilages in 72% and 92.1% of volunteers, respectively, with significant differences favoring ZTE and UTE over PD-WI (p < 0.0001) and UTE over ZTE (p = 0.027). Inter-rater agreement exceeded 80% except for SCa on ZTE (68%, 95%CI: 53.2–80.1). Abnormal SCa findings in asymptomatic patients were more frequent with UTE (23.7%) and ZTE (34%) than with PD-WI (2%) (p = 0.0045). Conclusions: At 3-Tesla, UTE and ZTE sequences reliably depict the enthesis organ of the Achilles tendon, outperforming PD-WI. However, the high sensitivity of these sequences also presents challenges in interpretation. Full article

Background and Objectives: The aim of our study was to assess the diagnostic accuracy of four imaging modalities—digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and breast magnetic resonance imaging (MRI)—applied individually and in combination in early cancer detection in women with dense breasts. Methods: This single-center retrospective study was conducted from January 2021 to September 2024 at the Oncology Institute of Vojvodina in Serbia and included 168 asymptomatic and symptomatic women with dense breasts. Based on the exclusion criteria, the final number of women who were screened with all four imaging methods was 156. The reference standard for checking the diagnostic accuracy of these methods is the result of a histopathological examination, if a biopsy is performed, or a stable radiological finding in the next 12–24 months. Results: The findings underscore the superior diagnostic performance of breast MRI with the highest sensitivity (95.1%), specificity (78.7%), and overall accuracy (87.2%). In contrast, DM showed the lowest sensitivity (87.7%) and low specificity (49.3%). While the combination of DM + DBT + US demonstrated improved sensitivity to 96.3%, its specificity drastically decreased to 32%, illustrating as ensitivity–specificity trade-off. Notably, the integration of all four modalities increased sensitivity to 97.5% but decreased specificity to 29.3%, suggesting an overdiagnosis risk. DBT significantly improved performance over DM alone, likely due to enhanced tissue differentiation. US proved valuable in dense breast tissue but was associated with a high false-positive rate. Breast MRI, even when used alone, confirmed its status as the gold standard for dense breast imaging. However, its widespread use is constrained by economic and logistical barriers. ROC curve analysis further emphasized MRI’s diagnostic superiority (AUC = 0.958) compared with US (0.863), DBT (0.828), and DM (0.820). Conclusions: This study provides a unique, comprehensive comparison of all four imaging modalities within the same patient cohort, offering a rare model for optimizing diagnostic pathways in women with dense breasts. The findings support the strategic integration of complementary imaging approaches to improve early cancer detection while highlighting the risk of increased false-positive rates. In settings where MRI is not readily accessible, a combined DM + DBT + US protocol may serve as a pragmatic alternative, though its limitations in specificity must be carefully considered. Full article