Search Results (12,724)

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

Although renal disease in Syrian hamsters (SHs) has been reported, imaging studies of normal kidneys in this commonly used pet and laboratory species are lacking, despite the key role of imaging in diagnosis. This study aimed to examine kidneys of Syrian hamsters using radiographic and anatomical methods, focusing on kidney location, visibility, size, and its ratio to the second lumbar vertebra, along with the effects of sex, body size, side, and recumbency. Abdominal radiographs were obtained from 29 clinically healthy adult Syrian hamsters of both sexes to assess kidney visibility, position, and size as well as the length of second lumbar vertebral body on lateral and ventrodorsal (VD) views, followed by an in situ anatomical study for comparative analysis. The kidneys were typically located opposite the first to third lumbar vertebrae. On VD views, the left kidney was generally visible, whereas the right was identified in only 28%. The mean values of radiographic RKL, LKL, and 2LVL were 15.2 mm, 12.44 mm, and 14.27 mm, respectively, and the KL/2LVL ratio ranged from 2.66 to 4.00. No significant sex differences were observed in KL or the KL/2LVL ratio in either anatomical or radiographic measurements (p > 0.05). Sex had a significant effect on both radiographic and anatomical 2LVL measurements, with females generally showing higher values than males. Unlike the anatomical measurements, no significant differences between sides were found in radiographic KL and the KL/2LVL ratio. The radiographic RKL, LKL, and 2LVL were significantly larger than those obtained from anatomical measurements. No significant correlation was found between KL, 2LVL, or the KL/2LVL ratio and body length or body weight in either radiographic or anatomical measurements, except for a correlation between body weight and anatomical KL. Right and left kidneys were symmetrically placed, as in rats, but differed from rabbits and guinea pigs. Kidney visibility on VD views was similar to that reported in rabbits. Radiographic RKL, LKL, and 2LVL values differed from those of rodents and rabbits. The radiographic ratio was larger than the values reported in rats, chinchillas, guinea pigs, and rabbits. A single KL-to-2LVL ratio reference range applies to both kidneys and sexes, simplifying clinical assessment. Full article

Pre-eclampsia and foetal growth restriction (FGR) are major pregnancy complications primarily driven by placental dysfunction, and remain leading causes of maternal and perinatal morbidity. Ultrasound imaging, Doppler studies, and angiogenic biomarkers like placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) constitute the main diagnostic modalities; however, these predominantly reflect established disease rather than early molecular disturbances underlying placentation. The identification of biomarkers directly associated with trophoblast signalling pathways has the potential to improve early risk stratification and enable mechanistic classifications. Kisspeptin signalling via its receptor (KISS1R) regulates trophoblast invasion, extracellular matrix remodelling, ERK1/2 activation, and angiogenic balance, thereby modulating spiral artery transformation. Kisspeptin-10 (KP-10), the minimal bioactive fragment of KISS1, is highly expressed in placental syncytiotrophoblasts and exerts its effects through the G-protein-coupled receptor KISS1R. Core features of early-onset FGR and pre-eclampsia (PE)—including defective placentation, maternal vascular malperfusion, and angiogenic imbalance—have been linked to dysregulation of this pathway. During normal gestation, maternal circulating kisspeptin concentrations rise exponentially. In contrast, pregnancies subsequently complicated by FGR or PE, particularly in the early gestation, are associated with reduced levels. However, the comparability of existing studies and their translational applicability are limited by a substantial methodological heterogeneity, including assay variability, gestational age dependence, and inadequate adjustment for maternal confounders. These limitations hinder robust conclusions regarding the role of kisspeptin in placental pathology. This review critically integrates molecular, pathophysiological, and clinical evidence relating to the role of KP-10 in placental dysfunction. The key question is whether KP-10 represents a mechanistic biomarker of trophoblast signalling dysfunction or merely a secondary marker of reduced placental mass; resolving this distinction is essential. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into dental implantology, particularly in treatment planning, a critical phase for implant success. Traditionally dependent on clinician expertise, planning can now be supported by AI-assisted systems that aim to improve diagnostic accuracy, precision, and efficiency. Objective: To synthesise recent evidence on the use of AI in dental implant planning, particularly its ability to analyse cone beam computed tomography (CBCT) imaging to identify edentulous regions and assess bone dimensions compared with conventional planning methods. Methods: A systematic search was conducted across PubMed, Scopus, Google Scholar, and the Cochrane Library, with additional manual searches from October 2024 to July 2025. Eligibility was defined using the Population, Intervention, Comparison, Outcome (PICO) framework, focusing on adults undergoing implant procedures planned using AI-assisted CBCT imaging and deep learning (DL) models, particularly U-Net architectures, for CBCT segmentation. Results: Ten studies were included, AI systems demonstrated high accuracy (92–99.7%) in detecting teeth and edentulous regions, with precision and recall frequently exceeding 90%. AI-assisted planning also showed improved efficiency, and, in one study, higher implant success rates compared with traditional planning (92% vs. 78%). However, variability in study design, inconsistent reporting, and limited ethical oversight were noted. Conclusions: AI, particularly DL models applied to CBCT imaging, shows strong potential to enhance diagnostic precision and efficiency in dental implant planning. Nevertheless, the field requires standardised evaluation metrics, larger datasets, and well-designed clinical trials before widespread clinical implementation. Full article

Nanotheranostic platforms integrating diagnostic and therapeutic functions within a single system have attracted significant attention in precision medicine. However, conventional nanotheranostics based on systemic administration often suffer from off-target accumulation, limited retention at disease sites, and dose-limiting toxicity. To address these limitations, hydrogel-integrated nanotheranostic systems have emerged as a promising strategy for achieving localized diagnosis and therapy with improved spatial control and safety. This review provides a comprehensive overview of recent advances in hydrogel–nanomaterial nanotheranostic platforms, focusing on their design principles, diagnostic capabilities, and therapeutic applications. We discuss the complementary roles of hydrogels and nanomaterials, where hydrogels function as localized reservoirs and tissue interfaces, and nanomaterials provide imaging and therapeutic functionalities. Key integration strategies including physical encapsulation, chemical conjugation, and in situ nanoparticle formation are systematically compared. We further summarize localized diagnostic modalities such as real-time imaging and therapy monitoring, and highlight research-driven applications in cancer treatment, inflammation and infection management, and tissue regeneration. Finally, major translational challenges and future perspectives toward personalized, image-guided local theranostics are discussed. Overall, hydrogel-based nanotheranostic platforms represent a versatile approach for next-generation localized precision medicine. Full article

Background: Multimodal large language models (MLLMs) show promise in medical imaging, but their performance is highly dependent on prompt engineering. This study systematically evaluates how different prompting strategies affect diagnostic accuracy in clinical laboratory image interpretation. Methods: We evaluated five MLLMs (ChatGPT-4o, Gemini 2.0 Flash, Claude 3.5 Sonnet, Grok-2, and Perplexity Pro (Claude 3.5 Sonnet)) using 177 proficiency testing images across three domains: blood smears (n = 78), urinalysis (n = 50), and parasitology (n = 49). Three prompting approaches were compared: (1) complex multi-choice prompts with 20 diagnostic options, (2) zero-shot open-ended prompts, and (3) two-step descriptive-reasoning prompts. Images were sourced from the Taiwan Society of Laboratory Medicine external quality assurance archives with expert consensus diagnoses. Results: Zero-shot prompting significantly outperformed complex multi-choice prompts across all models and domains (p < 0.001). With zero-shot prompts, Gemini achieved 78.5% overall accuracy (urinalysis: 92.0%; parasitology: 75.5%; blood smears: 64.1%), representing a 17% improvement over complex prompts. Two-step descriptive-reasoning prompts further improved blood smear accuracy by 8–12% for top-performing models, but showed minimal benefit in urinalysis and parasitology. The re-query mechanism (“please reconsider”) improved urinalysis accuracy by 7.6% but had a negligible effect on blood smears and parasitology. Conclusions: Prompting strategy critically determines MLLM diagnostic performance. Zero-shot approaches with minimal constraints consistently outperform complex multi-choice formats. The remarkable performance of general-purpose models in structured domains like urinalysis (>90% accuracy) demonstrates the considerable progress of multimodal AI. However, complex morphological tasks like blood smear interpretation require either specialized prompting techniques or domain-specific fine-tuning. These findings provide evidence-based guidance for optimizing AI integration in clinical laboratories. Full article

Extrahepatic cholangiocarcinoma (eCCA) is a highly aggressive malignancy arising from the biliary epithelium, with surgical resection representing the only potentially curative treatment. The predominant periductal infiltrating growth pattern, characterized by subepithelial tumor spread and desmoplastic stromal reaction, severely limits the diagnostic sensitivity of conventional endoscopic sampling techniques, which primarily assess the luminal mucosal surface. This review provides a histomorphology-oriented diagnostic framework for indeterminate extrahepatic biliary strictures, integrating advanced endoscopic technologies with emerging optical diagnostic approaches. ERCP combined with cholangioscopy demonstrates superior sensitivity for perihilar strictures, while EUS-guided tissue acquisition shows higher diagnostic yield in distal cholangiocarcinoma, also providing locoregional staging. Advanced EUS technologies—including elastography, contrast harmonic EUS, and Detective Flow Imaging—further improve characterization of indeterminate strictures by evaluating tissue stiffness, microvascular architecture, and periductal infiltration. Ex vivo fluorescence confocal laser microscopy (FCM) enables real-time microscopic evaluation of biopsy specimens, reducing diagnostic turnaround time and minimizing inadequate sampling. A location-adapted diagnostic algorithm integrating cross-sectional imaging, ERCP, cholangioscopy, and EUS is proposed. An integrated, biology-informed endoscopic approach tailored to tumor location and ductal wall involvement may significantly improve early eCCA detection and guide patient selection for curative treatment. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

Background: Sex estimation represents a pivotal element of forensic anthropological investigation, conventionally dependent on highly dimorphic skeletal components such as the pelvis and skull. The purpose of the current study was to systematically evaluate the diagnostic accuracy of sternal measurements for sex estimation and to identify methodological- or population-based moderators that influence classification performance. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. R programming software was used to perform statistical meta-analysis. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), likelihood ratios (LR±), and overall accuracy were calculated using random-effects meta-analysis. Subgroup analyses and meta-regression were performed based on population origin, study design, statistical approach, and measurement protocol. Results: Forty-one studies comprising 293 predictive models were included. The overall pooled sensitivity and specificity were 80.9% (95% CI: 79.7–82.1) and 74.0% (95% CI: 72.4–75.5), respectively, with a mean accuracy of 77.3%. Subgroup analysis revealed that studies involving African populations and imaging-based methods achieved the highest accuracy. Machine learning- and ROC-based methods outperformed traditional discriminant analysis. Combined sternal measurements (manubrium and body) yielded the most robust diagnostic performance (accuracy: 87.3%). Significant heterogeneity (I² > 85%) was observed. Conclusions: Sternal morphometry exhibits a moderate to high degree of diagnostic accuracy in sex estimation and possesses significant forensic importance, especially in situations where more sexually dimorphic features are inaccessible. Nonetheless, variations across populations, the absence of standardized protocols, and methodological heterogeneity constrain its universal applicability. Full article

Background and Clinical Significance: Nonbacterial thrombotic endocarditis (NBTE) is a sterile fibrin-platelet valvular condition associated with malignancy and hypercoagulable states. It produces friable vegetations prone to systemic embolization, often presenting as multifocal ischemic stroke. While modestly linked to advanced adenocarcinomas, its association with melanoma is exceedingly rare; Case Presentation: We present a 43-year-old man with recently diagnosed metastatic melanoma who presented with fever, confusion and abdominal pain. Brain magnetic resonance imaging (MRI) revealed multifocal bilateral acute infarcts. Additional imaging demonstrated splenic and bilateral renal infarcts. Transesophageal echocardiography (TEE) revealed an 8 mm × 7 mm multilobar lesion on the posterior mitral valve leaflet. Blood cultures remained persistently negative; autoimmune and infectious workup were unrevealing, and positron emission tomography-computed tomography (PET-CT) showed no cardiac hypermetabolism. Despite empiric antibiotics for suspected infective endocarditis (IE), progressive embolic infarcts occurred. After exclusion of infection, NBTE was considered, and therapeutic enoxaparin was initiated, resulting in clinical stabilization without hemorrhagic conversion; Conclusions: Distinguishing NBTE from IE remains challenging due to overlapping and nonspecific imaging findings. TEE is the preferred diagnostic modality because of its high sensitivity for detecting small valvular vegetations. Adjunctive imaging modalities such as brain MRI and PET-CT may support the diagnosis by demonstrating embolic patterns or excluding metabolically active infectious vegetations. Management primarily relies on systemic anticoagulation, while percutaneous vegetation aspiration may represent a potential diagnostic and therapeutic strategy. Clinicians should maintain high suspicion of this condition in patients with advanced melanoma and other malignancies presenting with multifocal embolic phenomena and negative cultures to enable timely anticoagulation. Full article

Accurate distinction between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) remains the key local staging problem in bladder cancer because treatment intensity, timing of radical therapy, and suitability for bladder-preserving strategies all depend on it. Multiparametric magnetic resonance imaging (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) now provide a standardized imaging framework for local staging and increasingly support MRI-first clinical pathways. Artificial intelligence (AI) has emerged as an additional decision-support layer, but the evidence base remains methodologically uneven. In this structured narrative review, we synthesized peer-reviewed literature from January 2020 to March 2026, while retaining foundational VI-RADS studies from 2018 to 2019, and prioritized guideline documents, meta-analyses, prospective cohorts, multicenter and externally validated AI studies, response-assessment studies, and papers addressing implementation and reporting quality. Current evidence shows that radiomics and deep learning models can achieve high discrimination for MIBC detection on MRI, and that the most plausible incremental value of AI lies in equivocal VI-RADS lesions, reader support outside high-volume expert settings, and multimodal risk stratification. However, most studies remain retrospective, highly selected, segmentation-dependent, and vulnerable to reference-standard bias, domain shift, and poor calibration. This review therefore emphasizes several translational issues that are often underreported: lesion-level versus patient-level inference, the distortive effect of TURBT-based labels, the need to evaluate false-negative consequences in VI-RADS 3 tumors, and the distinction between diagnostic support and broader pathway redesign. We also discuss response assessment, nacVI-RADS, segmentation automation, multicenter and federated infrastructure, workflow ownership, and the limits of imaging-only models in a biologically heterogeneous disease. The most credible near-term role of AI is not autonomous diagnosis, but augmentation of standardized mpMRI and VI-RADS within multidisciplinary care. Future progress will depend on prospective utility studies, site-held-out validation, transparent reporting, and the integration of imaging with molecular and cellular heterogeneity through radiogenomic and multi-omics approaches. Full article

Primary squamous cell carcinoma (SCC) of the prostate is a rare and biologically aggressive malignancy lacking a standardized management strategy. De novo primary SCC arising without prior androgen deprivation therapy or radiotherapy is uncommon and presents significant diagnostic and therapeutic challenges. We present the clinical presentation, diagnostic evaluation, treatment strategy, and early therapeutic response of de novo primary SCC of the prostate in a 56-year-old male with end-stage renal disease on maintenance hemodialysis. The patient presented with gross hematuria and a bulky prostate mass invading the bladder with bilateral pelvic lymphadenopathy despite low prostate-specific antigen (PSA) levels. Histopathological and immunohistochemical analyses confirmed pure SCC, staged as cT4N1M0. Because systemic chemotherapy was contraindicated and surgery was not feasible, definitive whole-pelvis radiotherapy with a simultaneous integrated boost was administered. Marked tumor regression was observed one month after treatment. Subsequent imaging demonstrated extensive tumor necrosis with fistulous communication in the context of locally invasive disease. Because long-term oncologic durability could not be assessed owing to non-oncologic clinical deterioration, these findings suggest that definitive radiotherapy may provide meaningful locoregional tumor control in selected medically inoperable patients with de novo prostatic SCC. Full article

This study characterizes the pathological findings in Atlantic Cory’s shearwater (Calonectris borealis) chicks from the Canary Islands associated with illegal hunting cases. A retrospective analysis of necropsy archives, including imaging techniques (X-rays and computed tomography) and histopathological examinations, was conducted on twenty juvenile shearwaters. The study revealed significant cranioencephalic trauma as the primary cause of death in both poaching events. Differences in the methods used by the perpetrators were noted, with variations in skull fractures and associated injuries. These findings highlight the importance of understanding local hunting techniques and sociocultural factors in forensic investigations. Diagnostic imaging, standardized necropsy, and histopathology are essential tools for the forensic investigation of illegal hunting in veterinary sciences. 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

Introduction: Clinical staging based on digital rectal examination is imprecise, leading to pathological upstaging in patients with prostate cancer (PCa). Accurate preoperative assessment remains a challenge despite the use of multiparametric magnetic resonance imaging (mpMRI) and fusion-guided biopsy. This study aims to identify key predictors of upstaging in preoperative patients. Materials and Methods: A retrospective analysis of 924 patients who underwent radical prostatectomy between July 2012 and January 2025 was performed. Variables included prostate-specific antigen, prostate volume, biopsy type, MRI, body mass index and age. Upstaging was defined as ≥pT3 in patients staged clinically as cT1–2. Optimal cut-offs for continuous variables were defined statistically. Multivariable logistic regression was applied to identify independent predictors of upstaging and minor staging upgrading (MSU)—defined as any upward shift in the pathological T stage relative to the clinical T stage. Model performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: Upstaging occurred in 31.9% and MSU in 50.6% of patients. The mean age was 65 years. Cut-off values for PSA density (PSAD) were 0.29 for upstaging and 0.28 for MSU. In the full-cohort model (AUC = 0.628), PSAD (odds ratio (OR) = 2.55), age (OR = 1.04), and hypertension (HT) (OR = 1.47) were associated with upstaging. In PIRADS-based models, PIRADS 5 and PSAD predicted both upstaging (OR = 1.62 and 6.10, respectively; AUC = 0.664) and MSU (OR = 1.75 and 4.67, respectively; AUC = 0.659). MSU was also associated with HT and a lack of fusion biopsy (AUC = 0.622). Conclusions: PSAD and PIRADS 5 lesions are strong determinants of pathological upstaging and MSU in PCa. These factors should be considered in preoperative risk stratification to improve staging accuracy. Despite advances in imaging and biopsy techniques, upstaging remains a common phenomenon, underlining the need for further refinement of diagnostic protocols. Full article