Search Results (269)

Background: Urine cytology remains widely used for surveillance of non-muscle-invasive bladder cancer despite well-known limitations in sensitivity, especially for low-grade tumors. Uromonitor^®, a molecular assay detecting TERT promoter, FGFR3, and KRAS mutations in voided urine, has emerged as a promising adjunct. To evaluate its suitability for routine use, a consolidated assessment of diagnostic performance and a direct comparison with urine cytology are needed. Methods: We conducted a prospectively registered systematic review (PROSPERO CRD420251173244), synthesizing all available studies that evaluated Uromonitor^® for the detection of urothelial carcinoma of the bladder (UCB). Methodological quality was assessed using the QUADAS-2 framework, and certainty of evidence was evaluated following GRADE for diagnostic tests. Sensitivity was prespecified as the primary endpoint. Comparative datasets were identified, and random-effects meta-analyses were performed for sensitivity, specificity, accuracy, and predictive values (PVs). Results: Across eight cohorts evaluating Uromonitor^®, 832 of 3196 patients (26.0%) had histologically confirmed UCB. Aggregated sensitivity was 0.55 (95% CI 0.52–0.58). Specificity was 0.95 (0.94–0.96). Accuracy was 0.85 (0.83–0.86). PPV was 0.79 (0.76–0.82), and NPV was 0.86 (0.84–0.87). Across seven paired datasets, urine cytology demonstrated a sensitivity of 0.42, a specificity of 0.91, an accuracy of 0.78, a PPV of 0.64, and an NPV of 0.81. Pooled odds ratio for sensitivity was 3.16 (0.73–13.76), while diagnostic accuracy yielded 1.71 (1.01–2.90). Differences in specificity and NPV were not statistically significant, whereas the PPV favored Uromonitor^®, reaching statistical significance in pooled analyses. Conclusions: Uromonitor^® demonstrates higher sensitivity and improved accuracy compared with urine cytology, although current performance remains insufficient for stand-alone surveillance. The sensitivity estimate showed very low certainty due to pronounced heterogeneity, underscoring the need for careful interpretation. With advancing DNA recovery methods, incorporation of droplet digital PCR, and rigorous evaluations in prospective multicenter studies, Uromonitor^® may become an integral element of risk-adapted follow-up strategies. Full article

Background: Several minimally invasive techniques have been introduced to augment horizontal ridge volume for prosthetically driven implant placement, utilizing different biomaterials to enhance regenerative outcomes. This article presents two clinical cases illustrating a tunneling approach for horizontal alveolar ridge augmentation using a combination of xenogeneic bone graft, hyaluronic acid, and an acellular dermal matrix. Methods: A single vertical incision was made mesial to the bone defect and a dermal matrix was suitably shaped and positioned into the subperiosteal tunnel. Subsequently, the bone graft was inserted between the dermal matrix and the buccal bone plate. Primary wound closure was achieved. After six months, implants were placed. For each patient, an optical scan was performed at baseline (T₀), at six months post-operative ridge augmentation surgery (T₁) and at two months post-implant insertion (T₂). A digital measurement of the horizontal ridge thickness was performed at each inserted implant site. Clinical parameters and patient postoperative morbidity were recorded. Results: The procedure was well tolerated by the patients. No postoperative clinical complications were observed. The mean tissue thickness achieved at T1 was recorded to be 13.3 mm. The same value was recorded at T2. Conclusions: This technique allowed the placement of prosthetically guided implants, with minimal morbidity and no observed complications. Further studies analyzing the histology of newly formed bone and performing three-dimensional radiological examinations to confirm the effectiveness of the surgical technique are warranted to validate these preliminary findings. Clinical Trial Number (NIH): NCT06424223 Full article

Background: Scabies, caused by Sarcoptes scabiei var. hominis, remains difficult to confirm histologically when parasites are sparse or fragmented. Conventional microscopy is particular but limited by small sample size, tissue destruction, and observer dependence. Objective: To evaluate visible–near-infrared hyperspectral imaging (VIS–NIR HSI) as a label-free optical method for detecting S. scabiei in human skin sections and to assess its compatibility with routine HE staining. Methods: Formalin-fixed, paraffin-embedded (FFPE) skin tissue from six patients with histologically verified scabies was analysed using VIS–NIR HSI (500–1000 nm). Unstained sections mounted on CaF₂ substrates and parallel HE-stained slides were imaged. Spectral datasets were processed by principal component analysis and segmentation to distinguish mite structures from epidermal and dermal compartments. Results: The chitin-rich mite exoskeleton exhibited a reproducible reflectance slope in the near-infrared range (R₈₅₀/R₅₅₀ > 1.5), clearly separating parasite from host tissue (R₈₅₀/R₅₅₀ < 1.0). PCA confirmed consistent cluster separation across all cases (ΔPC ≈ 3.7 ± 0.2). These contrasts remained detectable in HE-stained sections, validating applicability to conventional slides. Conclusions: VIS–NIR HSI enables reliable, label-free detection of S. scabiei mites in both unstained and HE-stained human skin tissue. By combining morphological and biochemical information in a single modality, HSI represents a promising adjunct to digital dermatopathology and may improve diagnostic sensitivity in challenging or atypical cases. Full article

Whole slide imaging has transformed the field of pathology by enabling high-resolution digitization of histopathological slides. However, the large image size and variability in morphology, tissue processing, and imaging can pose challenges for robust computational analysis. When working with specific tasks in digital pathology, conventional feature extractors pretrained on general images may not provide features as relevant as those trained on histopathological images. To address this, foundation models pretrained on histopathological images have been developed. Yet, their large size and computational demands might limit widespread adoptions to specific tasks. To facilitate the low-cost adoption of these models, we utilized low-rank adaptation for finetuning the model and developed evolving prototype-based multiple instance learning (EP-MIL). Our method’s capabilities were demonstrated by applying it to the classification of two histological subtypes of lung cancer. The results show that our approach achieves competitive performance when benchmarked against a state-of-the-art technique (CLAM), while offering improvements in efficiency. Specifically, our proposed method requires 8.3 times less training runtime compared with CLAM, uses less than 200.0 MB of memory during training, and enables 73.8 times faster inference runtime. These efficiency gains, combined with competitive performance, suggest that utilizing evolving prototypes with LoRA-tuned foundation models offers a more efficient and practical approach for broader use of foundation models in resource-constrained clinical settings. Full article

Background: Cell-in-cell (CIC) structure is a histological picture of a whole cell inside another cell. Homotypic CIC structures formed by cancer cells are consistently demonstrated to be a factor of poor prognosis and resistance to chemo- and immunotherapy in colorectal cancer (CRC). However, the absence of a standardized counting method limits the use of this factor in the applied research. Objective: To propose an adapted method for quantifying CIC structures in CRC surgical specimens and to evaluate their correlation with established adverse prognostic factors. Methods: A total of 250 histological slides of surgical specimens from 58 patients with pT1-pT4 colorectal adenocarcinoma were studied. Identification of tumor cells and visualization of CIC structures were performed by immunohistochemistry (CK20). Quantitative assessment was performed on digital scans of H&E stained slides. Quantitative assessment was performed on digital slide scans stained with H&E. CIC structures were counted in 5 fields of view corresponding to a ×40 objective (0.975 mm²). A correlation analysis of CIC structures with CRC poor prognosis factors was performed. Results: Immunohistochemical study (CK20) confirmed the formation and prevalence of homotypic structures (95%) over heterotypic ones (5%) (p < 0.001). This finding informed the evaluation of H&E-stained slides and the formulation of criteria for CIC structure identification. A significant predominance of CIC structures in the invasive front was established compared to the tumor central zone (16.7 ± 5.2 and 1.2 ± 1.3 per 5 fields of view, respectively, p < 0.0001). Correlation analysis revealed weak but statistically significant relationships with the tumor-stromal ratio, the tumor buds number and the density of tumor-infiltrating lymphocytes. No correlations were found with the right- or left-sided location, pTNM, grading, lymphovascular and perineural invasion. Conclusions: The paper presents the adapted CIC structures counting method for surgical specimens of CRC, defines the criteria of the CIC, and demonstrates a higher number of CIC structures in the tumor invasive front. Weak correlations between the CIC structures and established factors of CRC poor prognosis are obtained. Full article

Background: Appendiceal neoplasms account for less than 1% of gastrointestinal cancers but are increasing in incidence worldwide. Their marked histological variations and differences create multiple challenges for prognosis and management planning, as current staging systems are limited in certain aspects for capturing the entire disease complexity. Methods: We synthesized data from 18 large observational studies, including 67,001 patients diagnosed between 1973 and 2024. Using advanced computational modeling, we combined multiple statistical methods and machine learning techniques to improve risk stratification, survival prediction, treatment optimization, and forecasting. A novel overlap-aware weighting methodology was applied to prevent double-counting across overlapping registries. Results: Our multi-dimensional risk model outperformed TNM staging (C-index 0.758 vs. 0.689), identifying five prognostic groups with five-year overall survival ranging from 88.7% (low-risk neuroendocrine tumors (NETs)) to 27.3% (high-risk signet-ring cell carcinomas (SRCC)). Hierarchical survival analysis demonstrated marked variation across histological variants, with goblet cell adenocarcinoma showing the most favorable outcomes. Causal inference confirmed the survival benefit of hyperthermic intraperitoneal chemotherapy (HIPEC) in stage IV disease (five-year overall survival (OS) 87.4%) and highlighted disparities in outcomes by race and institutional volume. Time-series forecasting projected a 25% to 50% increase in incidence by 2030, highlighting the growing risk of global burden. Conclusions: By integrating multi-database evidence with advanced modeling and statistical methodologies, our findings demonstrate valuable insights and implications for individualized prognosis, better management decision-making, and health system planning. Our proposed approach and demonstrated methodologies are warranting better progression and advancements in precision oncology and utilization of computational modeling techniques in big data as well as digital health progression landscape. Full article

Accurate and label-free quantitative phase imaging (QPI) plays a crucial role in advancing diagnostic techniques that streamline histology and diagnostic procedures by minimizing sample preparation time, resources, and requirements. Although Digital Holographic Microscopy (DHM) has become a prominent tool within QPI, its diffraction-limited resolution has hindered broader adoption of QPI-DHM. The use of structured and oblique illumination in DHM platforms has overcome the resolution limit, advancing QPI-DHM technology to super-resolution QPI. Despite demonstrated success, adoption of super-resolution DHM (SR-DHM) in clinical and biomedical research remains limited by the absence of a standardized reconstruction algorithm capable of delivering quantitatively accurate, distortion-free super-resolved phase images. This work presents OpenSRQPI, the first standardized computational framework for super-resolution phase reconstruction in DHM systems, whether using structured or oblique illumination. Through its intuitive graphical user interface (GUI) and minimal parameter requirements, OpenSRQPI reduces the technical barrier for non-experts, making super-resolution QPI broadly accessible, enabling new studies of live-cell dynamics, subcellular structure, and tissue morphology. Full article

Background: Cutaneous melanoma (CM) poses significant diagnostic challenges due to its biological heterogeneity and the subjective interpretation of histopathologic criteria. While early and accurate diagnosis remains critical for patient outcomes, conventional pathology is limited by interobserver variability and diagnostic ambiguity, especially in borderline lesions. Objective: This narrative review explores the integration of digital pathology (DP) and artificial intelligence (AI)—including deep learning (DL), machine learning (ML), and interpretable models—into the histopathologic workflow for CM diagnosis. Methods: We systematically searched PubMed, Scopus, and Web of Science (2013–2025) for studies using whole slide imaging (WSI) and AI to assist melanoma diagnosis. We categorized findings across five domains: WSI-based classification models, feature extraction (e.g., mitoses, ulceration), spatial modeling and TIL analysis, molecular prediction (e.g., BRAF mutation), and interpretable pipelines based on nuclei morphology. Results: We included 87 studies with diverse AI methodologies. Convolutional neural networks (CNNs) achieved diagnostic accuracy comparable to expert dermatopathologists. U-Net and Mask R-CNN models enabled robust detection of critical histologic features, while nuclei-level analyses offered explainable classification strategies. Spatial and morphometric modeling allowed quantification of tumor–immune interactions, and select models inferred molecular alterations directly from H&E slides. However, generalizability remains limited due to small, homogeneous datasets and lack of external validation. Conclusions: AI-enhanced digital pathology holds transformative potential in CM diagnosis, offering accuracy, reproducibility, and interpretability. Yet, clinical integration requires multicentric validation, standardized protocols, and attention to workflow, ethical, and medico-legal challenges. Future developments, including multimodal AI and integration into molecular tumor boards, may redefine diagnostic precision in melanoma. Full article

The global rise in cancer incidence and mortality represents a major challenge for modern healthcare. Although current screening programs rely mainly on histological or immunological biomarkers, cancer is a multifactorial disease in which biological, psychological, and behavioural determinants interact. Psychological dimensions such as stress, anxiety, and depression may influence vulnerability and disease evolution through neuro-endocrine, immune, and behavioural pathways, especially by affecting adherence to therapeutic recommendations. However, these dimensions remain underexplored in current screening workflows. This review synthesizes current evidence on the integration of biological markers (tumor and inflammatory biomarkers), psychometric profiling (stress, depression, anxiety, personality traits), and behavioural digital phenotyping (facial micro-expressions, vocal tone, gait/posture metrics) for potential early cancer risk evaluation. We examine recent advances in computational sciences and artificial intelligence that could enable multimodal signal harmonization, structured representation, and hybrid data fusion models. We discuss how structured computational information management may improve interpretability and may support future AI-assisted screening paradigms. Finally, we highlight the relevance of digital health infrastructure and telemedical platforms in strengthening accessibility, continuity of monitoring, and population-level screening coverage. Further empirical research is required to determine the true predictive contribution of psychological and behavioural modalities beyond established biological markers. Full article

Background: Myxoid glomus tumors (mGTs) are an uncommon histologic pattern of glomus tumors, characterized by prominent myxoid stromal changes that may mimic a wide range of soft tissue neoplasms. Recent reports of unexpected CD34 expression in some cases have further complicated their differential diagnosis. Objectives: This study aimed to characterize the histopathological, immunohistochemical, and clinical features of cutaneous mGTs, with particular emphasis on CD34 expression. Methods: We analyzed 8 histologically confirmed cases of cutaneous mGTs underwent to a comprehensive evaluation of morphological features and immunophenotypic profile, with available clinical data. The immunohistochemical panel included smooth muscle actin (SMA), CD34, and S100. Mast cell density was assessed by tryptase in 3 cases. As controls, 8 glomus tumors without myxoid features were also examined for CD34 expression. Results: The cohort consisted of 8 patients (2 males, 6 females; age range 23–71 years). All tumors were located on the distal phalanges of the digits and showed extensive myxoid stromal changes. Immunohistochemistry demonstrated SMA positivity and CD34 expression in all mGTs. In contrast, none of the control GTs without myxoid stroma expressed CD34. Mast cells were consistently identified in the tested cases, predominantly within the myxoid matrix, suggesting a possible role in stromal remodeling. Conclusions: mGTs represent a rare but distinct histological pattern within the glomus tumor spectrum; frequent CD34 expression and mast cell infiltration appear to be characteristic features, although their biological significance remains uncertain. Recognition of these findings is essential to avoid misdiagnosis with other CD34-positive perivascular neoplasms or myxoid soft tissue sarcomas. Full article

Objective: This study aimed to evaluate peri-implant tissue changes during early osseointegration using a combined approach of digital radiographic analysis, RGB pseudocolorization, and histomorphometry in a sheep tibia model. Materials and Methods: Thirty titanium implants were placed in the tibiae of six adult sheep and evaluated at 14 and 28 days post-implantation. Digital periapical radiographs were acquired, grayscale values and RGB channel intensities were measured using Fiji/ImageJ, and compared with histological parameters (bone tissue, collagen, and medullary spaces) quantified from picrosirius–hematoxylin-stained sections. Manual overlay of radiographic and histological images was performed to ensure spatial correspondence of regions of interest. Statistical analyses assessed differences over time and correlations between image data and histological composition. Results: Radiographic grayscale values and histologically measured bone and collagen increased significantly from 14 to 28 days (p < 0.01), while medullary spaces decreased (p < 0.001), indicating progressive bone formation and matrix maturation. RGB analysis revealed significant increases in green channel intensity and decreases in red channel intensity (p < 0.05), while the blue channel remained stable. At 14 days, strong correlations were observed between blue channel intensity and bone tissue (r = 0.81; p = 0.015), and between green channel intensity and collagen (r = 0.98; p < 0.001). Visual overlays demonstrated alignment between radiographic high-density zones and histologically dense bone regions. Conclusions: RGB pseudocolorized radiographic analysis, correlated with histological findings, offers a non-invasive and reproducible method for early detection of peri-implant tissue maturation. This feasibility correlation study provides a foundation for future investigations integrating imaging, histology, and biomechanical testing. Full article

Background/Objectives: Lung cancer is characterized by a significant microstructural heterogenicity among different histological types. Artificial intelligence and digital pathology instruments can facilitate morphological analysis by introducing calculated metrics allowing for the distinguishment of different tissue patterns. Methods: We used computer vision models to calculate a number of morphometric features of tumor vascularization and proliferation. We used two frameworks to process whole-slide images: (1) LVI-PathNet framework for vascular detection, based on the SegFormer architecture; and (2) Mito-PathNet framework for mitotic figure detection, based on the RetinaNet detector and an ensemble classification model. The results were visualized in the segmented and gradient heatmaps. Results: SegFormer for vessel segmentation achieved the following quality metrics: IoU = 0.96, FBeta-score = 0.98, and AUC-ROC = 0.98. RetinaNet + CNN ensemble achieved the following quality metrics: specificity = 0.96 and sensitivity = 0.97. The analysis of the obtained parameters allowed us to identify trophic patterns of lung cancer according to the degree of aggressiveness, which can serve as potential targets for therapy, including proliferative-vascular, hypoxic, proliferative, vascular, and inactive. Conclusions: The analysis of the obtained parameters allowed us to identify distinct quantitative characteristics for each histological type of lung cancer. These patterns could potentially become markers for therapeutic choices, such as antiangiogenic and hypoxia-induced factor therapy. Full article

Background: Jawbone invasion is a common and prognostically unfavorable feature of oral squamous cell carcinoma (OSCC). Although cancer-associated fibroblasts (CAFs) are recognized for their role in tumor progression, their spatial dynamics at the tumor–bone interface remain poorly understood. Methods: We analyzed 14 OSCC specimens with confirmed jawbone invasion using histopathological and immunohistochemical techniques. Digital pathology combined with AI-assisted image analysis was employed to quantify and visualize the spatial distribution of OSCC cells (RANKL-positive), CAFs (α-SMA and FAP-positive), and osteoclasts (cathepsin K-positive) within defined regions of interest at the tumor–bone invasive front. Results: A consistent laminar stromal region enriched in CAFs was observed between the tumor nests and jawbone. CAFs were spatially clustered near OSCC cells and osteoclasts, with 81% and 74% residing within 50 μm, respectively. On average, 11.4 CAFs were present per OSCC cell and 23.2 per osteoclast. These spatial proximities were largely preserved irrespective of stromal thickness, suggesting active bidirectional cellular interactions. Conclusions: Our findings demonstrate that CAFs are strategically positioned to facilitate intercellular signaling between tumor cells and osteoclasts, potentially coordinating OSCC proliferation and bone resorption. This study highlights the utility of AI-assisted spatial histology in unraveling tumor microenvironmental dynamics and proposes CAFs as potential therapeutic targets in OSCC-induced osteolytic invasion. Full article

Background and Objectives: Breast cancer (BC) is a global health concern for women; the disease contributes to significant morbidity and mortality. A key element in the diagnosis of BC involves the histopathological diagnosis, which determines patient management and therapy. However, BC is a multifaceted disease, limiting access to early diagnosis and, therefore, treatment. Artificial intelligence (AI) is transforming diagnostics in the medical field, especially in the detection of BC. Due to the increased availability of digital slides, it has facilitated the effective integration of AI in breast cancer diagnosis. Diagnosis poses a great challenge, even for experienced pathologists, due to the heterogeneity of this malignancy. Analysing microscopic slides by pathologists requires a considerable amount of time. Implementation of AI into routine workflows holds potential to improve diagnostic sensitivity and inter-observer concordance, and to increase efficiency by reducing the review time, thereby helping to alleviate the burden of diagnosing BC. Previous studies mainly address imaging modalities or oncology broadly, while a few specifically concentrates on the histopathological aspect of breast cancer. This review aims to explore the novel synthesis of AI advancements in digital pathology, including tumour classification, grading, lymph node staging, and biomarker evaluation, and discuss their potential incorporation into clinical workflows. We will also discuss the current barriers and prospects for future advancements. Materials and Methods: A literature search was conducted in PubMed and Google Scholar using the mentioned keywords. Articles published in English until July 2025 were reviewed and synthesised narratively. Results: Recent studies demonstrate that AI models such as convolutional neural networks (CNNs), YOLO, and RetinaNet achieve high accuracy in tumour detection, histological grading, lymph node metastasis localisation, and biomarker analysis. The reported performance values range from 75% to over 95% accuracy across various tasks, with gains in diagnostic sensitivity and inter-observer concordance, and reduced review time in assisted workflows. However, certain limitations, such as data variability, external validation in clinical practice, and ethical concerns, restrict the growth and optimal performance of AI and its clinical applicability. Conclusions: The future for AI looks promising, as it is rapidly evolving. By analysing evidence across multiple domains, this review evaluates both opportunities and persisting barriers, offering practical overviews for future clinical transition. AI cannot replace pathologists; however, it has the capabilities to enhance diagnostic precision, efficiency, and ultimately patient outcomes. It is only a matter of time before AI is adopted into healthcare. Full article

The benefit of neoadjuvant chemotherapy (NAC) over upfront surgery (UFS) for resectable pancreatic ductal adenocarcinoma (PDAC) is increasingly recognized, yet prognostic biomarkers remain undefined. We evaluated tumor–stroma ratio (TSR), β-catenin (β-CTN) expression, and tumor budding (TB) in 84 resected PDACs (35 NAC, 49 UFS) using digital image analysis of multi-cytokeratin (m-CK) and β-CTN immunohistochemistry. TSR was defined as the proportion of malignant epithelial area within the tumor, and the β-CTN/m-CK index as the ratio of β-CTN to m-CK immunoreactivity in tumor tissue relative to intralobular ducts. TB was significantly less frequent in NAC than UFS (p = 0.003), suggesting that NAC may indirectly modulate epithelial–mesenchymal transition, with TB regarded as its morphological correlate. In the NAC cohort, low TSR was associated with more favorable histological response (Evans IIa/IIb, median 7%; Evans I, 16%; p = 0.009), likely reflecting NAC-induced tumor shrinkage with relative stromal predominance. In multivariable analysis, low β-CTN/m-CK index (<0.5) predicted shorter relapse-free survival in both NAC (HR = 2.516, p = 0.043) and UFS (HR = 2.230, p = 0.025) subgroups. High TSR (≥13%) was associated with shorter cancer-specific survival (HR = 2.414, p = 0.034) in the overall cohort, indicating prognostic value complementing its association with NAC response. These results identify the β-CTN/m-CK index and TSR as prognostic biomarkers in PDAC. Full article