Search Results (649)

Lung cancer remains the leading cause of cancer-related mortality worldwide despite advances in screening, navigational bronchoscopy, and systemic therapies. Diagnostic and therapeutic limitations persist, including uncertainty regarding intraprocedural tissue adequacy during biopsy sampling and constraints of existing ablative modalities for tumors located near critical thoracic structures. This review examines two emerging technologies: Full-Field Optical Coherence Tomography-based Dynamic Cell Imaging (DCI) and monopolar biphasic Pulsed Electric Field (PEF) ablation as complementary emerging technologies that may address these gaps. The Van Gogh™ Microscopy System (CellTivity Scientific, Inc.) utilizes DCI to enable real-time visualization of cellular metabolic activity without tissue destruction, providing functional information regarding tissue viability and microstructural morphology. The Aliya^® PEF ablation system (Galvanize Therapeutics, Inc.) delivers biphasic high-voltage electrical pulses that induce non-thermal tumor cell death while preserving extracellular matrix architecture, potentially allowing treatment near sensitive thoracic structures such as airways, vasculature, and pleura. Early preclinical studies and initial clinical experience suggest that DCI can facilitate rapid intraprocedural assessment of biopsy adequacy, while PEF ablation may provide reproducible focal tumor destruction with a favorable safety profile near critical structures. Although the current evidence base remains limited to early-phase studies and feasibility trials, the convergence of real-time biologic tissue assessment with structurally preserving ablation technologies introduces the possibility of integrating diagnostic confirmation and local therapy within a single procedural workflow. This review summarizes the mechanistic rationale, emerging evidence, and potential clinical applications of these technologies and proposes a conceptual “See and Strike” framework within these two emerging technologies. The methodological limitations, workflow considerations, and future research directions required to validate this approach are also discussed. Prospective multicenter trials and long-term oncologic outcomes will be necessary before widespread clinical adoption. Full article

Background/Objectives: Lung cancer is one of the most common and lethal malignancies worldwide. Early detection remains challenging due to its variable biological behavior. Computed tomography (CT) is the primary imaging method used for early detection. However, the manual interpretation of CT scans is constrained by several challenges such as reliance on expert experience, increasing clinical workload, and considerable variability among observers. Methods: This study introduces an explainable transformer-based framework capable of distinguishing among the three principal clinical categories of lung cancer (small-cell lung cancer, non-small-cell lung cancer, and normal) while simultaneously generating automated radiology reports from CT images. In contrast to conventional single-slice methodologies, the proposed model employs a multi-slice volumetric encoding strategy that captures spatial continuity and anatomical relationships across the CT slices. Visual features extracted by a ViT-based encoder are transformed into a compact patient-level representation through a Learnable Query Attention Pooling (LQAP) mechanism, and this unified representation is subsequently used for both three-class prediction and report generation with a GPT-2-based decoder. To enhance explainability, slice-wise Grad-CAM maps are produced, visually highlighting the anatomical cues that guide the model’s decisions. Results: Experiments conducted on the newly curated LungCA dataset comprising 767 patients demonstrate that the model achieves 97.40% accuracy in the Turkish (TR) reporting scenario and 94.81% accuracy in the English (EN) scenario, alongside strong alignment with human-written reports in BLEU, ROUGE, METEOR, and CIDEr metrics. Conclusions: The findings demonstrate that the proposed multi-slice transformer framework achieves robust performance in both classification and radiology report generation, enhances transparency throughout the decision-making process, and provides a robust artificial intelligence solution capable of effectively supporting clinical workflows in lung cancer assessment. Full article

Macrophages play a crucial role in health and disease. Currently, reporter mice for tracking alternatively activated macrophages in vivo are lacking. We designed a transgenic mouse model in which luminescence and fluorescence proteins, click beetle red luciferase (CBRED2) and mKate2, report on the expression of the Mrc1/Cd206 promoter, active in the monocyte/macrophage population. The mouse line was named B6Mrc1-mKate2-CBRED2. Using this novel mouse model, we were able to develop in vitro assays to validate transgenic macrophage polarization and test them with compounds of repolarization potency. Furthermore, in the in vivo assays, we exploited the migratory and infiltrative potency of macrophages for detecting tumor locations via optical imaging. In fact, macrophages can act as universal cancer markers, as they infiltrate primary and secondary tumors, stimulating or suppressing tumor growth. We first characterized transgenic mice for reporter expression ex vivo, followed by the generation of luminescence-based assays to reflect the polarity of differentiated macrophages, and lastly, we visualized reporter macrophages accumulating and infiltrating the tumor microenvironment (TME) of murine pancreatic ductal adenocarcinoma (PDAC) at multiple time points. We found that the extent of macrophage recruitment and retention was dependent on the infiltrative T-cell and dendritic cell populations present in the TME, reflecting the immunologically hot or cold nature of the PDAC clones, respectively. In conclusion, the ability to optically detect light-emitting macrophages can be applied not only for cancer studies but also in the context of inflammatory diseases. Full article

Vogt–Koyanagi–Harada syndrome (VKH) is a rare granulomatous autoimmune disease characterised by a systemic immune response directed against melanocytes. This multisystem condition primarily affects organs that are rich in melanocytes, such as the eyes, inner ear, meninges and skin. VKH might be responsible for the development of chronic uveitis and permanent visual impairment, particularly in cases where a diagnosis is delayed and treatment is not administered in a timely manner. A key factor in its pathogenesis is the loss of immune tolerance to melanocytes, driven by a T-cell–mediated immune response and genetic susceptibility, including the presence of HLA-DRB1*04 antigens. In recent years, immune checkpoint inhibitors (ICIs) have become the standard treatment in oncology, including non-small cell lung cancer and unresectable melanoma. However, it should be noted that their utilisation carries with it the potential for immune-related adverse events, including rare cases of VKH-like uveitis. The objective of this review is to outline the clinical features of VKH syndrome, examine current diagnostic and treatment approaches, and emphasise the immunopathological mechanisms associated with drug-induced forms of VKH, with a particular focus on programmed cell death protein 1 (PD-1) inhibitors. The article also includes an analysis of the genetic, epigenetic, and environmental factors that predispose individuals to the disease. This analysis facilitates a deeper understanding of the pathogenesis of the disease and assists in the identification of patients at increased risk of drug-induced VKH manifestations. Full article

The rising incidence of skin cancer necessitates scalable and accurate diagnostic tools. While dermoscopy-based systems have achieved expert-level performance, clinical smartphone images pose challenges due to variability in lighting, resolution, and artifacts. Recent advances in multimodal deep learning have shown promise, yet most approaches rely on simple feature concatenation or single-model classifiers, limiting their ability to capture complex cross-modal interactions. This study aims to bridge the diagnostic gap in resource-limited settings by developing a robust multimodal framework that synergizes clinical smartphone images with structured patient metadata for automated skin cancer classification. We propose a novel hybrid architecture integrating a Swin Transformer V2 (SwinV2-Tiny) for hierarchical visual feature extraction and a Denoising Autoencoder (DAE) with PCA for robust metadata embedding. These heterogeneous modalities are fused via a Gated Attention Mechanism that dynamically weighs feature importance across streams. Classification is performed by a Heterogeneous Meta-Stack Ensemble comprising CatBoost, LightGBM, XGBoost, and Logistic Regression, designed to maximize calibration and generalization across imbalanced classes. Evaluated on the PAD-UFES-20 dataset (2298 clinical smartphone images, six diagnostic classes), the proposed framework achieves state-of-the-art performance with a macro-averaged F1-score of 0.977, accuracy of 0.978, and an AUC of 0.990. It significantly outperforms unimodal baselines and existing multimodal methods, demonstrating superior sensitivity (0.974) and precision (0.981), particularly for underrepresented malignant classes like Melanoma (F1: 0.995) and Squamous Cell Carcinoma (F1: 0.960). The integration of clinical metadata with advanced visual embeddings via gated attention significantly enhances diagnostic reliability. Comprehensive ablation studies confirm the contribution of each architectural component. This framework offers a viable pathway for deploying high-precision, AI-driven dermatological screening tools on standard smartphone devices. Full article

Background: Digestive tract cancers, like most other cancers, are usually categorized based on cell or tissue of origin. Molecular clustering based on the transcriptome often produces the same classification. Methods: We developed a new method, Newmanization, to reduce underlying tissue signals from transcriptomic analysis. To test our method, we downloaded data on 1635 samples of digestive tract cancers from The Cancer Genome Atlas. The available data includes transcriptomic data by RNA-Seq, as well as binary mutation allele frequency data by whole exome sequencing. We compared, using silhouette widths and visualization by dimension reduction plots, the effectiveness of Newmanized transcriptome and mutation data to separate digestive tract cancers. Results: The Newmanized transcriptome clusters have clearer separation and larger average silhouette widths. Feature analysis of each cluster for Newmanized transcriptomic data and mutation data revealed that clusters determined with Newmanized data contained more mRNAs present at higher frequencies than clusters defined by mutation data. Conclusions: This suggests that the Newmanized method holds great potential for advancing personalized transcriptomic medicine. Full article

Background: Early detection of oral potentially malignant disorders (OPMDs) and early-stage oral squamous cell carcinoma (OSCC) remains a major clinical challenge, as initial lesions often present with subtle or nonspecific findings during conventional white-light examination. Narrow-band imaging (NBI) enhances visualization of mucosal microvasculature and may improve the identification of dysplastic and malignant transformation. Methods: A narrative review of the literature was conducted in the PubMed, Scopus and Google Scholar databases. Studies published between January 2012 and January 2025 evaluating clinical applications of NBI in oral mucosal lesions, OPMDs, or OSCC were included. Results: NBI enhances visualization of intraepithelial papillary capillary loops (IPCLs), whose morphological alterations correlate with epithelial dysplasia and malignant transformation. Evidence suggests high diagnostic sensitivity (up to 87–100%) and specificity (approximately 83–96%) for detecting high-grade dysplasia and early OSCC. NBI also improves biopsy site selection, reduces sampling error, and supports surveillance of high-risk patients. Conclusions: NBI represents a valuable adjunctive diagnostic tool in oral medicine and dentistry. Although it does not replace histopathological examination, its integration into clinical assessment may enhance early cancer detection and improve management of patients with OPMDs. Full article

Background: Sézary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8⁺ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. Methods: We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n = 26) and PCAECTCL (n = 13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Fisher’s exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. A conversational AI agent, AI-HOPE, was used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. Results: TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. Conclusions: This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics. Full article

Acute lymphoblastic leukemia (ALL) is a clinically diverse cancer in which microRNA (miRNA)-mediated post-transcriptional regulation contributes to leukemogenesis and subtype heterogeneity. In this study, miRNA expression profiling by microarray was performed on ALL cases (B-ALL and T-ALL) and healthy controls. Data were normalized and analyzed for differential expression using false discovery rate (FDR)-adjusted p-values. Differentially expressed miRNAs were further examined using unsupervised visualization to assess overall disease-related expression patterns. To explore their biological significance, experimentally validated miRNA–target interactions were obtained using multiMiR, limited to validated databases (miRTarBase, TarBase, and miRecords) and summarized via target-burden ranking, miRNA–target network analysis, and Circos–style interaction mapping. A unique miRNA expression signature was identified in ALL. Upregulated miRNAs included miR-106a-5p, miR-106b-5p, miR-17-5p, miR-20a-5p, miR-20b-5p, miR-181b-5p, and miR-128-3p, while miR-127-3p, miR-139-5p, miR-433-3p, and miR-584-5p were downregulated. Validated targets concentrated on key leukemia-related genes like PTEN, BCL2L11, CDKN1A, CCND1, RB1, E2F1, and TGFBR2. KEGG pathway analysis highlighted pathways associated with leukemic cell survival and growth, including MAPK, cell cycle, autophagy, Hippo, ubiquitin-mediated proteolysis, and mTOR signaling pathways. These findings reveal a concise ALL-associated miRNA panel predominantly comprising the miR-17/20/106 family and provide a prioritized set of candidate regulatory networks for subtype-specific validation and functional follow-up studies. Full article

Background/Objectives: Peritoneal micrometastases (micromets) remain a major barrier to durable cytoreduction in ovarian and other intra-abdominal cancers because lesions are difficult to visualize and are often resistant to systemic therapy. Liposomal doxorubicin (Dox) improves pharmacokinetics but can be limited by slow intratumoral release. Porphyrin-phospholipid (PoP) liposomes enable near-infrared light–triggered release of Dox (chemophototherapy (CPT)), creating an opportunity for intraoperative fluorescence-guided treatment planning and monitoring. Here, we evaluate a laparoscopic fluorescence imaging platform for quantifying light-triggered drug delivery. Methods: LC-Dox-PoP was applied to SCC2095sc and SKOV-3 cultures in 2D monolayers and 3D spheroid clusters. Dox fluorescence was quantified using a laparoscopic fluorescence imaging system over 1–9 μg/mL concentrations and compared with standard well-plate reader measurements. Porphyrin fluorescence was monitored to assess spheroid localization and photobleaching after activation light exposure. Results: For both cell lines, Dox fluorescence exhibited an approximate 4-fold increase at the maximum administered LC-Dox-PoP concentration, following a linear trend in both SCC2095sc and SKOV-3 cultures (R² = 0.97, 0.98 for 2D and R² = 0.98, 0.98 for spheroids). Laparoscope-derived fluorescence measurements agreed with well-plate reader measurements (R² = 0.89–0.96). Porphyrin fluorescence provided stronger complementary contrast for localizing spheroid constructs and decreased after activation light exposure, consistent with photobleaching during triggered release. Conclusions: These results support a quantitative imaging framework for fluorescence-guided monitoring of light-triggered liposomal drug release and may enable individualized CPT dosimetry for peritoneal micrometastases. Findings in SCC2095sc additionally suggest potential relevance of fluorescence-guided CPT for head and neck/oral cancer, where localized post-resection adjuvant treatment may improve control of residual disease. Full article

Prostate-specific membrane antigen (PSMA) is an essential zinc-dependent metalloprotease classified within the type II transmembrane protein family, often referred to as glutamate carboxypeptidase II (GCPII). PSMA is recognized as a particularly promising target for both the diagnosis and therapeutic intervention of prostate cancer. In this study, we designed and synthesized PSMA-targeted DOTA-loaded bimodal conjugate 11 with SulfoCy5 fluorescent dye, performed in vitro characterization, and analyzed biodistribution in vivo. At 40–100 nM concentrations, the resulting conjugate demonstrated reliable visualization of tumor cells, on par with the reference PSMA-SylfoCy5 compound. In vivo biodistribution analysis of [⁶⁸Ga]Ga-11 in mice demonstrated a reduction in renal accumulation in comparison with dye-free conjugate [⁶⁸Ga]Ga-10. The specificity of [⁶⁸Ga]Ga-11 for PSMA was confirmed in a murine LNCaP xenograft model: its effective accumulation in tumors and kidneys, as well as relatively rapid elimination from non-target tissues, make it a promising agent for PET imaging but not radionuclide therapy. Full article

Background/Objectives: Brain metastasis (BM) represents a significant clinical challenge in advanced breast cancer, yet the molecular mechanisms driving breast cancer brain metastasis (BCBM) remain incompletely characterized. This study aims to identify key molecular pathways and hub genes specifically associated with BCBM through comprehensive bioinformatic analyses. Methods: Gene Set Enrichment Analysis (GSEA), differential gene expression analysis, and weighted gene co-expression network analysis (WGCNA) were performed using two independent GEO datasets (GSE191230 and GSE43837). Protein–protein interaction (PPI) networks were constructed to visualize functional interconnections among dysregulated genes. Survival analyses were conducted using the Kaplan–Meier Plotter database to evaluate the prognostic significance of identified hub genes. Results: GSEA revealed significant upregulation of metabolic pathways (mTORC1 signaling, glycolysis, oxidative phosphorylation) and downregulation of immune-related pathways in BCBM compared to primary tumors. Integrative analysis identified 34 consistently dysregulated genes across datasets, from which 12 hub genes were validated. Among these, RRM2, CDCA8, CCNB1, LMNB2, FANCI, NCAPH, YWHAZ, and ESPL1 demonstrated brain-specific over-expression compared to other metastatic sites. Functional enrichment analysis highlighted cell cycle dysregulation as a critical mechanism in BCBM, and all hub genes showed significant association with poor prognosis in breast cancer patients. Conclusions: This study identifies a unique molecular profile of BCBM characterized by cell cycle dysregulation, metabolic reprogramming, and immune microenvironment alterations. The brain-specific expression patterns of these hub genes represent potential biomarkers for BCBM risk assessment and novel therapeutic targets, providing a basis for precision medicine development. Full article

As critical regulators of gene expression, microRNAs (miRNAs) are key biomarkers and therapeutic targets in cancer. However, current methods for intracellular miRNA imaging are often limited by poor sensitivity and operational complexity. In this study, we identified a site-specifically modified DNAzyme variant, 11Bn, which exhibits up to 7-fold higher catalytic activity than the wild-type 8-17 through systematic screening. Using this variant, we constructed a DNAzyme-based sensor for miRNA-21 imaging in living cells. The sensor achieves a limit of detection of 7.89 nM, threefold lower than that of the wild-type sensor, and enables sensitive visualization of intracellular miRNA-21 without signal amplification. Moreover, it can capture dynamic changes in miRNA levels within cells, providing a versatile molecular tool for miRNA imaging and related biomedical applications. Full article

Purpose: Prostate-specific membrane antigen (PSMA) is a well-established molecular target in prostate cancer (PCa). Both radionuclide imaging and near-infrared fluorescence (NIRF) imaging offer high sensitivity for in vivo tumor detection. PSMA-targeted dual-modality probes integrating these two imaging techniques provide complementary preoperative and intraoperative tumor visualization, thereby improving surgical guidance in PCa. In this study, we aimed to develop a novel dual-labeled PSMA probe combining radioactive and fluorescent properties to achieve precise tumor delineation during radical prostatectomy (RP). Methods: A high-affinity PSMA-targeted fluorescent probe (PSMA-DF) was synthesized using solid-phase synthesis. Subsequent radiolabeling with the radionuclide [⁶⁸Ga]Ga yielded the successful generation of a dual-modal PSMA-targeted molecular probe, namely [⁶⁸Ga]Ga-PSMA-DF. The probe was systematically evaluated both in vitro and in vivo, and its safety profile was assessed through acute toxicity testing. Tumor-bearing nude mouse models were established using PSMA-positive 22Rv1 and PSMA-negative PC-3 PCa cell lines. Imaging performance, tumor-targeting specificity, and biodistribution of the probe were comprehensively evaluated using micro-PET imaging, in vivo fluorescence imaging, and biodistribution studies. Results: High-quality and high-purity PSMA-DF was successfully prepared, which exhibited excellent optical properties. Following radiolabeling with [⁶⁸Ga]Ga, a dual-modality radionuclide-fluorescence probe ([⁶⁸Ga]Ga-PSMA-DF) was successfully constructed. In vitro cellular uptake studies demonstrated that 22Rv1 cells had relatively high uptake of the probe, reaching 7.34 ± 0.55 IA%/10⁶ cells at 120 min. In contrast, PC-3 cells and blocked 22Rv1 cells displayed minimal uptake, confirming the specific targeting ability of the probe. In vivo evaluations were conducted on tumor-bearing mice using micro-PET/CT and NIRF imaging. The results revealed that [⁶⁸Ga]Ga-PSMA-DF achieved high specific tumor accumulation in 22Rv1 xenografts, with the peak tumor uptake (SUVmax = 1.748 ± 0.132) and tumor-to-muscle ratio (11.542 ± 1.511) observed at 120 min. Notably, high-contrast fluorescence imaging was also achieved at later time points, yielding a tumor-to-background ratio (TBR) of 6.559 ± 1.415 at 48 h. Notably, ex vivo biodistribution data were consistent with in vivo imaging findings. Conclusions: This preclinical study demonstrates that [⁶⁸Ga]Ga-PSMA-DF exhibits high and specific uptake in PCa models, supporting its potential as a dual-modality tracer for both PET/CT imaging and real-time intraoperative fluorescence guidance during PCa surgery. Full article

Background: Robotic-assisted thoracic surgery is increasingly recognized as an advanced minimally invasive technique for treating non-small cell lung cancer, offering technical advantages such as enhanced precision and visualization. Although numerous studies have been published worldwide, there are no comparable data from Poland. Therefore, evidence on the perioperative safety and oncologic adequacy of robotic-assisted lobectomy during early phase of program implementation within the Polish healthcare system remains limited. Methods: This retrospective, single-institution observational study included 81 consecutive patients who underwent robotic-assisted lobectomy for primary NSCLC between January 2022 and December 2024. All procedures were carried out using the da Vinci Xi system with a standardized four-arm portal approach. Clinical, perioperative, and pathologic parameters were prospectively collected and analyzed descriptively. Postoperative complications were classified according to Clavien-Dindo. Results: The median patient age was 70 years (IQR: 65–74), 52% were male, and 67% had a history of smoking. Adenocarcinoma was the predominant histologic subtype (51%). The median operative time was 176 min (IQR: 149–220). There were no conversions to thoracotomy and no 30-day mortalities. Postoperative complications occurred in 24% of cases, with prolonged air leak being most common (17%). The median hospital stay was 8 days (IQR: 6–10). R0 resection was achieved in 96% of patients, with a median of 14 lymph nodes dissected across 5 nodal stations. Conclusions: Robotic-assisted lobectomy performed during the early implementation phase of a national program demonstrated low morbidity, high rates of complete (R0) resection, and adequate lymph node yields consistent with international benchmarks. These results support the feasibility of robotic lobectomy within the Polish healthcare setting; however, the single-surgeon, single-center design limits generalizability. Further multicenter prospective studies are needed to confirm reproducibility, assess learning curves, and evaluate long-term oncologic outcomes. Full article