Search Results (533)

Carcinomas originate from polarized epithelia, displaying luminal and basal orientations with distinct biological properties. Regardless of tissue of origin, many carcinomas show luminal or basal traits that are reflected in molecular profiles and are associated with different clinical behaviors and outcomes. Traditionally, cancers have been classified by histology and anatomical site, but accumulating evidence indicates that luminal/basal subtyping reflects shared biological programs that transcend organ boundaries. Breast cancer was the first model in which these subtypes were defined, revealing clear prognostic and therapeutic implications. Subsequent studies have identified similar subtypes in bladder, lung, prostate, pancreatic, and head and neck carcinomas, where basal phenotypes are consistently associated with aggressive disease and distinct vulnerabilities to treatment. In this review, we synthesize advances from the last decade (2010–2024) on the basal-like subtype across epithelial tumors. We summarize key studies applying luminal/basal subtyping in large cohorts of carcinomas and in single tissue tumor types. By integrating these findings, we aim to clarify the current understanding of luminal and basal subtypes in epithelial tumors and outline their potential to refine cancer classification, improve prognostic accuracy, and guide therapeutic decision-making. This perspective supports a biology-driven framework for cancer classification and treatment, moving beyond traditional histological boundaries. Full article

Over the past decade, patient-derived organoids (PDOs) have emerged as powerful in vitro models that closely recapitulate the histological, genetic, and functional features of their parental primary tissues, representing a ground-breaking tool for cancer research and precision medicine. This advancement has led to the development of living PDO biobanks, collections of organoids derived from a wide range of tumor types and patient populations, which serve as essential platforms for drug screening, biomarker discovery, and functional genomics. The classification and global distribution of these biobanks reflect a growing international effort to standardize protocols and broaden accessibility, supporting both basic and translational research. While their relevance to personalized medicine is increasingly recognized, the establishment and maintenance of PDO biobanks remain technically demanding, particularly in terms of optimizing long-term culture conditions, preserving sample viability, and mimicking the tumor microenvironment. In this context, this review provides an overview of the classification and worldwide distribution of tumor and paired healthy tissue-specific PDO biobanks, explores their translational applications, highlights recent advances in culture systems and media formulations, and discusses current challenges and future perspectives for their integration into clinical practice. Full article

Background/Objectives: This study aimed to evaluate the diagnostic performance of simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in distinguishing malignant from benign breast lesions, and to explore their association with clinicopathological features. Methods: This retrospective study included 108 women who underwent breast MRI with multi-b-value DWI (0, 20, 200, 500, 800 s/mm²). Of those 108 women, 73 had pathologically confirmed malignant lesions. IVIM maps (ADC_map, D, D*, and perfusion fraction f) were generated using IB-Diffusion™ software version 21.12. Lesions were manually segmented by radiologists, and clinicopathological data including receptor status, Ki-67 index, cancer type, histologic grade, and molecular subtype were extracted from medical records. Nonparametric tests and ROC analysis were used to assess group differences and diagnostic performance. Additionally, a binary logistic regression model combining D, D*, and f was developed to evaluate their joint diagnostic utility, with ROC analysis applied to the model’s predicted probabilities. Results: Malignant lesions demonstrated significantly lower diffusion parameters compared to benign lesions, including ADC_map (p = 0.004), D (p = 0.009), and D* (p = 0.016), indicating restricted diffusion in cancerous tissue. In contrast, the perfusion fraction (f) did not show a significant difference (p = 0.202). ROC analysis revealed moderate diagnostic accuracy for ADC_map (AUC = 0.671), D (AUC = 0.657), and D* (AUC = 0.644), while f showed poor discrimination (AUC = 0.576, p = 0.186). A combined logistic regression model using D, D*, and f significantly improved diagnostic performance, achieving an AUC of 0.725 (p < 0.001), with 67.1% sensitivity and 74.3% specificity. ADC_map achieved the highest sensitivity (100%) but had low specificity (11.4%). Among clinicopathological features, only histologic grade was significantly associated with IVIM metrics, with higher-grade tumors showing lower ADC_map and D* values (p = 0.042 and p = 0.046, respectively). No significant associations were found between IVIM parameters and ER, PR, HER2 status, Ki-67 index, cancer type, or molecular subtype. Conclusions: Simplified IVIM DWI offers moderate accuracy in distinguishing malignant from benign breast lesions, with diffusion-related parameters (ADC_map, D, D*) showing the strongest diagnostic value. Incorporating D, D*, and f into a combined model enhanced diagnostic performance compared to individual IVIM metrics, supporting the potential of multivariate IVIM analysis in breast lesion characterization. Tumor grade was the only clinicopathological feature consistently associated with diffusion metrics, suggesting that IVIM may reflect underlying tumor differentiation but has limited utility for molecular subtype classification. Full article

Type 1 autoimmune pancreatitis (AIP), IgG4-related sclerosing cholangitis (IgG4-SC), and IgG4-related cholecystitis are recognized as IgG4-related pancreatobiliary diseases. Endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasonography (EUS) are crucial diagnostic modalities for these conditions. In the diagnosis of AIP, EUS-guided tissue acquisition plays an important role in obtaining histological confirmation and excluding pancreatic cancer (PC). EUS, including contrast-enhanced harmonic imaging and elastography, is used to differentiate focal-type AIP from PC. Endoscopic retrograde pancreatography (ERP) is utilized to obtain a pancreatogram when it is challenging to distinguish AIP from pancreatic cancer. Duodenal papilla biopsy may serve as a supplementary tool, particularly in cases involving the pancreatic head. Cholangiographic classification is essential for differentiating IgG4-SC from PC, primary sclerosing cholangitis (PSC), and cholangiocarcinoma (CCA). ERCP is commonly performed for additional ERCP-related procedures. Intraductal ultrasonography (IDUS) is useful for distinguishing IgG4-SC from CCA or PSC. The primary role of bile duct biopsy is exclusion of malignant biliary strictures; EUS-guided tissue acquisition may also provide histological evidence of IgG4-SC. In the diagnosis of IgG4-related cholecystitis, EUS is helpful to differentiate it from gallbladder cancer. EUS-guided tissue acquisition can aid in confirming IgG4-related cholecystitis and excluding gallbladder cancer or xanthogranulomatous cholecystitis. Transpapillary gallbladder cytology or biopsy may also be considered. Overall, endoscopic modalities play a critical role in diagnosing IgG4-related pancreatobiliary diseases. Full article

Cancer remains one of the most significant diseases threatening human health. The lack of effective diagnostic and therapeutic technologies is a critical factor contributing to the high clinical mortality rates associated with malignant tumors. Self-propelled micro/nano-motors (MNMs) hold promise for addressing the limitations of conventional nanoparticles in cancer diagnosis and treatment. Their unique motion characteristics enhance the efficiency of MNMs in achieving rapid distribution, deep tissue penetration, and targeted delivery in vivo. This review systematically summarizes recent advances in MNM-based therapy for tumor diagnosis and treatment, offering a comprehensive overview of their material classification, self-propelled mechanisms, targeting strategies, and therapeutic approaches. Subsequently, we discuss the therapeutic mechanisms of MNMs within the tumor microenvironment in detail and highlight the advantages of synergistic multimodal therapies, including chemodynamic therapy, sonodynamic therapy, photothermal therapy, immunotherapy, photodynamic therapy, and gas therapy. Finally, we outline the main challenges and prospects for the development of MNMs in cancer diagnosis and therapy. Full article

To deliver the most effective cancer treatment, clinicians require rapid and accurate diagnoses that delineate tumor type, stage, and prognosis. Consequently, minimizing the need for repetitive and invasive procedures like biopsies and myelograms, along with their associated risks, is a critical challenge. Non-invasive monitoring offers a promising avenue for tumor detection, screening, and prognostication. While the identification of oncogenes and biomarkers from circulating tumor cells or tissue biopsies is currently standard practice for cancer diagnosis and classification, accumulating evidence underscores the significant role of epigenetics in regulating stem cell fate, including proliferation, self-renewal, and malignant transformation. This highlights the importance of analyzing the methylome, exosomes, and circulating RNA for detecting cellular transformation. The development of diagnostic assays that integrate liquid biopsies with epigenetic analysis holds immense potential for revolutionizing tumor management by enabling rapid, non-invasive diagnosis, real-time monitoring, and personalized treatment decisions. This review covers current studies exploring the use of epigenetic regulation, specifically the methylome and circulating RNA, as diagnostic tools derived from liquid biopsies. This approach shows promise in facilitating the differentiation between primary central nervous system lymphoma and other central nervous system tumors and may enable the detection and monitoring of acute myeloid/lymphoid leukemia. We also discuss the current limitations hindering the rapid clinical translation of these technologies. Full article

Tongue cancer, the most aggressive subtype of oral cancer, presents critical challenges due to the limited number of specialists available and the time-consuming nature of conventional histopathological diagnosis. To address these issues, we developed an intelligent diagnostic system integrating Mueller matrix microscopy with deep learning to enhance diagnostic accuracy and efficiency. Through Mueller matrix polar decomposition and transformation, micro-polarization feature parameter images were extracted from tongue cancer tissues, and purity parameter images were generated by calculating the purity of the Mueller matrices. A multi-stage feature dataset of Mueller matrix parameter images was constructed using histopathological samples of tongue cancer tissues with varying stages. Based on this dataset, the clinical potential of Mueller matrix microscopy was preliminarily validated for histopathological diagnosis of tongue cancer. Four mainstream medical image classification networks—AlexNet, ResNet50, DenseNet121 and VGGNet16—were employed to quantitatively evaluate the classification performance for tongue cancer stages. DenseNet121 achieved the highest classification accuracy of 98.48%, demonstrating its potential as a robust framework for rapid and accurate intelligent diagnosis of tongue cancer. Full article

Carboxypeptidase A4 (CPA4) is an exopeptidase that cleaves peptide bonds at the C-terminal domain within peptides and proteins. It preferentially cleaves peptides with terminal aromatic or branched chain amino acid residues such as phenylalanine, tryptophan, or leucine. CPA4 was first discovered in prostate cancer cells, but it is now known to be expressed in various tissues throughout the body. Its physiologic expression is governed by latexin, a noncompetitive endogenous inhibitor of CPA4. Nevertheless, the overexpression of CPA4 has been associated with the progression and aggressiveness of many malignancies, including prostate, pancreatic, breast and lung cancer, to name a few. CPA4’s role in cancer has been attributed to its disruption of many cellular signaling pathways, e.g., PI3K-AKT-mTOR, STAT3-ERK, AKT-cMyc, GPCR, and estrogen signaling. The dysregulation of these pathways by CPA4 could be responsible for inducing epithelial--mesenchymal transition (EMT), tumor invasion and drug resistance. Although CPA4 has been found to regulate cancer aggressiveness and poor prognosis, no comprehensive review summarizing the role of CPA4 in cancer is available so far. In this review, we provide a brief description of peptidases, their classification, history of CPA4, mechanism of action of CPA4 as a peptidase, its expression in various tissues, including cancers, its role in various tumor types, the associated molecular pathways and cellular processes. We further discuss the limitations of current literature linking CPA4 to cancers and challenges that prevent using CPA4 as a biomarker for cancer aggressiveness and predicting drug response and highlight a number of future strategies that can help to overcome the limitations. Full article

Background/Objectives: Androgen deprivation therapy (ADT) is widely used to manage prostate cancer (PC), but the emergence of treatment resistance remains a major clinical challenge. Although the GST family has been implicated in drug resistance, the specific role of GSTM5 remains poorly understood. This study investigates whether GSTM5, alone or in combination with clinical variables, can improve patient stratification based on the risk of early treatment resistance. Methods: In silico analyses were performed to examine GSTM5’s role in protein interactions, molecular pathways, and gene expression. The rs3768490 polymorphism was genotyped in 354 patients with PC, classified by ADT response. Descriptive analysis and logistic regression models were applied to evaluate associations between genotype, clinical variables, and ADT response. GSTM5 expression related to the rs3768490 genotype and ADT response was also analyzed in 129 prostate tissue samples. Results: The T/T genotype of rs3768490 was significantly associated with a lower likelihood of early ADT resistance in both individual (p = 0.0359, Odd Ratios (OR) = 0.18) and recessive models (p = 0.0491, OR = 0.21). High-risk classification according to D’Amico was strongly associated with early progression (p < 0.0004; OR > 5.4). Combining genotype and clinical risk improved predictive performance, highlighting their complementary value in stratifying patients by treatment response. Additionally, GSTM5 expression was slightly higher in T/T carriers, suggesting a potential protective role against ADT resistance. Conclusions: The T/T genotype of rs3768490 may protect against ADT resistance by modulating GSTM5 expression in PC. These preliminary findings highlight the potential of integrating genetic biomarkers into clinical models for personalized treatment strategies, although further studies are needed to validate these observations. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on large-scale labeled datasets. To address this, we propose Dynamic Cross-Scale Swin Transformer (DCS-ST), a robust and efficient framework tailored for histopathology image classification with scarce annotations. Specifically, DCS-ST integrates a dynamic window predictor and a cross-scale attention module to enhance multi-scale feature representation and interaction while employing a semi-supervised learning strategy based on pseudo-labeling and denoising to exploit unlabeled data effectively. This design enables the model to adaptively attend to diverse tissue structures and pathological patterns while maintaining classification stability. Extensive experiments on three public datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that DCS-ST consistently outperforms existing state-of-the-art methods across various magnifications and classification tasks, achieving superior quantitative results and reliable visual classification. Furthermore, empirical evaluations validate its strong generalization capability and practical potential for real-world weakly-supervised medical image analysis. Full article

Sporadic colorectal cancer (CRC), the third leading cause of cancer-related death globally, arises through a continuum from normal tissue to adenomas, progressing from low-grade (LGD) to high-grade dysplasia (HGD); yet, the early epigenetic drivers of this transition remain unclear. To investigate these events, we profiled LGD and HGD adenomas using EM-seq, and identified a consensus differential methylation signature (DMS) of 626 regions through two independent bioinformatics pipelines. This signature effectively distinguished LGD from HGD in both tissue and plasma-derived cell-free DNA (cfDNA), highlighting specific methylation patterns. Functional annotation indicated enrichment for regulatory elements associated with transcription factor activity and cell signaling. Applying the DMS to the TCGA CRC dataset revealed three tumor subtypes with increasing hypermethylation and one normal cluster. The most hypermethylated subtype exhibited poor survival, high mutation burden, and disrupted transcriptional networks. While overlapping with classical CpG Island Methylator Phenotype (CIMP) categories, the DMS captured a broader spectrum of methylation alterations. These findings suggest that the DMS captures functionally relevant, antecedent epigenetic alterations in CRC progression, enabling the robust stratification of dysplasia severity and tumor subtypes. This signature holds promise for enhancing preclinical detection and molecular classification, and warrants further evaluation in larger prospective cohorts. Full article

BRCA1 and BRCA2 are associated with advanced prostate cancer progression and poor prognosis. Copy number variants (CNVs) of these genes play a crucial role in guiding targeted treatments, particularly for patients receiving PARP inhibitors. However, CNV detection using multiplex ligation-dependent probe amplification (MLPA) is often limited by tumor heterogeneity, leading to ambiguous results. This study therefore aimed to evaluate BRCA1/2 CNVs in advanced prostate cancer patients using droplet digital PCR (ddPCR) and compare the results with MLPA. DNA from 11 advanced prostate cancer tissues was analyzed using both methods, in parallel with four cell lines and seven healthy volunteers. Our findings revealed that ddPCR effectively classified normal CNV groups—including normal control cell lines, healthy volunteers, and samples with normal MLPA final ratios—from deletion groups, which included deletion control cell lines, samples with deletion final ratios from MLPA, and cases with previously ambiguous results. Interestingly, two cases involving BRCA1 and one case involving BRCA2 exhibited ambiguous results using MLPA; however, ddPCR enabled more precise classification by applying the Youden Index from ROC analysis and identifying optimal cutoff values of 1.35 for BRCA1 and 1.55 for BRCA2. These optimal thresholds allowed ddPCR to effectively reclassify the ambiguous MLPA cases into the deletion group. Overall, ddPCR could offer a more sensitive and reliable approach for CNV detection in heterogeneous tissue samples and demonstrates strong potential as a biomarker tool for guiding targeted therapy in advanced prostate cancer patients. However, further validation in larger cohorts is necessary to optimize cutoff precision, confirm diagnostic performance, and evaluate the full clinical utility of ddPCR. Full article

Objective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification. The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. Results: The results demonstrated that SAVE enhanced classification performance and increased its accuracy, sensitivity, and specificity compared to a traditional RGB imaging approach. Conclusions: This advanced method offers dermatologists a tool for early and accurate diagnosis, reducing the likelihood of misclassification and improving patient outcomes. Full article

Background and Objectives: Thyroid nodules are a common endocrine disorder, with 10–15% exhibiting malignancy. Accurate differentiation of malignant and benign nodules is crucial for optimizing treatment outcomes. Current diagnostic tools, such as the Bethesda classification and fine-needle aspiration biopsy (FNAB), are limited in sensitivity and specificity, particularly in indeterminate cases. Tumor-infiltrating immune cells (TIICs) in the tumor microenvironment (TME) play a significant role in thyroid cancer progression. CD66b⁺ neutrophil-like monocytes constitute a novel subset of myeloid cells that are implicated in the modulation of anti-tumor immune responses, but their role in thyroid cancer remains unclear. Materials and Methods: Peripheral blood and thyroid nodule tissue samples were obtained from 24 patients with papillary thyroid carcinoma, and from 10 patients who underwent surgery for symptoms of tracheal compression due to benign thyroid nodules. Myeloid cell populations were assayed by flow cytometric immunophenotyping with CD45, HLA-DR, CD14, and CD66b. The data were statistically analyzed with the clinical properties of the patients. Results: The neutrophil-like monocytes, which were determined as HLA-DR⁺CD14⁺CD66b⁺ cells, found in the circulation (11.9 ± 2.4% of total mononuclear immune cells) of the patients with papillary thyroid carcinoma, were significantly elevated (p < 0.001). Accordingly, these cells were more frequently detected in tumor tissues (21.1 ± 2.1% of total tumor-infiltrating immune cells) compared to non-tumor thyroid tissues (p = 0.0231). The infiltration levels of neutrophil-like monocytes were significantly higher in malignant nodules as well as in the peripheral blood of the papillary thyroid carcinoma patients compared to the samples obtained from the patients with benign nodules. The tumor tissues exhibited increased immune cell infiltration and harbored CD66b-expressing neutrophil-like HLA-DR⁺CD14⁺ monocytic cells, which indicates an inflammatory milieu in malignant thyroid cancer. Conclusions: This study identifies neutrophil-like monocytes as a potential biomarker for differentiating malignant and benign thyroid nodules. Elevated levels of this novel subtype of immune cells in malignant tissues suggest their role in tumor progression and their utility in enhancing diagnostic accuracy. Incorporating these findings into clinical practice may refine surgical decision-making and improve outcomes through personalized diagnostic and therapeutic strategies, particularly for radioiodine-refractory thyroid cancer. Full article