Search Results (800)

Background and Objective: Gastric cancer (GC) is a serious public health problem in southeastern Mexico. Some cases go undiagnosed or are diagnosed at advanced stages of the tumors. Borrmann classification is the method used by endoscopists to classify gastric lesions and identify tumor stage. This study aimed to characterize GC patients treated at a specialized hospital in the Yucatan Peninsula, Mexico, according to the Borrmann endoscopic classification, with a focus on clinicopathological characteristics and survival differences. Materials and Methods: A retrospective cohort study was conducted among patients aged 18 years or older who underwent an endoscopic procedure at the hospital to confirm a diagnosis of GC between January 2019 and December 2024. Clinical data were collected, including medical history, blood type, non-communicable diseases, tumor type, tumor location (primary or metastatic), and details of medical and/or surgical treatment. Survival curves were generated for all patients and stratified by the Borrmann classification. Results: A total of 209 cases of GC were included, with 115 men with a mean age of 59.3 years and 94 women with a mean age of 52.2 years. Acid peptic disease (70.3%), followed by wasting syndrome (66.9%), was the most common medical condition in patients with GC. Blood type O with a positive Rh factor was the most frequent (66.5%). According to the Borrmann classification, localized tumors (p = 0.001) were observed at lower Borrmann levels, whereas Helicobacter pylori (p = 0.040) was more frequent at higher levels. The overall survival time was 18 months for all patients; specifically, 18 months at higher Borrmann levels and 20 months at lower levels. Conclusions: GC is a highly prevalent malignancy in southeastern Mexico. The Borrmann classification remains a valuable and practical tool for evaluating GC. The association between Borrmann endoscopic classification and the clinicopathological and survival characteristics may contribute to accurate diagnosis assessment and improved prognostic stratification in future GC cases. Full article

The simultaneous detection of multiple cancer biomarkers using microfluidic multiplexed immunosensors is gaining significant interest in the field of Point-of-Care diagnostics. This study highlights integrating surface-enhanced infrared Fourier transform (SE-FTIR) with a plasmonic-active nanostructure thin film (PANTF) on a printed circuit board (PCB), housed within a microfluidic device for rapid, non-destructive detection of breast cancer (BC). Detection uses monoclonal antibody (mAb)-functionalized gold nanourchins (GNUs) on dual sensing regions. A total of 12 serum samples (24 data points) were tested for HER-II and CA 15-3. The system demonstrated a SE-FTIR enhancement factor (EF) of ~0.18 × 10⁵ using Rhodamine 6G (R6G). Calibration with HER-II (1–100 ng/mL) and CA 15-3 (10–100 U/mL) showed linear responses (R² = 0.8 and 0.76, respectively). Measurements of unknowns were performed at 1 µL/min over 68 min, with 43 min for biomarker interaction. SE-FTIR spectra were recorded at active zones and analyzed using SpectraView (SV), a custom Python 3.12-based tool. Data preprocessing included filtering (SciPy’s filtfilt) and baseline correction using the Improved Asymmetric Least Squares (IASLS) algorithm (pybaselines.Whittaker). Fourier cross-correlation (FCC) showed stronger signal consistency for HER-II. Partial Least Squares (PLS) regression, a dimensionality reduction technique, enabled clear discrimination between the samples and types, with classification accuracy reaching 1.0. Cancer staging based on these biomarkers yielded an overall accuracy of 0.54, indicating that classification regardless of biomarker type. Further studies involving larger and more diverse sample sets are critical before any definitive conclusions can be drawn. Full article

Background and Clinical Significance: Mesonephric adenocarcinoma (MA) of the uterine cervix is an exceptionally uncommon and aggressive cancer that arises from remnants of the mesonephric duct. It was first classified by the World Health Organization (WHO) in the 2020 WHO Classification of Female Genital Tumors as a type of cervical adenocarcinoma, also referred to as Gartner’s duct carcinoma. Due to its rarity, both detection and treatment pose significant challenges, and there is little information on its clinical manifestations and prognosis. Mesonephric hyperplasia (MH) in the uterine cervix is an uncommon condition that is often misdiagnosed as adenocarcinoma. Case Presentation: We present the case of a 49-year-old, asymptomatic, perimenopausal woman diagnosed with cervical mesonephric adenocarcinoma following a routine Pap smear, performed by Papanicolaou test, with a III A-B result; however, a cone biopsy revealed stage IB1 mesonephric adenocarcinoma. The patient underwent a radical hysterectomy type C (Querleu–Morrow 2017 classification). The final pathology confirmed stage IB2 of the cancer (2018 classification) according to The International Federation of Gynecology and Obstetrics (FIGO), with previous evidence of mesonephric hyperplasia from a trial abrasion performed three years earlier. Conclusions: This case highlights the challenges in recognizing and managing mesonephric hyperplasia and adenocarcinoma of the cervix. Given the uncommon nature of this cancer, clinicians should consider it when treating patients with ambiguous cervical pathology and mesonephric hyperplasia. Optimizing patient outcomes relies on early detection, accurate staging, and radical surgical treatment. Full article

Microcalcifications (HAp, CaCO₃, and CaC₂O₄) in breast tissue may indicate malignancy. Early-stage breast cancer diagnosis may benefit from the clinical application of dual-energy techniques. Dual-energy cone-beam computed tomography (CBCT) could strongly contribute to an accurate diagnosis, especially in dense breasts. This study focused on photon-counting detector alternatives to the standard cesium iodide (CsI) that CBCT currently relies on and investigated potential advantages over the employed CsI scintillators. Denser detector materials with a higher effective atomic number than CsI could improve image quality. A micro-CBCT was simulated in GATE using seven different detector configurations (CsI, bismuth germanate (BGO), lutetium oxyorthosilicate (LSO), lutetium–yttrium oxyorthosilicate (LYSO), gadolinium aluminum gallium garnet (GAGG), lanthanum bromide (LaBr₃), and cadmium zinc telluride (CZT)) and four breast tissue phantoms containing microcalcifications of both type I and type II. The dual-energy methodology was applied to planar and tomographic acquisition data. Tomographic data were reconstructed using filtered backprojection (FBP) and the ordered-subsets expectation-maximization (OSEM) algorithm. Image quality was measured using contrast-to-noise ratio (CNR) values. Both monoenergetic and polyenergetic models were considered. CZT and GAGG crystals presented higher CNR values than CsI. HAp microcalcifications exhibited the highest CNR values, which, when accompanied by OSEM, could be distinguished for classification. Detector configurations based on CZT or GAGG crystals could be adequate alternatives to CsI in dual-energy CBCT. Full article

Background: Endometrial cancer (EC) is a common malignancy found among women. It is ranked as the 6th most common cancer among women and the 15th most common cancer globally. Increasing prevalence of several factors like obesity and other metabolic disorders have caused a growing trend of prevalence of endometrial cancer. The standard approach of treatment with excellent prognosis is total hysterectomy with bilateral salpingo-oophorectomy (TH/BSO). However, due to its drawback of complete infertility, newer approaches of fertility-sparing approaches are emerging to combat this challenge. Clinicians must choose the most suitable candidates for fertility-sparing surgery (FSS) using the present existing conventional criteria with regard to the patient’s age, tumor characteristics, and fertility goals. The limitations using the conventional criteria can be eliminated by refining the criteria with molecular prognostic factors to ease the candidate selection process for FSS. Methods: Relevant literature regarding molecular subtypes, hormone therapy sensitivity, clinical assessment, and guidelines pertaining to fertility preservation in EC were retrieved from several electronic databases and articles addressing the role of molecular profiling in predicting patient response, guiding patient selection, and/or informing the development of therapies for fertility preservation in early-stage EC, particularly in women of reproductive age were included. Primary focus was on areas of consensus, emerging trends, and evidence gaps that warrant further investigation. This review will assess the integration of molecular prognostic factors to refine the patient selection criteria and guide FSS in early-stage EC. We will present existing clinical criteria, ongoing clinical trials, limitations, and the advantages of integrating molecular data on patient selection, treatment safety, and fertility outcomes. Results: Four distinct molecular subtypes have been classified which includes POLE-mut, MMR-d, p53-abn and NSMP. POLE-mut subtype had excellent prognosis with >95% patients achieving complete remission with <2% recurrence rate followed by MMRd and NSMP with intermediate prognosis and lastly p53-abn with poor prognosis of 60–70% achieving complete remission and 30–40% having recurrence. The data highlights the clinical value of molecular classification in selecting appropriate candidates for fertility sparing surgery (FSS). Conclusions: There is a lack of integration of molecular subtypes for clinicians to choose candidates for FSS and this gap should be addressed. Further research must be performed to follow personalized medicine to refine their treatment plan. Full article

Background: There is a lack of population-based and real-world data on sex and age differences in gastric cancer care. The aim of this study was to close this data gap and to analyze the sex and age differences in the clinicopathological characteristics of gastric adenocarcinoma. Methods: The analysis focused on patients diagnosed with gastric adenocarcinomas (ICD-10: C16.0–C16.9) documented in the cancer registry of the Federal State of Brandenburg from 2000 to 2020. The patient variables include sex, age at time of tumor diagnosis and the ECOG performance status. The tumor variables included location, grading, clinical TNM classification, clinical UICC stage and synchronous distant metastasis. Results: Of n = 8582 patients, 38% (n = 3263) were women. Compared with males, females had fewer adenocarcinomas located in the cardia (24.1% vs. 12.5%, p < 0.001), more signet ring cell carcinomas (13.2% vs. 22.8%, p < 0.001), more high-grade tumors (55.4% vs. 63.1%, p < 0.001) and a higher tumor cUICC stage (cUICC IV: 44.9% vs. 48.4%, p = 0.001). There was an interaction between sex and age modulating the differences between males and females. Conclusions: We were able to demonstrate several relevant prognostic differences in gastric cancer between men and women in terms of tumor location, stage, and metastases in a large patient cohort. Full article

Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths globally, making a timely and reliable diagnosis essential. Manual histopathology assessment, though clinically standard, is prone to observer variability, while existing computational approaches often trade accuracy for interpretability, limiting their clinical utility. This paper introduces a deep learning framework that couples the YOLOv8 architecture for multiclass lesion classification with EigenCAM for transparent model explanations. The pipeline integrates three core stages: (i) acquisition and preprocessing of 5000 hematoxylin-and-eosin-stained slides from the University Medical Center Mannheim, categorized into eight tissue types; (ii) comparative evaluation of five YOLOv8 variants (Nano, Small, Medium, Large, XLarge); and (iii) interpretability through EigenCAM visualizations to highlight discriminative regions driving predictions. Extensive statistical validation (including box plots, empirical cumulative distribution functions, Bland–Altman plots, and pair plots) demonstrated the robustness and reliability of the framework. The YOLOv8 XLarge model achieved 99.38% training accuracy and 96.62% testing accuracy, outperforming recent CNN- and Transformer-based systems (≤95%). This framework establishes a clinically dependable foundation for AI-assisted CRC diagnosis by uniting high precision with visual interpretability. It represents a significant step toward real-world deployment in pathology workflows. Full article

Background/Objective: Biochemical recurrence (BCR) after radical prostatectomy (RP) for prostate cancer indicates disease progression. Although type 2 diabetes mellitus (T2D) shows a paradoxical association with prostate cancer risk, the prognostic role of T2D-related genetic variants remains unclear. Methods: We analyzed 113 common T2D susceptibility-related single-nucleotide polymorphisms (SNPs) in 644 Taiwanese men with localized prostate cancer (D’Amico risk classification: 12% low, 34% intermediate, and 54% high) treated with RP. Associations between SNPs and BCR were assessed using Cox regression, adjusting for key clinicopathological factors. Functional annotation was performed using HaploReg and FIVEx, while The Cancer Genome Atlas transcriptomic data were analyzed for C2 calcium-dependent domain-containing 4A (C2CD4A) expression. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to explore related biological pathways. Results: C2CD4A SNP rs4502156 was independently associated with a reduced risk of BCR (hazard ratio = 0.80, p = 0.035). The protective C allele correlated with higher C2CD4A expression. Low C2CD4A expression is associated with advanced pathological stages, higher Gleason scores, and disease progression. GSEA revealed negative enrichment of mitotic and chromatid segregation pathways in high-C2CD4A-expressing tumors, with E2F targets being the most suppressed. GSVA confirmed an inverse correlation between C2CD4A expression and E2F pathway activity, with CDKN2C as a co-expressed functional gene. Conclusions: The T2D-related variant rs4502156 in C2CD4A independently predicts a lower risk of BCR, potentially via suppression of the E2F pathway, and may serve as a germline biomarker for postoperative risk stratification. Full article

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article

Background/Objectives: Breast cancer incidence is increasing in younger Ghanaian women. However, few epidemiological studies have evaluated the modifiable risk factors in this population. Additionally, these studies have classified breast cancer in Ghanaian women based on the global menopausal case classification. This study reclassified premenopausal and postmenopausal breast cancer in a Ghanaian cohort, assessing the risk factors using the observed menopausal age in Ghanaian women of 48 years, rather than the global standard of 50 years. Methods: Women diagnosed with breast cancer and scheduled for surgery from December 2018 to March 2023 were recruited across four hospitals in Ghana for the Ghana Breast Cancer Omics Project (BCOPGh), and data were collected using a questionnaire. Cross-tabulation and linear regression were used to evaluate the relationships between categorical variables and age at diagnosis. Results: Out of a total of 262 women recruited, 34.4% were classified as having premenopausal breast cancer, while early-onset breast cancer (EOBC) accounted for 14.9% of all cases. The molecular subtypes were predominantly hormone receptor (HR)-positive (61%) while triple-negative breast cancer (TNBC) accounted for 16%. The tumours were predominantly at stage II (62%) and grade 2 (51%), with invasive carcinoma NST (56%) being the most common subtype. Within this cohort, nulliparity increased the odds of EOBC by 13.5-fold, while having a first birth after the age of 23 doubled the odds of premenopausal breast cancer. Reproductive factors (menarche and menopause) and lifestyle (alcohol intake, smoking, contraceptive use, and breastfeeding duration) were not associated with premenopausal breast cancer in this cohort. About 13% of participants reported a family history of breast cancer, and 79% had prior knowledge of the disease. Conclusion: This study supports previous reports of the relatively higher incidence of aggressive disease in young Ghanaian women and the protective effect of early age at first birth. It further underscores the need to investigate its genetic underpinnings, whilst highlighting the importance of public education on self-examination techniques to reduce advanced disease presentation in Ghanaian women. Full article

Objectives: Endometrial carcinoma is the most common gynaecological cancer in developed countries, with both incidence and mortality rates continuing to rise globally. For women of reproductive age diagnosed with early-stage disease or endometrial intraepithelial neoplasia, fertility-preserving treatment should be considered to maintain the possibility of future childbearing. Effective fertility-sparing management requires a multidisciplinary approach that includes patient education, reduction in risk factors, accurate molecular and histological classification to guide targeted therapies, assisted reproductive technologies to improve early conception rates, and attention to the psycho-sexual well-being of patients to support treatment adherence. Methods: This retrospective cohort study analysed the clinicopathological features and treatment outcomes of thirteen patients who received fertility-preserving therapy between 2018 and 2023. Results: The mean age of the patients (n = 13) was 34.4 years, with a range of 20 to 41 years. The overall treatment response rate was 76.9%, including 69.2% complete and 7.7% partial responses. Stable disease was observed in 15.4% of cases, while progression occurred in 7.7%. Among those who achieved complete remission, in vitro fertilisation (IVF) was initiated in four cases, with two ongoing as of the time of data analysis. In one of the cases, after two unsuccessful assisted reproductive attempts, spontaneous conception occurred, resulting in the birth of a child. Conclusions: Our findings support the feasibility and success of fertility-preserving treatment in carefully selected patients, allowing the preservation of reproductive potential alongside oncological care. Full article

Background/Objective: Esophageal cancer (EC) is a major global health issue due to its high mortality rate, as patients are often diagnosed at advanced stages. This research examines whether the Spectrum-Aided Vision Enhancer (SAVE), a hyperspectral imaging (HSI) technique, enhances endoscopic image categorization for superior diagnostic outcomes compared to traditional White Light Imaging (WLI) and Narrow Band Imaging (NBI). Methods: A dataset including 2400 photos categorized into eight disease types from National Taiwan University Hospital Yun-Lin Branch was utilized. Multiple machine learning and deep learning models were developed, including logistic regression, VGG16, YOLOv8, and MobileNetV2. SAVE was utilized to transform WLI photos into hyperspectral representations, and band selection was executed to enhance feature extraction and improve classification outcomes. The training and evaluation of the model incorporated precision, recall, F1-score, and accuracy metrics across WLI, NBI, and SAVE modalities. Results: The research findings indicated that SAVE surpassed both NBI and WLI by achieving superior precision, recall, and F1-scores. Logistic regression and VGG16 performed with a comparable reliability to SAVE and NBI, whereas MobileNetV2 and YOLOv8 demonstrated inconsistent yet enhanced results. Overall, SAVE exhibited exceptional categorization precision and recall, showcasing impeccable performance across many models. Conclusions: This research indicates that AI hyperspectral imaging facilitates early diagnosis of esophageal diseases, hence enhancing clinical decision-making and improving patient outcomes. The amalgamation of SAVE with machine learning and deep learning models enhances diagnostic capabilities, with SAVE and NBI surpassing WLI by offering superior tissue differentiation and diagnostic accuracy. Full article

Malignant pleural effusion, commonly referred to as MPE, is a prevalent complication associated with individuals diagnosed with neoplastic disorders. The data acquired by pleural fluid cytology is beneficial for diagnostic objectives. Consequently, the initial step in the diagnostic procedure for lung cancer is the analysis of pleural effusion fluid. This research aims to provide a cutting-edge model for analyzing PE cytology images. This model utilizes a computer-aided diagnosis (CAD) system that integrates hyperspectral imaging (HSI) technology for the classification of spectral variations. Giemsa, which is one of the most popular microscopic stains, is employed to stain the samples, after which a sensitive CCD mounted on a microscope captures the images. Subsequently, the HSI model is tasked with obtaining the image spectra. Principal Component Analysis (PCA) constitutes the concluding phase in the classification procedure of various cell types. We expect that the suggested technique will enable medical professionals to stage lung cancer more rapidly. In the future, we aspire to develop an extensive data system that utilizes deep learning techniques to facilitate the automatic classification of cells, thereby ensuring the most precise diagnosis. Furthermore, enhancing accuracy and minimizing data dimensions are important priorities to accelerate diagnostics, conserve resources, and reduce computing time. Full article

Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The early detection of BC is crucial; yet, conventional diagnostic techniques, including MRI, mammography, and biopsy, are costly, time-intensive, less sensitive, incorrect, and necessitate skilled physicians. This narrative review will examine six novel imaging approaches for BC diagnosis. Methods: Optical coherence tomography (OCT) surpasses existing approaches by providing non-invasive, high-resolution imaging. Raman Spectroscopy (RS) offers detailed chemical and structural insights into cancer tissue that traditional approaches cannot provide. Photoacoustic Imaging (PAI) provides superior optical contrast, exceptional ultrasonic resolution, and profound penetration and visualization capabilities. Hyperspectral Imaging (HSI) acquires spatial and spectral data, facilitating non-invasive tissue classification with superior accuracy compared to grayscale imaging. Contrast-Enhanced Spectral Mammography (CESM) utilizes contrast agents and dual energy to improve the visualization of blood vessels, enhance patient comfort, and surpass standard mammography in sensitivity. Multispectral Imaging (MSI) enhances tissue classification by employing many wavelength bands, resulting in high-dimensional images that surpass the ultrasound approach. The imaging techniques studied in this study are very useful for diagnosing tumors, staging them, and guiding surgery. They are not detrimental to morphological or immunohistochemical analysis, which is the gold standard for diagnosing breast cancer and determining molecular characteristics. Results: These imaging modalities provide enhanced sensitivity, specificity, and diagnostic accuracy. Notwithstanding their considerable potential, the majority of these procedures are not employed in standard clinical practices. Conclusions: Validations, standardization, and large-scale clinical trials are essential for the real-time application of these approaches. The analyzed studies demonstrated that the novel modalities displayed enhanced diagnostic efficacy, with reported sensitivities and specificities often exceeding those of traditional imaging methods. The results indicate that they may assist in early detection and surgical decision-making; however, for widespread adoption, they must be standardized, cost-reduced, and subjected to extensive clinical trials. This study offers a concise summary of each methodology, encompassing the methods and findings, while also addressing the many limits encountered in the imaging techniques and proposing solutions to mitigate these issues for future applications. Full article

Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care. Full article