Search Results (5,419)

Background/Objectives: Early-onset colorectal cancer (EO-CRC) incidence continues to rise globally, yet age-stratified risk data for adults under 45 remain limited. Methods: This retrospective, single-center, colonoscopy-based cohort study included 3959 examinees aged 18–44 at Peking University International Hospital (2023–2024) and used Joinpoint regression to identify age inflection points for polyp detection rates. Case–control analysis with Least Absolute Shrinkage and Selection Operator (LASSO)-penalized logistic regression assessed metabolic risk factors for adenoma and serrated polyps in the 40–44 stratum. Results: Detection rates for polyps, adenomas, and serrated lesions all rose with age (p < 0.001). From the youngest (18–29) to the oldest (40–44) group, the polyp detection rate increased 2.6-fold, the adenoma detection rate (ADR) 4.3-fold, and the serrated lesion detection rate 2.0-fold. Joinpoint regression revealed an ADR inflection at age 33 (annual percentage change (APC): +0.51% → +1.27%), a high-risk adenoma (HRA) inflection at age 39 (APC: +0.14% → +0.77%), and an advanced-neoplasia inflection also at age 39 (APC: +0.20% → +0.81%). Across age strata, adenoma and advanced-neoplasia detection rates were comparable between asymptomatic screening and symptomatic examinees from age 35 onward (40–44 ADR 20.5% vs. 21.6%), whereas symptomatic examinees had higher rates in the youngest strata. In exploratory cross-sectional analyses within the 40–44 group, total cholesterol (odds ratio, OR = 1.47) and body mass index (BMI) (OR = 1.05) showed associations with adenoma, without establishing causality, with BMI elevation conferring risk only in women (p for interaction = 0.018). Conclusions: Adenoma risk accelerates at 33 and high-risk lesions escalate at 39, providing age-stratified risk benchmarks for adults under 45 in a predominantly symptomatic Chinese cohort. These inflection points warrant prospective validation in asymptomatic screening populations. Full article

Background: Cervical cancer remains one of the leading causes of cancer-related morbidity among women worldwide. The Papanicolaou (Pap) smear is widely used for early detection; however, its manual interpretation is time-consuming, requires substantial expertise, and is often affected by inter-observer variability, particularly in cases with dense and overlapping cells. Methods: This study developed an optimized deep learning framework for cervical cell instance segmentation, specifically targeting the separation of overlapping cells in Pap smear images. The proposed framework was based on Mask R-CNN with a ResNet-50 backbone and Feature Pyramid Network. A public development dataset of 460 cervical smear images was used for model development and internal evaluation, while an independent 210-image dataset collected from King Abdullah University Hospital was reserved as an external held-out clinical assessment set. To improve small-cell detection and mask refinement, the Region Proposal Network was adapted using biologically informed anchor scales (16, 32, 64, 128, 256), followed by soft calibration-based post-processing. Results: The proposed framework achieved an AP50 of 89.90% and a Mask IoU of 90.44%. Small-cell performance, measured using APs under the COCO small-object convention, reached 22.80%, while APm and APl reached 68.45% and 81.42%, respectively. Clinical cell-count evaluation on the independent KAUH held-out set was performed using the mean count of five physicians as the reference standard and achieved an overall clinical detection accuracy of 93.00%. Conclusions: The optimized Mask R-CNN framework improved the detection and separation of overlapping cervical cells in Pap smear images and may serve as a supportive tool for cytopathology workflows. The results suggest that biologically informed anchor optimization and soft calibration can improve cell-level instance segmentation, particularly for small and overlapping cells. Further validation on larger multi-center datasets remains necessary before routine clinical deployment. Full article

Cancer risk estimation remains one of the main unresolved challenges in human spaceflight beyond low Earth orbit, where astronauts are exposed to galactic cosmic rays, solar particle events, and high-linear energy transfer (high-LET) secondary radiation. This narrative review summarizes the principal quantitative models used to estimate radiation-induced cancer risk in astronauts, including particle fluence-based cross-sections, mixture models, risk of exposure-induced death (REID)-based operational frameworks, uncertainty distribution approaches, and ensemble models. Early studies estimated 1-year excess cancer mortality at solar minimum as 1.3% in women and 1.1% in men under 10 g/cm² aluminum shielding, whereas later models projected non-leukemia lifetime cancer incidence after 1 Sv dose equivalent/effective dose between 2.20% and 2.98%, depending on sex and age. Earlier REID-based models suggested that the historical 3% REID threshold could be exceeded after approximately 18 months in women and 24 months in men under unfavorable solar conditions, whereas the current NASA radiation standard uses a universal career-effective dose limit of 600 mSv, applied regardless of sex or age. More recent revisions of the NASA Space Cancer Risk model and non-targeted effect scenarios suggest that exploration mission risks may be higher than previously estimated, while uncertainty remains substantial, especially for high-LET radiobiology, mixed-field exposure, and the transfer of terrestrial epidemiological data to the spaceflight setting. Future progress may also involve exploring quantitative imaging biomarkers and tomographic assessments as complementary tools for longitudinal monitoring and early detection of radiation-related tissue changes, although these approaches are not yet validated as components of operational astronaut cancer risk models. Full article

Colorectal cancer (CRC) remains a major global health burden, and early detection through population-based screening programs significantly reduces both incidence and mortality. Although gut microbiome-based biomarkers have emerged as promising non-invasive tools for CRC detection, limited evidence is available regarding patient acceptance and compliance with microbiome-based screening studies, factors that may influence their future implementation in clinical practice. We conducted a preliminary analysis of an ongoing multicenter, prospective observational study designed to develop a gut microbiome-based diagnostic tool for CRC and advanced colorectal adenomas in fecal immunochemical test (FIT)-positive individuals. The primary objective of this preliminary analysis was to evaluate patient acceptance and compliance with participation in a microbiome-based study within an organized CRC screening setting. Secondary objectives included describing the clinical, endoscopic, and histopathological characteristics of the enrolled cohort. FIT-positive individuals referred for screening colonoscopy at participating Italian centers were screened for eligibility, underwent colonoscopy, and were invited to provide a stool sample for microbiome analysis. A total of 315 individuals were screened, of whom 212 (67%) were enrolled. Among eligible patients, 90% agreed to enroll after receiving study information. Overall, 200 (94%) of enrolled individuals completed the required study activities, including stool sample collection and colonoscopy, indicating high compliance with study procedures. Colonoscopy was performed in 209 patients (99% of enrolled patients). CRC was detected in 7 patients (3%), and advanced colorectal adenomas in 39 (18%), while 86 (41%) colonoscopies were negative. The positive predictive value of FIT was 3.35% for CRC and 18.66% for advanced adenomas. In our preliminary analysis, patient acceptance and compliance with microbiome-based sampling were high among FIT-positive individuals undergoing CRC screening. These findings support the feasibility of conducting microbiome-based studies within organized screening programs. Analyses aimed at developing and validating the microbiome-based diagnostic tool are currently ongoing and are beyond the scope of the present report. Full article

Background: The increasing use of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in patients with breast cancer has led to a growing number of incidentally detected thyroid lesions. Thyroid incidentaloma refers to a focal area of increased metabolic activity within the thyroid gland. Up to 35% of focal thyroid incidentalomas are malignant. Early detection of thyroid carcinoma may allow less invasive surgical management and reduce the need for more extensive treatment associated with advanced disease, including total thyroidectomy, neck dissection, and radioactive iodine therapy. The objective of this study was to estimate the prevalence of focal thyroid incidentaloma and incidental thyroid carcinoma detected on PET/CT scans performed in breast cancer patients at a single tertiary medical center serving a highly ethnically diverse population. Methods: Medical records and PET/CT scans were retrospectively reviewed at a single medical center. Data collected included the presence of thyroid incidentaloma, maximum standardized uptake value (SUVmax), thyroid cytology, primary malignancy type, breast cancer status, ethnicity, and age. A total of 1233 patients with cancer who underwent PET/CT imaging and received treatment at ZIV Medical Center between August 2018 and December 2024 were included. Forty-two patients with primary thyroid carcinoma or head and neck carcinoma were excluded. Patients were categorized into breast cancer and non-breast cancer groups and compared regarding the prevalence of thyroid incidentaloma, referral rates for further evaluation by head and neck specialists, and the rate of incidental thyroid carcinoma. Results: Among 330 patients with breast cancer, 16 (4.8%) had a focal incidental thyroid finding, compared with 24 (2.8%) of 861 patients with non-breast malignancies who underwent PET/CT imaging. Among the 16 breast cancer patients with thyroid incidentaloma, thyroid carcinoma was subsequently confirmed in 4 patients (25%) who underwent further evaluation. The proportion of patients with confirmed thyroid carcinoma among those with incidentalomas was 1.2% (4/330) in the breast cancer group compared with 0.46% (4/861) in the non-breast cancer group. Approximately half of the patients in both groups were referred for further thyroid evaluation. Among breast cancer patients, non-significant trends toward higher referral rates were observed in patients with non-advanced disease, higher SUVs, and Jewish ethnicity. Nearly all patients evaluated by head and neck specialists underwent fine-needle aspiration (FNA). The mean SUV among patients diagnosed with papillary thyroid carcinoma (PTC) on FNA (n = 8; breast and non-breast cancer combined) was 15.2 (IQR: 7.3–17.0), compared with 5.2 (IQR: 5.0–6.0) among patients with benign cytology (n = 5; p = 0.011). Conclusions: Thyroid incidentalomas identified on PET/CT scans in breast cancer patients were relatively common in this cohort, and a proportion of evaluated lesions were subsequently diagnosed as thyroid carcinoma. Referral and diagnostic follow-up rates were variable, with approximately half of patients referred for further evaluation and only one-third attending a head and neck clinic. Given the retrospective design, limited number of biopsy-confirmed cases, and potential verification bias, these findings should be interpreted with caution. Nevertheless, they highlight the importance of clinical awareness and support individualized assessment of PET/CT-detected thyroid incidentalomas rather than broad management recommendations. Full article

Cervical cancer remains a major global public health challenge, and the accurate classification of cervical transformation zones (TZs) constitutes a critical step in early detection and clinical decision-making. However, distinguishing between Type 2 and Type 3 transformation zones remains particularly challenging due to their high morphological similarity and the inherent interobserver variability associated with colposcopic assessment. In this study, we propose a novel Dual-Track Specialist Feature Fusion and Meta-Learning Stacking Ensemble architecture for the automated classification of cervical transformation zones using the Intel & MobileODT Cervical Cancer Screening dataset. The proposed framework integrates a global feature extractor based on ResNet50 (Gatekeeper) with a visual specialist based on InceptionResNetV2, trained exclusively on the most diagnostically ambiguous cases (Type 2 and Type 3). The extracted features are fused and processed through a multi-level stacking scheme composed of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting (GB), XGBoost, and LightGBM classifiers at the base level, followed by an XGBoost meta-learner and a clinically guided probability calibration strategy designed to maximize diagnostic sensitivity. Experimental results demonstrate a peak overall accuracy of 91.22%, substantially outperforming the baseline ResNet50 model (70%). Furthermore, the proposed system achieved Recall values of 0.90, 0.90, and 0.94 for Type 1, Type 2, and Type 3 transformation zones, respectively, highlighting its ability to accurately identify diagnostically challenging cases. Ablation studies, Grad-CAM visualizations, and external-image validation experiments confirm that the proposed architecture improves discrimination between ambiguous categories, learns clinically meaningful representations, and maintains strong generalization capability across heterogeneous scenarios. These findings demonstrate the potential of visual specialization and calibrated meta-learning strategies for the development of artificial intelligence-assisted colposcopic decision-support systems. Full article

Cervical cancer is the fourth most frequent malignancy in women globally. Pap smear screening is important for early cancer detection, but manual smear analysis is time-consuming, labor-intensive and error-prone for diagnosis. Such issues in resource-limited areas have led to the introduction of deep learning (DL) methods for automated cervical cancer diagnosis. But the majority of current methodologies depend on models pretrained on natural image datasets like ImageNet, which may inadequately represent domain-specific pathological characteristics. To mitigate this constraint, this research employs a domain-specific transfer learning algorithm approach using the PathMNIST histopathological dataset to enhance cervical cell classification. An accuracy score of 96.77% is achieved for the proposed YOLO* model, using the SIPAKMED dataset. To the best of available knowledge, no previous study has reported the use of PathMNIST as a pretraining source for cytology image classification. As domain-specific medical pretraining is becoming more popular, our study shows the importance of cross-domain generalization. Full article

Background/Objectives: Gastric precancerous conditions and early gastric cancer represent a heterogeneous disease spectrum with variable malignant potential and complex management pathways. Despite well-established international guidelines, discrepancies remain between recommended strategies and routine clinical practice, particularly regarding endoscopic diagnosis, risk stratification, therapeutic selection, and follow-up. This review aims to synthesize current evidence and provide practice-oriented, question-based guidance for the management of gastric precancerous lesions and early gastric cancer. Methods: A comprehensive review of the literature was conducted using PubMed and Google Scholar, focusing on endoscopic diagnosis, histological risk assessment, therapeutic options, and surveillance strategies for gastric precancerous lesions and early gastric cancer. Key areas of clinical uncertainty and controversy were identified and translated into focused, practice-oriented clinical questions designed to reflect and possibly help to improve real-world gastroenterological practice. Results: Clinical questions were formulated to cover the entire management pathway, from endoscopic detection and characterization to therapeutic decision-making and post-treatment surveillance. Topics include high-quality endoscopic diagnosis, biopsy strategies, histological staging system, selection between endoscopic and surgical therapy, and follow-up according to individual risk profile. For each question, current evidence is summarized into concise, actionable recommendations. Conclusions: Management of gastric precancerous lesions and early gastric cancer requires a structured and individualized approach, integrating high-quality endoscopy, accurate histological risk stratification, and evidence-based therapeutic and surveillance strategies. Organizing available evidence into practice-oriented clinical questions may help harmonize clinical practice, reduce unwarranted variability, and support gastroenterologists in delivering optimal patient-centered care. Full article

Background: Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, necessitates improved non-invasive diagnostic and monitoring tools. Circulating tumor cells (CTCs), as intact cellular biomarkers in liquid biopsies, offer valuable morphological and genetic information and hold significant clinical potential for early detection, prognosis, therapy monitoring, and drug development. Bio-ferrography is a non-invasive immunomagnetic separation technique that isolates magnetically labeled entities from fluid samples onto a glass substrate via a focused external magnetic field. Methods: This study employs, for the first time, bio-ferrography for isolation, counting, and microscopic characterization of circulating tumor cells (CTCs) expressing the human epidermal growth factor receptor (EGFR) from blood biopsies taken from patients in the hospital. Magnetic beads conjugated with anti-EGFR antibodies were used to selectively capture CTCs from peripheral blood samples of patients with metastatic CRC and other epithelial malignancies. The method enabled both enumeration and microscopic characterization of isolated cells. Results: Preliminary clinical results demonstrate that bio-ferrography achieves a sensitivity of 90% in stage-IV patients and exhibits higher true positive detection rates compared to conventional tumor biomarkers, including carbohydrate antigen 19-9 (CA 19-9) and carcinoembryonic antigen (CEA). Conclusions: These findings highlight the potential of bio-ferrography as a robust platform for CTC isolation and analysis. Full article

Gastric cancer is a globally prevalent malignancy, with early detection being pivotal for improving patient survival. While endoscopy remains the diagnostic gold standard, it frequently faces challenges such as missed lesions and operator dependency. Artificial intelligence (AI) has emerged as a powerful tool to address these limitations. This narrative review synthesizes recent evidence from PubMed and Web of Science, focusing on four core functional domains of AI-assisted gastric endoscopy: lesion detection and characterization, margin delineation, invasion depth prediction, and blind-spot monitoring. Furthermore, we summarize current limitations, including single-center data biases and algorithmic “black-box” issues, and discuss future directions such as multimodal data integration and real-time video analysis systems. Ultimately, carefully validated AI represents a vital clinical adjunct that holds great potential to significantly enhance diagnostic accuracy and patient outcomes. Full article

The detection of RASSF1A (Ras-associated domain family 1 isoform A) promoter methylation in body fluids can offer a powerful tool for the early diagnosis of bladder cancer. Zinc finger proteins (ZFPs) serve as sequence-specific recognition elements for targeting double-stranded DNA sequences. Here, we report a cyclic olefin copolymer (COC) chip-integrated ZFP array-based molecular sensor that bypasses the need for bisulfite conversion and PCR amplification to recognize the specific site of DNA methylation in the RASSF1A promoter. Building upon the SEER-LAC (SEquence-Enabled Reassembly of β-Lactamase) framework, we engineered a dual-recognition split-enzyme system in which a COC chip-immobilized ZFP array confers sequence specificity while a co-recruited methyl-binding domain (MBD) enforces methylation-dependent gating, together driving the proximity-induced reconstitution of functional β-lactamase at methylated target loci. Accordingly, this sensor specifically reassembles and restores enzymatic activity only in the presence of specific methylated DNA in the RASSF1A promoter region. We demonstrate that this dual-component array effectively differentiates methylation status with high specificity. Given its rapid turnaround and non-PCR-based mechanism, this system can be well-suited for developing diagnostic assays for bladder cancer, offering a potential alternative to conventional epigenetic screening methods. Full article

Background/Objectives: The N-NOSE test is a non-invasive, urine-based multi-cancer screening assay that uses Caenorhabditis elegans chemotaxis toward cancer-associated volatile organic compounds in human urine. Scaling the test from a manual research-grade workflow to a high-throughput clinical service has required automation, and the central question this raises, centering around whether mechanization alters the analytical performance of the test, must be answered with operational, not bench-top, data. Methods: Here, we present a nine-month (January–September 2023) real-world, side-by-side comparison of the two workflows operating under their actual routine clinical laboratory conditions: the manual chemotaxis assay performed by trained technicians at the Fukuoka Research and Development Center (R&D) and the fully automated Chemotaxis Scoring Apparatus (CSA) running continuously at the Tokyo Testing Center. Results: The manual workflow generated 551 paired chemotaxis index (CI) measurements from positive-control (PC) and negative-control (NC) synthetic urine/volatile organic compound (VOC)-mimic reference materials at each of two standard urine dilutions (10⁻¹ and 10⁻²); over the same period, the CSA processed 2448 quality control samples (612 per control type) with both biobank-derived urine-based comparison materials and synthetic volatile organic compound reference standards. Both workflows produced large, highly significant, and quantitatively comparable PC-versus-NC separation under genuine operating conditions (manual: Δ_CI = 0.096 and 0.103; Welch’s t = 19.83 and 21.95; p < 0.0001; Cohen’s d = 1.19 and 1.32; CSA risk scale Δ_P–N = 14.47 with biobank-derived urine-based materials and 10.17 with synthetic VOC standards). The CSA risk score is a linear, monotonic transformation of the CI. Standardized separation is directly comparable across workflows and is concordant (Cohen’s d: manual 1.19–1.32; CSA 0.80–1.44, all large); the manual and automated processes therefore show no meaningful difference in discriminative performance. Because the CSA mechanizes only the handling of worms, samples, and machine-vision counting around an unchanged biological transducer, the live nematode, analytical equivalence is the predicted outcome, and these data confirm it at scale in a real clinical laboratory setting. Conclusions: Automation of the N-NOSE process does not compromise its ability to discriminate cancer from non-cancer urine. These results provide real-world evidence supporting the validity, reproducibility, and reliability of the N-NOSE testing process and large-scale validation studies. Full article

Over the last few decades, survival among patients with lung cancer (LC) has progressively improved due to major advances in treatment strategies, particularly the introduction of immunotherapy and targeted therapies, as well as the increased detection of early-stage disease resulting from the widespread use of chest computed tomography (CT). Although the reduction in mortality, frequently achieved through effective control of the primary disease, represents a major therapeutic success, it also raises new clinical challenges, including the long-term management of cancer remission or disease stability and the competing risk of adverse outcomes related to comorbidities and treatment-related toxicities. Among these, cardiovascular (CV) complications have emerged as particularly relevant because of their frequency and prognostic impact. Within the framework of a holistic long-term management approach, increasing attention should be directed toward non-pharmacological interventions targeting lifestyle factors, particularly nutrition and physical exercise, whose role remains underestimated. These interventions may modulate chronic inflammation and immune responses, which are key drivers influencing both the effectiveness of novel anticancer therapies and the progression of cardiovascular complications. Patients with LC frequently present malnutrition and unfavorable lifestyle patterns associated with substantial physical and psychological stress, factors that may negatively affect treatment outcomes and overall prognosis. This narrative review examines the emerging role of targeted nutritional strategies and structured physical exercise as integral components of supportive care in LC, with a specific focus on their impact on cardiac metabolism, CV risk, and response to anticancer therapies, including immunotherapy. Full article

Pancreatic cancer ranks among the most lethal malignancies, characterized by a five-year survival rate of approximately 10%. This dismal prognosis is largely attributable to diagnoses occurring at advanced stages and the inherent limitations of conventional imaging modalities in detecting early lesions, identifying metastases, and assessing tumor heterogeneity. Consequently, there is a critical need for non-invasive imaging techniques capable of visualizing pancreatic cancer lesions to enable accurate diagnosis, risk assessment, and the development of personalized treatment strategies. Molecular imaging, which combines highly specific targeted probes with advanced imaging technologies, offers the potential to elucidate disease-associated pathways. This review provides a comprehensive overview of recent advancements in molecular imaging platforms for pancreatic cancer, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), optical molecular imaging, photoacoustic imaging, and molecular MRI. We begin by elucidating the biological rationale for targeting key molecules, including fibroblast activation protein (FAP), integrins, and programmed death ligand 1 (PD-L1). Moreover, we critically evaluate the development and clinical translation of these probes, highlighting their ability to enhance lesion detectability, characterize intratumoral heterogeneity, and guide both targeted therapy and surgical resection. Compared with existing reviews, this work uniquely integrates a comprehensive cross-modality analysis of the latest molecular imaging strategies for pancreatic cancer. Furthermore, we examine prevailing challenges and emerging frontiers in this domain, specifically focusing on multimodal hybrid imaging, artificial intelligence-driven analytics, and integrated theranostic platforms as pivotal strategies to advance precision oncology. Full article

Early detection of lung cancer remains challenging due to the extremely low concentrations of disease-specific biomarkers, which limit the development of highly sensitive and reliable point-of-care (PoC) diagnostic devices. Electrochemical biosensors integrating graphene oxide (GO) and gold nanoparticles (AuNPs) have emerged as promising platforms for the rapid, sensitive, and selective detection of lung cancer biomarkers, enabling more timely diagnosis. Biomarkers such as carcinoembryonic antigen (CEA), cytokeratin-19 fragments (CYFRA 21-1), neuron-specific enolase (NSE), and circulating tumour DNA are increasingly investigated for PoC applications since they can be detected in various biological fluids associated with lung cancer. Nanocomposite materials, particularly GO/AuNP hybrids, provide synergistic advantages by combining the large surface area and abundant functional groups of GO for stable immobilization of biorecognition elements with the excellent conductivity and bioconjugation capability of AuNPs that enhance signal transduction. This review critically discusses key biomarker targets for lung cancer, the properties of GO and Au in biosensing, and the role of AuNP/GO nanocomposites in improving biosensor performance. It further examines the application of electrochemical biosensors for lung cancer biomarker detection, highlighting recent developments. Additionally, the review outlines current challenges limiting clinical translation and PoC implementation, provides recommendations to address these barriers, and discusses future perspectives for improving the detection of low-abundance biomarkers for early lung cancer diagnosis. Ultimately, these technologies seem promising for the development of rapid diagnostic tools equivalent to established platforms such as lateral-flow immunoassays. Full article