Search Results (252)

Background/Objectives: Acute kidney injury (AKI) is a common and severe complication in patients with acute myocardial infarction (AMI). Very elderly patients are at a heightened risk of developing AKI. Fibrinogen and albumin are well-known biomarkers of inflammation and nutrition, which are highly related to AKI. We aim to explore the predictive value of the fibrinogen-to-albumin ratio (FAR) for AKI in very elderly patients with AMI. Methods: A retrospective cohort of AMI patients ≥ 75 years old hospitalized at the First Affiliated Hospital of Xi’an Jiaotong University between January 2018 and December 2022 was established. Clinical data and medication information were collected through the biospecimen information resource center at the hospital. Univariate and multivariable logistic regression models were used to analyze the association between FAR and the risk of AKI in patients with AMI. FAR was calculated as the ratio of fibrinogen (FIB) to serum albumin (ALB) level (FAR = FIB/ALB). The primary outcome is acute kidney injury, which was diagnosed based on KDIGO 2012 criteria. Results: Among 1236 patients enrolled, 66.8% of them were male, the median age was 80.00 years (77.00–83.00), and acute kidney injury occurred in 18.8% (n = 232) of the cohort. Comparative analysis revealed significant disparities in clinical characteristics between patients with or without AKI. Patients with AKI exhibited a markedly higher prevalence of arrhythmia (51.9% vs. 28.1%, p < 0.001) and lower average systolic blood pressure (115.77 ± 25.96 vs. 122.64 ± 22.65 mmHg, p = 0.013). In addition, after adjusting for age, sex, history of hypertension, left ventricular ejection fraction (LVEF), and other factors, FAR remained an independent risk factor for acute kidney injury (OR = 1.47, 95%CI: 1.36–1.58). ROC analysis shows that FAR predicted stage 2–3 AKI with superior accuracy (AUC 0.94, NPV 98.6%) versus any AKI (AUC 0.79, NPV 93.0%), enabling risk-stratified management. Conclusions: FAR serves as both a high-sensitivity screening tool for any AKI and a high-specificity sentinel for severe AKI, with NPV-driven thresholds guiding resource allocation in the fragile elderly. Full article

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims to examine the relationship of ACEs with incident osteoporosis and investigate interactions with polygenic risk score (PRS). Materials and Methods: This study analyzed 124,789 UK Biobank participants initially free of osteoporosis. Cumulative ACE burden (emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse) was ascertained through validated questionnaires. Multivariable-adjusted Cox proportional hazards models assessed osteoporosis risk during a median follow-up of 12.8 years. Moderation analysis examined genetic susceptibility interactions using a standardized PRS incorporating osteoporosis-related SNPs. Results: Among 2474 incident osteoporosis cases, cumulative ACEs showed dose–response associations with osteoporosis risk (adjusted hazard ratio [HR]_{per one-unit increase} = 1.07, 95% confidence interval [CI] 1.04–1.11; high ACEs [≥3 types] vs. none: HR = 1.26, 1.10–1.43). Specifically, emotional neglect (HR = 1.14, 1.04–1.25), emotional abuse (HR = 1.14, 1.03–1.27), physical abuse (HR = 1.17, 1.05–1.30), and sexual abuse (HR = 1.15, 1.01–1.31) demonstrated comparable effect sizes. Sex-stratified analysis revealed stronger associations in women. Joint exposure to high ACEs/high PRS tripled osteoporosis risk (HR = 3.04, 2.46–3.76 vs. low ACEs/low PRS) although G × E interaction was nonsignificant (P-interaction = 0.10). Conclusions: These results suggest that ACEs conferred incremental osteoporosis risk independent of genetic predisposition. These findings support the inclusion of ACE screening in osteoporosis prevention strategies and highlight the need for targeted bone health interventions for youth exposed to ACEs. Full article

Background: Anemia and preeclampsia are common and independently associated with adverse neonatal outcomes. Their combined effect, however, remains insufficiently explored. This study aims to evaluate the impact of second-trimester maternal anemia on neonatal outcomes in pregnancies complicated by preeclampsia, and to assess its predictive value for preterm birth and NICU admission. Methods: We conducted a retrospective cohort study including 3517 singleton births from a Romanian tertiary maternity hospital between October 2023 and December 2024. A total of 295 preeclamptic pregnancies were stratified by anemia severity (none, mild, moderate-to-severe) and compared with 428 matched non-preeclamptic anemic pregnancies matched by closest-neighbor selection. Neonatal outcomes included birthweight, gestational age, anthropometric parameters, Apgar score, preterm birth, and NICU admission. Logistic regression and ROC curve analysis were performed using anemia severity as a predictor. Results: Moderate-to-severe anemia in preeclamptic pregnancies was associated with significantly lower birthweight (2618 ± 461 g), shorter gestational age (36.6 ± 2.0 weeks), and higher preterm birth (41.1%) and NICU admission rates (40.0%) were compared to non-anemic counterparts. Each increase in anemia severity conferred 84% higher odds of preterm delivery (OR = 1.84; AUC = 0.63) and a 49% increase in NICU admission (OR = 1.49; AUC = 0.58). Youden’s indices were 0.25 and 0.14, respectively. Conclusions: Maternal anemia is associated with increased neonatal morbidity in preeclamptic pregnancies, with moderate predictive value for preterm birth. These findings support the integration of early anemia screening and risk stratification into hypertensive pregnancy protocols to improve perinatal outcomes. Full article

Background: Food addiction (FA) is an emerging construct that mirrors the behavioral and neurobiological characteristics of substance use disorders. Despite growing interest, its association with specific psychiatric disorders among bariatric patients remains understudied. Objective: Our aim was to examine the prevalence and strength of associations between FA and seven major psychiatric disorders in individuals who underwent bariatric surgery. Methods: In a sample of 100 post-bariatric patients referred for psychiatric evaluation, FA was assessed using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0), and psychiatric disorders were diagnosed using the Mini International Neuropsychiatric Interview (MINI). Logistic regression models were used to estimate adjusted odds ratios (aORs) for the association between FA and each psychiatric disorder, controlling for sex, age, body mass index (BMI), employment status, the number of children, clinical comorbidities, physical activity, family psychiatric history, and region of residence. Results: FA was present in 51% of the sample. Descriptive analyses revealed a significantly higher prevalence of major depressive disorder, panic disorder, generalized anxiety disorder, social anxiety disorder, agoraphobia, obsessive–compulsive disorder, and bulimia nervosa among individuals with FA. Multivariate models showed robust associations between FA and bulimia nervosa (aOR = 19.42, p < 0.05), generalized anxiety disorder (aOR = 2.88, p < 0.05), obsessive–compulsive disorder (aOR = 6.64, p < 0.05), agoraphobia (aOR = 3.14, p < 0.05), social anxiety disorder (aOR = 4.28, p < 0.05) and major depressive disorder (aOR = 2.79, p < 0.05). Conclusions: FA is strongly associated with a range of psychiatric comorbidities in post-bariatric patients, reinforcing the need for comprehensive mental health screening in this population. These findings underscore the potential role of FA as a clinical marker for stratified risk assessment, supporting more personalized approaches to mental health monitoring and intervention following bariatric surgery. Full article

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings. Full article

Background/Objectives: Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns. Methods: A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters. Results: We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p < 0.001), smoking intensity (p < 0.001), geographic distribution (p < 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer. Conclusions: This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine. Full article

Lung-cancer incidence is projected to rise by 50% by 2035, underscoring the need for accurate yet accessible risk-stratification tools. We trained a Bayesian neural network on 300 annotated chest-CT scans from the public LIDC–IDRI cohort, integrating clinical metadata. Hamiltonian Monte-Carlo sampling (10 000 posterior draws) captured parameter uncertainty; performance was assessed with stratified five-fold cross-validation and on three independent multi-centre cohorts. On the locked internal test set, the model achieved 99.0% accuracy, AUC = 0.990 and macro-F1 = 0.987. External validation across 824 scans yielded a mean AUC of 0.933 and an expected calibration error $<0.034$, while eliminating false positives for benign nodules and providing voxel-level uncertainty maps. Uncertainty-aware Bayesian deep learning delivers state-of-the-art, well-calibrated lung-cancer risk predictions from a single CT scan, supporting personalised screening intervals and safe deployment in clinical workflows. Full article

Background: Prehabilitation programs improve postoperative outcomes in vulnerable patients undergoing major surgery. However, current screening tools such as the Malnutrition Universal Screening Tool (MUST) may lack the sensitivity needed to identify those who would benefit most. Muscle quality assessed by Computed Tomography (CT), specifically muscle radiodensity in Hounsfield Units (HUs), has emerged as a promising alternative for risk stratification. Objective: To evaluate the prognostic performance of CT-derived muscle radiodensity in predicting adverse postoperative outcomes in colorectal cancer patients, and to compare it with the performance of the MUST score. Methods: This single-center cross-sectional study included 201 patients with non-metastatic colon cancer undergoing elective laparoscopic resection. Patients were stratified based on enrollment in a multimodal prehabilitation program, either within an Enhanced Recovery After Surgery (ERAS) protocol or a non-ERAS pathway. Nutritional status was assessed using MUST, SARC-F questionnaire (strength, assistance with walking, rise from a chair, climb stairs, and falls), and the Global Leadership Initiative on Malnutrition (GLIM) criteria. CT scans at the L3 level were analyzed using automated segmentation to extract muscle area and radiodensity. Postoperative complications and hospital stay were compared across nutritional screening tools and CT-derived metrics. Results: MUST shows limited sensitivity (<27%) for predicting complications and prolonged hospitalization. In contrast, CT-derived muscle radiodensity demonstrates higher discriminative power (AUC 0.62–0.69), especially using a 37 HU threshold. In the non-ERAS group, patients with HU ≤ 37 had significantly more complications (33% vs. 15%, p = 0.036), longer surgeries, and more severe events (Clavien–Dindo ≥ 3). Conclusions: Opportunistic CT-based assessment of muscle radiodensity outperforms traditional screening tools in identifying patients at risk of poor postoperative outcomes, and may enhance patient selection for prehabilitation strategies like the ERAS program. Full article

Background/Objectives: Metabolic dysfunction-associated fatty liver disease (MAFLD) is linked to cardiovascular complications, including atrial fibrillation. P-wave indices (PWIs) reflect atrial conduction heterogeneity but have not been fully evaluated in MAFLD. To compare PWIs in MAFLD patients versus controls, assess their association with fibrosis severity, and evaluate their diagnostic performance for MAFLD and fibrosis. Methods: In this retrospective single-center study, 447 subjects were included (noMAFLD: Fatty Liver Index (FLI) < 30 without metabolic dysfunction, n = 205; MAFLD: FLI ≥ 60+ ≥ 1 metabolic risk factor, n = 242). Among MAFLD subjects, the non-alcoholic fatty liver disease (NAFLD) Fibrosis Score (NFS) stratified lower (NFS ≤ −1.455; n = 170), and there was a higher fibrosis risk (NFS > −1.455; n = 72). Standard 12-lead ECGs were digitized for offline PWI measurement. Statistical analyzes included group comparisons, multivariable logistic regression, and ROC curve analysis. Results: MAFLD patients exhibited a longer PWPT-D2 (63 ± 12 vs. 52 ± 10 ms, p = 0.003), PWPT-V1 (68 ± 14 vs. 60 ± 13 ms, p = 0.005), PWdis (55 ± 13 vs. 46 ± 11 ms, p = 0.010), and PTFV₁ (38 [31–46] vs. 28 [22–34] mm·ms, p = 0.021) compared with controls. Within MAFLD, a higher fibrosis risk was associated with a further PWI prolongation (all p < 0.015). Multivariate analysis identified PWPT-D2 (OR 1.05 per ms; 95% CI 1.02–1.08; p = 0.002) and PWDIS (OR 1.03 per ms; 95% CI 1.00–1.06; p = 0.048) as independent MAFLD predictors. ROC curves showed PWPT-D2 had the highest AUC for MAFLD detection (0.78; 95% CI 0.72–0.84) and fibrosis (0.82; 95% CI 0.76–0.88). Combining PWPT-D2 with BMI and waist circumference improved MAFLD discrimination (AUC 0.89; 95% CI 0.85–0.93; p < 0.001 vs. PWPT-D2 alone). Conclusions: PWPT-D2 and PWdis are significantly prolonged in MAFLD and more so with advanced fibrosis. PWPT-D2 may be a simple, noninvasive ECG marker for MAFLD screening and fibrosis staging, particularly when combined with anthropometric measures. Full article

Background: Gastric cancer (GC) remains a major global health challenge, with rising incidence among patients post-Helicobacter pylori (H. pylori) eradication, particularly those with persistent intestinal metaplasia (IM). Current risk stratification tools are limited in this high-risk population. Aim: To develop, validate, and externally test a machine learning-based prediction model—termed the Early Gastric Cancer Model (EGCM)—for identifying early gastric cancer (EGC) risk in H. pylori-eradicated patients with IM, and to implement it as a web-based clinical tool. Methods: This retrospective, dual-center study enrolled 214 H. pylori-eradicated patients with histologically confirmed IM from 900 Hospital and Fujian Provincial People’s Hospital. The dataset was split into a training cohort (70%) and an internal validation cohort (30%), with an external test cohort from the second center. A total of 21 machine learning algorithms were screened using cross-validation and hyperparameter optimization. Boruta and SHAP analyses were employed for feature selection, and the final EGCM was constructed using the top five predictors: atrophy range, xanthoma, map-like redness (MLR), MLR range, and age. Model performance was evaluated via ROC curves, precision–recall curves, calibration plots, and decision curve analysis (DCA), and compared against conventional inflammatory biomarkers such as NLR and PLR. Results: The CatBoost algorithm demonstrated the best overall performance, achieving an AUC of 0.743 (95% CI: 0.70–0.80) in internal validation and 0.905 in the external test set. The EGCM exhibited superior discrimination compared to individual inflammatory markers (p < 0.01). Calibration analysis confirmed strong agreement between predicted and observed outcomes. DCA showed the EGCM yielded greater net clinical benefit. A web calculator was developed to facilitate clinical application. Conclusions: The EGCM is a validated, interpretable, and practical tool for stratifying EGC risk in H. pylori-eradicated IM patients across multiple centers. Its integration into clinical practice could improve surveillance precision and early cancer detection. Full article

Background: Breast cancer poses a significant health challenge in Bangladesh, where limited screening and unique reproductive patterns contribute to delayed diagnoses and subtype-specific disparities. While reproductive risk factors such as age at menarche, parity, and contraceptive use are well studied in high-income countries, their associations with hormone-receptor-positive (HR+) and triple-negative breast cancer (TNBC) remain underexplored in low-resource settings. Methods: A case-control study was conducted at the National Institute of Cancer Research and Hospital (NICRH) including 486 histopathologically confirmed breast cancer cases (246 HR+, 240 TNBC) and 443 cancer-free controls. Socio-demographic and reproductive data were collected through structured interviews. Machine learning models—including Logistic Regression, Lasso, Support Vector Machines, Random Forest, and XGBoost—were trained using stratified five-fold cross-validation. Model performance was evaluated using sensitivity, F1-score, and Area Under Receiver Operating Curve (AUROC). To interpret model predictions and quantify the contribution of individual features, we employed Shapley Additive exPlanation (SHAP) values. Results: XGBoost achieved the highest overall performance (F1-score = 0.750), and SHAP-based interpretability revealed key predictors for each subtype. Rural residence, low education (≤5 years), and undernutrition were significant predictors across subtypes. Cesarean delivery and multiple abortions were more predictive of TNBC, while urban residence, employment, and higher education were more predictive of HR+. Age at menarche and age at first childbirth showed decreasing predictive importance with increasing age for HR+, while larger gaps between marriage and childbirth were more predictive of TNBC. Conclusions: Our findings underscore the value of machine learning coupled with SHAP-based explainability in identifying context-specific risk factors for breast cancer subtypes in resource-limited settings. This approach enhances transparency and supports the development of targeted public health interventions to reduce breast cancer disparities in Bangladesh. Full article

Objectives: Hyperuricaemia has been linked to cognitive decline, yet cerebral structural and haemodynamic changes in this population remain poorly defined. We evaluated transcranial colour-coded duplex (TCCD) sonography as a non-invasive screening tool for early mild cognitive impairment (MCI) in elderly hyperuricaemic men. Methods: In this cross-sectional study, 195 men aged ≥ 60 years with hyperuricaemia were stratified by the Montreal Cognitive Assessment (MoCA) into HUA + MCI (MoCA < 26, n = 46) and HUA (MoCA ≥ 26, n = 149) groups. TCCD measured third-ventricle width (TVW) and peak systolic/end-diastolic velocities to calculate resistive (RI) and pulsatility (PI) indices in the middle (MCA) and posterior (PCA) cerebral arteries. Serum uric acid was recorded. Kernel density plots and receiver operating characteristic (ROC) curves assessed diagnostic performance. Results: The HUA + MCI group exhibited higher serum uric acid (508.5 ± 36.3 vs. 492.9 ± 44.0 µmol/L; p = 0.031), greater TVW (0.55 ± 0.11 vs. 0.51 ± 0.08 cm; p = 0.037), and elevated left PCA RI (0.69 ± 0.07 vs. 0.64 ± 0.06) and PI (1.05 ± 0.17 vs. 0.95 ± 0.12; both p < 0.001). ROC analysis identified left PCA PI as the most specific marker (AUC = 0.701; specificity 90.6%; sensitivity 45.7%). Kernel density plots confirmed distinct distributions of key parameters. Conclusions: TCCD-detected ventricular enlargement and raised PCA pulsatility accurately distinguish MCI among hyperuricaemic men. As a non-invasive, accessible technique with high specificity, TCCD may complement MRI and cognitive testing in early screening of at-risk populations. Full article

Background/Objectives: Prostate cancer is the most common cancer among men globally and a leading cause of cancer-related death. Germline genetic evaluation is increasingly recognized as essential for men with high-risk features such as a strong family history or advanced disease. Methods: Comprehensive genetic risk assessment should integrate three components: family history (FH), rare pathogenic mutations (RPMs), and polygenic risk scores (PRS). RPMs in DNA repair genes (e.g., BRCA2, CHEK2, ATM) can inform screening, prognosis, and treatment strategies, particularly for metastatic or aggressive disease. PRS, derived from common genetic variants, provides a personalized and independent measure of prostate cancer risk and may guide decisions on screening intensity and timing. Results: Although PRS cannot yet differentiate between indolent and aggressive cancer, it has the potential to stratify men into low and high-risk categories more effectively than FH or RPMs alone. Knowledge of specific RPMs can influence treatment decisions in clinically advanced prostate cancer. Challenges in clinical implementation include limited provider awareness, underutilization of genetic counseling, and lack of diversity in genomic datasets, which can lead to misdiagnoses. Emerging technologies and digital tools are being developed to streamline genetic testing and counseling. Population-level strategies and tailored screening protocols based on genetic risk are under active investigation. Conclusions: While early evidence suggests high satisfaction with genetic testing among patients, further studies in diverse populations are needed. Integration of germline genetic information into prostate cancer management offers promising avenues for personalized screening, surveillance, and treatment, ultimately aiming to reduce morbidity and mortality. Full article

Objectives: Prostate cancer’s prevalence is rapidly increasing in Korea, with incidence rates rising by over 13% annually since 2017 according to the Korea Central Cancer Registry, highlighting the need for effective early detection strategies. This study systematically reviews the benefits and harms of PSA-based prostate cancer screening, focusing on its clinical effectiveness and public health implications. Methods: Following PRISMA 2020 guidelines, we searched five databases (PubMed, Embase, Cochrane Library, Google Scholar, and KMbase) for studies from 2014 to 2024. The eligible studies included RCTs, cohort studies, meta-analyses, and guidelines. Risk of bias was assessed using the Cochrane tool. We synthesized our findings narratively due to their methodological heterogeneity. Results: Sixteen studies were included. PSA screening reduced prostate-cancer-specific mortality by 20–31%, as reported in multiple randomized controlled trials, such as ERSPC and ProScreen, among men aged 55–69, but showed minimal impact on all-cause mortality. Advanced tools such as MRI and multi-biomarker models, which were implemented in several included studies, enhanced diagnostic accuracy. The potential harms included overdiagnosis, overtreatment, and psychological distress. Community-based education and shared decision-making, inferred from observational and implementation studies, improved participation and equity in screening. Conclusions: PSA-based screening offers modest mortality benefits but carries the risk of overdiagnosis. Precision diagnostics and risk-stratified strategies improve screening outcomes. Public health approaches, particularly those led by nurses and community health workers, are essential to promoting informed, equitable screening decisions. Full article

Background/Objectives: Randomised trials show that screening with prostate-specific antigen (PSA) and systematic prostate biopsies can reduce prostate cancer mortality but leads to high rates of overdiagnosis. Today, improved diagnostic methods more selectively detect potentially lethal, high-grade prostate cancer. Methods: This is a narrative review of modern diagnostic methods, ongoing trials, national policies and knowledge gaps related to screening and early detection of prostate cancer. Results: Screening intervals can be prolonged in men with PSA values below around 1 ng/mL as these men are at very low long-term risk of prostate cancer death. Overdiagnosis can be reduced by magnetic resonance imaging (MRI) and lesion-targeted prostate biopsies. Risk calculators and ancillary biomarkers can select men for further investigation and thereby reduce resource needs. These new methods are evaluated in large, randomised screening trials. The remaining knowledge gaps include optimal PSA cut-offs, screening intervals, start and stop ages, and the long-term balance between benefits and harm. Until recently, almost no national healthcare authority recommended population-based screening for prostate cancer. Now, the European Union Council recommends an evaluation of the feasibility of organised, risk-stratified screening. This has led to several pilot projects. In some other parts of the world, such as sub-Saharan Africa and the Caribbean, such initiatives are lacking despite high prostate cancer mortality rates. Conclusions: Risk-stratified prostate cancer screening including MRI and targeted biopsy reduces overdiagnosis. Results from ongoing research are needed to optimise screening protocols and to define long-term benefits and harms. Initiatives for early detection and screening are emerging across the world but are still lacking in many countries with high prostate cancer mortality. Full article