Search Results (146)

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality in Central and Eastern Europe (CEE), where late-stage diagnosis, structural healthcare limitations, and regional epidemiological confounders complicate early detection. This review aimed to synthesize the evidence from Romania, Poland, Hungary, and Bulgaria and to outline a context-adapted multimodal screening strategy for CEE settings. Methods: A structured review of PubMed-, Scopus-, and Web of Science-indexed literature published from 2010 through 27 December 2025 was performed, focusing on lung cancer epidemiology, screening, implementation barriers, risk stratification, and adjunctive diagnostic approaches in the four selected CEE countries. A total of 297 articles were included. Results: The evidence confirms a persistently high burden of late-stage lung cancer across CEE, driven by tobacco exposure, air pollution, radon, comorbidities, diagnostic delays, fragmented registries, workforce shortages, and marked socioeconomic and geographic inequalities. In addition, tuberculosis-related granulomatous lesions and chronic inflammatory lung disease complicate nodule interpretation and reduce screening specificity in parts of the region. Screening experience from Poland and Hungary supports the feasibility of low-dose computed tomography (LDCT) when paired with volumetric assessment and structured follow-up. Risk-prediction models may improve participant selection, while biological triage may help reduce unnecessary invasive procedures, although prospective validation remains limited. Conclusions: In CEE, lung cancer screening should be implemented as a multimodal, context-adapted program combining risk-based enrollment, volumetric LDCT, selective biological triage, smoking-cessation support, and centralized multidisciplinary delivery. Full article

Background: Low-dose computed tomography (LDCT) offers a unique opportunity to assess osteoporosis risk during routine lung cancer screening. This study aims to develop an integrated prediction model using trabecular bone features from LDCT and clinical factors to identify high-risk individuals early. Methods: This retrospective observational cohort study included 429 adults who underwent both DEXA and LDCT scans within one week at Kaohsiung Veterans General Hospital (2018–2022). Clinical data, including demographics, lifestyle factors, and comorbidities, were extracted from electronic records. Participants were categorized into osteoporotic (T-score ≤ −2.5) and non-osteoporotic groups. Trabecular bone morphometry was assessed at the T12 vertebra using QUIBIM Precision^® software, analyzing parameters such as BV/TV, Tb.Th, Tb.N, Tb.Sp, D2D, D3D, and QTS. ROI placement and measurements followed standardized protocols. Ethical approval was obtained, and informed consent was waived. Statistical analyses included t-tests, ROC curves, logistic regression, and Delong tests to compare clinical and trabecular predictors of osteoporosis using SPSS v22. Results: In this study of 429 individuals, osteoporosis was significantly associated with female gender, older age, lower BMI, and smaller waist circumference. Trabecular bone morphometry revealed that osteoporotic individuals had significantly thinner trabeculae (lower Tb.Th), higher trabecular number (Tb.N), and more complex trabecular architecture (higher D2D/D3D), with lower QTS. Logistic regression showed that Model 1—the model combining clinical factors and Tb.N—showed a slightly higher predictive performance (AUC 0.738) than Model 2 (AUC 0.711), although the improvement was modest (p = 0.022). A nomogram based on age, sex, BMI, waist circumference, and Tb.N effectively estimated osteoporosis probability, providing a clinically useful tool for risk stratification. Conclusions: In conclusion, combining trabecular bone morphometry (Tb.N) from routine LDCT with age, sex, BMI, and waist circumference enhances osteoporosis risk prediction, enabling personalized assessment without extra radiation during standard lung cancer screening. Full article

Background: The increasing use of computed tomography (CT) has led to a substantial rise in population exposure to ionizing radiation, highlighting the need for accurate and individualized dose assessment methods. This study aimed to evaluate a novel dosimetric parameter—the size-specific dose–length product (DLPss)—in low-dose chest CT (LDCT) protocols and to compare its performance with conventional dose metrics. Methods: A retrospective single-center analysis was conducted in a cohort of 221 patients undergoing LDCT of the chest. Anthropometric parameters were used to calculate the size-specific conversion factor (k), enabling determination of SSDE and DLPss. Dose parameters (CTDIvol, DLP, SSDE, and DLPss) were analyzed and compared with data from a standard chest CT cohort (n = 134) from the first study in the series. The contribution of the topogram to total radiation dose was also assessed. All examinations were considered diagnostically adequate in routine clinical evaluations. Results: The mean CTDIvol in the LDCT group was 1.33 mGy, with a DLPss of 61.93 mGy·cm and an estimated effective dose below 0.7 mSv, representing a dose reduction exceeding 82% compared to standard CT. DLPss values were approximately 23% higher than conventional DLP, indicating underestimation of dose by standard metrics. The topogram accounted for 10.23% of total radiation dose in LDCT, significantly higher than in standard CT (1.84%). Significant sex-related differences were observed in CTDIvol, DLP, and DLPss, but not in SSDE. Conclusions: DLPss provides a more comprehensive and individualized assessment of radiation exposure than conventional dose metrics by integrating patient size and scan length. The substantial contribution of the topogram to total dose in LDCT highlights the need for its optimization, particularly in long-term screening programs. From a clinical perspective, implementation of DLPss may improve patient-specific risk stratification and support more precise monitoring of cumulative radiation exposure, especially in populations undergoing repeated imaging, such as lung cancer screening cohorts. Advanced reconstruction algorithms, including deep learning-based methods, may enable further dose reductions and warrant future clinical investigation. Full article

Lung cancer remains the leading cause of cancer mortality worldwide, with most cases diagnosed at advanced stages. Conventional tissue biopsy is invasive, and low-dose CT (LDCT) screening—although effective—faces practical and logistical limitations. Liquid biopsy has emerged as a minimally invasive approach to capture tumor-derived material, including circulating tumor DNA (ctDNA), cells, and extracellular vesicles (EVs). Among EVs, exosomes and their microRNA (miRNA) cargo offer a stable, disease-specific signal. Airway-proximal fluids such as bronchial aspirate and bronchoalveolar lavage fluid (BALF) are in direct contact with the tumor microenvironment and may contain higher concentrations of tumor-derived exosomal miRNAs compared with blood. This review synthesizes the limited but promising evidence for exosomal miRNAs in bronchial aspirate and BALF as diagnostic and prognostic biomarkers in lung cancer, examines methodological and standardization challenges, and discusses potential integration into clinical workflows, with particular emphasis on Romania’s lung cancer epidemiology and healthcare context. While only two primary studies in the last five years have explored BALF exosomal miRNAs, these data justify further multicenter investigations aligned with MISEV2023 guidelines. Integrating airway-proximal exosomal miRNA analysis into bronchoscopy procedures could enhance diagnostic precision in resource-limited health systems and support the transition towards personalized thoracic oncology. Full article

Background: Past workplace exposure to asbestos in combination with tobacco smoking has increased the risk of lung cancer for some residents in an area within the Friuli Venezia Giulia region, Northeast Italy. In light of studies showing that lung cancer screening (LCS) with low-dose computed tomography (LDCT) can reduce mortality, local stakeholders and decision-makers decided to assess the potential benefits, harms and cost-effectiveness of a single round of LCS with LDCT versus standard care among people aged 55 to 80 who were formerly exposed to asbestos and with at least 10 pack-years of smoking. Methods: An economic model was developed using a decision tree connected to a Markov cohort model. The primary outcome was the incremental cost per additional quality-adjusted life year (QALY). Other outcomes included the number of life years saved, the number of deaths averted and overdiagnosis. Results: Per 10,000 people screened, the intervention led to 395 additional QALYs (95% credible interval: 129 to 831) and incremental total costs of EUR 1,086,345 (95% credible interval: −852,607 to 2,155,826). The incremental cost per QALY gained was EUR 2750. There was a probability of cost-effectiveness of 99.5% relative to a threshold of EUR 25,000. Conclusions: The model estimated that the intervention was cost-effective. The model’s simplifications and limitations should be considered when interpreting the findings in relation to policy-making decisions. Further research could include the costs and benefits of incidental findings and could assess the cost-effectiveness of repeated rounds of screening for the same population. Full article

Objectives: Our objectives were to develop and validate the habitat model based on low-dose computed tomography (LDCT) for noninvasive prediction of the visceral pleural invasion (VPI) in subpleural nodules with solid component. Methods: A total of 313 patients with subpleural lung adenocarcinoma nodules from three centers were retrospectively enrolled and divided into training (n = 192), validation (n = 82), and external test (n = 39) sets. All patients underwent preoperative LDCT scan. The habitat model was constructed using unsupervised clustering to partition each tumor into three distinct habitats, from which radiomic features were extracted and selected. Its diagnostic performance was compared with a whole-lesion radiomic model and radiological model. Statistical analysis included receiver operating characteristic (ROC) analysis and DeLong test. Results: The habitat model significantly outperformed both the radiomic and radiological models across the validation and external test sets, with areas under the ROC curve of 0.893 and 0.908, respectively (all p < 0.05). In contrast, the radiomic model achieved 0.833 and 0.772, while the radiological model yielded 0.746 and 0.624. The corresponding software tool has been made publicly available to facilitate broader clinical application. Conclusions: The habitat imaging model based on LDCT effectively predicts the VPI in subpleural lung adenocarcinoma by quantifying intratumoral spatial heterogeneity and demonstrates promising diagnostic performance compared to conventional radiomic and radiological methods. This approach offers a noninvasive preoperative tool to assist in risk stratification and guide personalized therapeutic decision-making for subpleural nodules detected during lung cancer screening. Full article

Background: Low-dose computed tomography (LDCT) lung cancer screening (LCS) frequently identifies emphysema in high-risk smokers. However, the extent to which CT-detected emphysema reflects underlying physiological impairment remains uncertain. We evaluated whether spirometry can detect functional abnormalities in this population beyond structural imaging findings. Methods: This cross-sectional study included 323 individuals with LDCT- detected emphysema and no lung cancer or prior chronic respiratory diseases within a screening cohort (n = 3076). Participants underwent pre-bronchodilator spirometry and symptom assessments (COPD Assessment test (CAT) and Modified Medical Research Council (mMRC) Dyspnea Scale). Pre-bronchodilator airflow limitation was defined as forced expiratory volume in one second to forced vital capacity ratio (FEV₁/FVC) < 0.70. Small airways dysfunction was defined by ≥2 reduced mid-expiratory flow parameters (<60% predicted). Flow–volume curve morphology was assessed qualitatively. Results: Pre-bronchodilator airflow limitation was observed in 45.2% of participants, predominantly mild. Small-airway dysfunction was present in 52%, and an abnormal flow–volume curve morphology in 67.5%. Notably, functional abnormalities were frequently observed despite preserved FEV₁. Symptom burden was low, with only 7.7% of participants reporting clinically significant symptoms. Functional impairments often overlapped and were common in minimally symptomatic individuals. Conclusions: In a lung cancer screening (LCS) cohort with CT-detected emphysema, functional abnormalities are frequently observed, including in individuals with preserved FEV₁ and minimal symptoms. Spirometry provides additional physiological insight beyond structural imaging; however, these findings are descriptive and should not be interpreted as diagnostic of COPD. Further studies are needed to determine their clinical relevance. Full article

Background/Objectives: Despite demonstrated mortality benefits, annual low-dose computed tomography (LDCT) screening faces challenges in real-world adoption due to low uptake and poor longitudinal adherence. This review evaluates patient- and provider-level factors that influence screening participation and highlights strategies to strengthen equitable engagement throughout the screening pathway. Methods: A structured literature search of PubMed and Web of Science was performed to identify studies published between 2013 and November 2025 (search conducted on 25 November 2025). Eligible publications included qualitative and quantitative studies, study protocols, and reviews examining LDCT screening uptake, adherence, and follow-up practices. Extracted evidence was synthesized, with particular attention being paid to patient- and provider-level determinants. Results: The evidence demonstrates that both patient- and provider-level factors substantially influence screening participation and continuity. At the patient level, limited awareness of screening, misconceptions regarding asymptomatic disease, and psychosocial factors such as fear, fatalism, stigma, and medical mistrust were consistently associated with reduced uptake and adherence. At the provider level, gaps in guideline familiarity, time constraints, and challenges in delivering high-quality shared decision-making limited referrals and follow-up. Conclusions: Improving real-world effectiveness of LDCT lung cancer screening requires reframing screening as a longitudinal program of care. Strategies that support patient navigation, enhance provider capacity for sustained engagement, and integrate tobacco dependence treatment into screening pathways are central to improving adherence and reducing disparities. Full article

Background/Objectives: In Puerto Rico (PR), lung cancer mortality remains high because diagnoses frequently occur at advanced stages. Although low-dose computed tomography (LDCT) lowers lung cancer–specific mortality, this screening is difficult to operationalize locally due to high false-positive rates, radiology capacity constraints, payer limitations, and geographic barriers affecting rural populations. Methods: We performed a narrative review on the literature from 2001–2026 of established and emerging detection strategies—LDCT; serum biomarkers (CEA, CYFRA-21-1, NSE, ProGRP, SCC-Ag, HE4, Hp, TAAb); breath analysis (FeNO and VOCs); and liquid biopsy (ctDNAs/CTCs/miRNAs). We assessed technical performance, feasibility, and health-system fit in PR and then synthesized these findings into an implementable biomarker-first triage workflow for are. Results: Multiplex serum panels analyzed with machine learning outperform single markers and TAAb provide high specificity with biological lead time, supporting their use as a triage gateway before LDCT. Breathomics is also feasible at the point of care. Liquid biopsy has modest sensitivity in very-early disease yet provides molecular adjudication for indeterminate nodules. A stepwise pathway—expanded risk assessment, integrated multi-panel testing in primary care, LDCT reserved for biomarker-positive individuals, and liquid biopsy when imaging is inconclusive—can enrich pre-test probability, reduce unnecessary scans, align with capitation, and protect limited radiology capacity. Conclusions: An integrated, non-invasive, biomarker-first triage model offers a pragmatic, equitable route to earlier lung cancer detection in PR and resource stewardship, while reducing disparities. Full article

Low-dose computed tomography (LDCT) can effectively reduce ionizing radiation; however, the associated image noise and artifacts can severely compromise the accuracy of clinical diagnosis. To address the challenge of balancing noise suppression and detail preservation in LDCT images, this study proposes a deep learning (DL)-based image denoising method termed Progressive Fusion Distillation Network (PFDN). Building upon the Information Multi-distillation Network (IMDN), the proposed method incorporates a pixel attention (PA) mechanism and a progressive fusion strategy, and further designs a Pixel Parallel Extraction Block (PPEB) together with a Progressive Fusion Distillation Block (PFDB) to fully exploit multi-scale and multi-channel features, thereby optimizing the image denoising network through efficient feature separation and re-fusion. In addition, by explicitly leveraging the noise characteristics specific to LDCT images, the method establishes an end-to-end training framework suitable for medical imaging. Experimental results demonstrate that PFDN not only effectively reduces image noise and artifacts, but also enhances overall image quality while preserving diagnostically relevant image structures under the adopted evaluation setting. Full article

Objective: The integration of artificial intelligence (AI) into computer-aided detection (CAD) is a major innovation in lung cancer diagnosis. However, its reliability in detecting the earliest radiographic sign—faint ground-glass opacities (GGOs) indicating pre-invasive adenocarcinoma—remains a critical, unquantified gap. This study aimed to perform a rigorous failure analysis to define the specific conditions under which commercial AI/CAD systems fail in a low-dose CT (LDCT) screening setting. Methods: In this retrospective diagnostic accuracy study, a primary cohort of 100 patients and an external validation cohort of 50 patients with moderate/low-risk nodules on LDCT were included. An expert reference standard was established by a consensus panel of three thoracic radiologists. Two independent, commercially deployed AI/CAD systems from different vendors (Vendor A & Vendor B) processed all cases. Nodules confirmed by experts but missed by AI were analyzed. Their morphology was categorized, and their mean CT attenuation (HU) was measured via manual region-of-interest placement. Results: The AI systems demonstrated significant and comparable false negative rates in the combined cohort: 12.7% for Vendor A and 14.7% for Vendor B. The vast majority of missed nodules were GGOs (92.3% and 78.6%, respectively, in the primary cohort). Crucially, quantitative analysis revealed a consistent density threshold for AI failure: the mean CT value of missed GGOs was −737 ± 51.50 HU for Vendor A and −727 ± 70.07 HU for Vendor B. This algorithmic blind spot was fully corroborated by the external validation cohort (−741 ± 48.2 HU and −733 ± 62.5 HU, respectively). Anatomical complexity (juxta-pleural/endobronchial location) was a secondary failure factor. Conclusions: This study identifies a quantifiable “−730 HU blind spot” as a common limitation of current commercial AI/CAD systems in diagnosing early lung adenocarcinoma. This finding represents a pivotal advancement in understanding AI’s role in diagnostics: it is not infallible. To innovate and safeguard screening efficacy, radiologists must adopt a human–AI collaborative model with mandated manual verification targeting low-attenuation opacities, ensuring this diagnostic innovation fulfills its promise while mitigating the risks of overdiagnosis. Full article

Background/Objectives: Assessing structural damage in pediatric sacroiliitis is challenging, necessitating radiation-free alternatives to computed tomography (CT). This study evaluated the diagnostic performance of advanced MRI sequences—3D-MENSA (Multi-Echo in Steady-State Acquisition), 3D-MERGE (Multiple-Echo Recombined Gradient Echo), and Zero Echo Time (ZTE)—against conventional T1-weighted sequences for detecting structural lesions. Low-dose computed tomography (LDCT) served as the reference standard. A secondary objective was to qualitatively assess the visibility of active inflammatory lesions and fat metaplasia. Methods: In this cross-sectional study, 23 pediatric patients with enthesitis-related arthritis (ERA) were included. To adhere strictly to radiation safety principles, the study used pre-existing ldCT datasets from a clinical cohort as the reference standard. No new CT scans were performed for this study. Structural lesions (erosions, sclerosis, and joint-space changes) were independently scored by two blinded radiologists. Interobserver agreement was assessed using intraclass correlation coefficients (ICC). Results: Advanced sequences (ZTE, 3D-MENSA, 3D-MERGE) demonstrated high agreement with ldCT for erosion detection (ICC range: 0.924–0.998) and significantly outperformed conventional T1-weighted MRI (ICC: 0.707). 3D-MENSA provided distinct contrast, effectively differentiating the ligamentous component of the sacroiliac joint from both the synovial component and the adjacent bone cortex. Qualitatively, 3D-MENSA also identified bone marrow edema and fat metaplasia, which cannot be visualized by ZTE or ldCT. Conclusions: 3D-MENSA and 3D-MERGE enable comprehensive evaluation of structural sacroiliitis lesions in pediatric patients with diagnostic accuracy comparable to ldCT. Specifically, 3D-MENSA demonstrates the potential to detect both active and chronic lesions in a single, rapid, radiation-free acquisition. These findings suggest that it should be considered for routine pediatric imaging protocols. Full article

Lung cancer remains the leading cause of cancer-related mortality globally and is often diagnosed at advanced stages in Saudi Arabia. This cross-sectional study aimed to quantify public awareness and knowledge of lung cancer screening (LCS) using LDCT and identify barriers to its implementation in Riyadh. A validated 24-item questionnaire was administered to 452 participants to assess demographic factors, smoking history, and LCS knowledge. Results revealed that only 30.1% of participants had heard of LCS, and 50.2% demonstrated “poor” knowledge scores (mean score 11.0 ± 4.97). Higher knowledge scores were significantly associated with being female, having a bachelor’s degree or higher, and being a non-smoker. While 78.1% expressed willingness to undergo screening, the most significant barrier was a lack of knowledge about the test (44.1%), followed by concerns regarding radiation exposure (36.1%). Conversely, a healthcare provider’s recommendation was identified as the primary motivator for 53.3% of respondents. These findings highlight a critical “awareness–willingness” gap. While public willingness is high, this should not be misconstrued as systemic preparedness; substantial educational and structural gaps remain that must be bridged before national implementation can be considered feasible. We conclude that while public willingness is high, successful implementation requires a transition toward organized invitation systems and the use of multifactorial risk profiles. Integrating epidemiological evidence with proactive policy design is essential to ensure that the national program avoids systematic under- or over-inclusion and remains effective for all demographics. Full article

Background and Objectives: Emphysema and coronary artery calcium (CAC) share common lifestyle-related risk factors, yet their association in Chinese populations remains understudied. This study investigated how lifestyle factors influence the association between emphysema and CAC score in an urban Chinese general population. Methods: The study included 1000 participants from the Chinese Nelcin-B3 urban general population study originating in 2017 who underwent low-dose CT (LDCT) screening and comprehensive CT assessment. Emphysema was visually assessed by subtype and severity. CAC was measured using the Agatston method and categorized as 0, 1–100, and >100. Questionnaire-based lifestyle factors (smoking, BMI, diet, physical activity, alcohol consumption and environmental exposures) were categorized based on number of unfavorable behaviors. Multivariable multinomial logistic regression adjusted for age, sex, education and cardiovascular risk factors examined the associations between emphysema and CAC, with interactions and stratified analyses for lifestyle effects. Results: Emphysema was present in 62.3% of the participants, with centrilobular being the most common subtype (61.5%). Paraseptal emphysema was associated with both CAC 1–100 (OR: 2.07 [1.03–4.15]) and CAC > 100 (OR: 2.94 [1.26–6.84]). Severe emphysema was linked to CAC > 100 (OR: 3.50 [1.38–8.84]). These associations were stronger in the intermediate unhealthy lifestyle group for paraseptal (OR: 5.41 [1.70–17.22] and moderate and severe emphysema (OR: 9.64 [1.64–56.55]; OR: 3.73 [1.07–13.06]), respectively, but not significantly different. Conclusions: While paraseptal and severe emphysema are associated with higher CAC scores, there is no modifying effect of lifestyle factors. These findings suggest that cardiovascular risk assessment could be of importance in individuals with emphysema. Further longitudinal studies are needed to clarify the clinical implications. Full article

Purpose: The present study aimed to assess the radiation dose associated with low-dose (LD) CT pelvimetry compared with conventional radiography and to evaluate the adequacy of the resulting image quality. Methods: The absorbed dose was measured using thermoluminescent dosimeters positioned in an anthropomorphic female phantom, including uterine locations, to estimate the fetal dose. Conventional radiographic pelvimetry and LD-CT pelvimetry were performed using clinically implemented protocols. Effective dose was calculated using Monte Carlo–based modeling applying acquisition parameters and retrospective patient dose registry data. Image quality of LD-CT pelvimetry was independently evaluated in 14 consecutive clinical cases using a four-point ordinal scale. Results: LD-CT pelvimetry reduced the mean absorbed pelvic dose by approximately 50% compared with conventional pelvimetry (0.18 vs. 0.39 mGy) and decreased estimated fetal dose by 40% (0.21 vs. 0.37 mGy). These estimates were based on standardized single acquisitions and did not incorporate additional radiation from retakes commonly observed in conventional practice. CT demonstrated substantially more homogeneous dose distribution, whereas conventional pelvimetry exhibited marked heterogeneity with peak values up to 2.3 mGy. The maternal effective dose was lower for LD-CT (0.16 mSv) than for conventional pelvimetry (0.36 mSv); inclusion of retakes increased the conventional effective dose to 0.71 mSv. All CT examinations were diagnostically adequate, and no recalls were required. Conclusions: Optimized low-dose CT pelvimetry significantly reduces radiation dose compared with conventional radiographic pelvimetry while maintaining reliable diagnostic image quality. These results support the clinical adoption of CT-based pelvimetry as a dose-efficient and reproducible alternative to conventional techniques. Full article