Search Results (85)

Background/Objectives: To assess quantitatively the correlation between the lung ultrasound severity scores (LUSSs) and chest CT severity scores (CTSSs) derived from low-dose computed tomography (LDCT) for evaluating pulmonary inflammation in COVID-19 patients. Methods: In this prospective observational study, from an initial cohort of 1000 patients, 555 adults (≥18 years) with confirmed COVID-19 were enrolled based on inclusion criteria. All underwent LDCT imaging, scored by the CTSS (0–25 points), quantifying involvement across five lung lobes. Lung ultrasound examinations using standardized semi-quantitative scales for the B-line (LUSS B) and consolidation (LUSS C) were performed in a subgroup of 170 patients; 110 had follow-up imaging after one week. Correlation analyses included Spearman’s and Pearson’s coefficients. Results: Significant positive correlations were found between the CTSS and both the LUSS B (r = 0.32; p < 0.001) and LUSS C (r = 0.24; p = 0.006), with the LUSS B showing a slightly stronger relationship. Each incremental increase in the LUSS B corresponded to an average increase of 0.18 CTSS points, whereas a one-point increase in the LUSS C corresponded to a 0.27-point CTSS increase. The mean influence of the LUSS on CTSS was 8.0%. Neither ultrasound score significantly predicted ICU admission or mortality (p > 0.05). Conclusion: Standardized lung ultrasound severity scores show a significant correlation with low-dose CT in assessing pulmonary involvement in COVID-19, particularly for the B-line artifacts. Lung ultrasound represents a valuable bedside tool, complementing—but not substituting—CT in predicting clinical severity. Integrating both imaging modalities may enable the acquisition of complementary bedside information and facilitate dynamic monitoring of disease progression. Full article

Background/Objectives: Lung cancer screening with low-dose computed tomography (LDCT) has reduced lung cancer mortality in high-risk smokers. However, the evidence on LDCT screening in the elderly is limited, with there being few older participants in major trials and ongoing debate about the benefits, risks, and appropriate age limits of LDCT. This study aimed to investigate the prevalence of pulmonary nodules and lung cancer detection rates in men aged 70 and above who underwent a single round of LDCT screening. Methods: We retrospectively analyzed data from elderly male participants aged 70 years or older who underwent a single low-dose CT lung cancer screening at the Veterans Health Service Medical Center between 2010 and 2023. The participants included those who requested screening or were asymptomatic but recommended by physicians. Individuals with prior lung cancer, symptoms suggestive of lung cancer, or suspicious findings on previous imaging were excluded. The nodule prevalence, lung cancer diagnoses, pathological subtypes, and clinical stages were reviewed. Results: A total of 1409 individuals with a mean age of 74.2 years were included. The median follow-up duration was 3.6 years. Among the included individuals, 1304 (92.6%) had a history of smoking. Positive nodules were detected in 179 patients (12.7%, 95% CI: 11.0–14.5%), and lung cancer was diagnosed in 31 patients (2.2%, 95% CI: 1.5–3.1%). Of the diagnosed cases, 14 (45.2%) were adenocarcinomas and 12 (38.7%) were squamous cell carcinomas. Nineteen patients (51.3%) were diagnosed with stage I or II cancer, while seven (22.6%) were diagnosed at stage IV. Conclusions: A single round of LDCT screening in elderly men resulted in a relatively high lung cancer detection rate, with over half of the diagnosed cases being identified at an early stage. This highlights the potential clinical benefit of even one-time screening in enabling timely treatment, which may still be feasible in older adults. However, potential harms such as overdiagnosis should also be considered. Full article

Lung cancer remains the leading cause of cancer-related mortality globally, largely due to late-stage diagnoses. While low-dose computed tomography (LDCT) has improved early detection and reduced mortality in high-risk populations, traditional screening strategies often adopt a one-size-fits-all approach based primarily on age and smoking history. This can lead to limitations, such as overdiagnosis, false positives, and the underrepresentation of non-smokers, which are especially prevalent in Asian populations. Precision medicine offers a transformative solution by tailoring screening protocols to individual risk profiles through the integration of clinical, genetic, environmental, and radiological data. Emerging tools, such as risk prediction models, radiomics, artificial intelligence (AI), and liquid biopsies, enhance the accuracy of screening, allowing for the identification of high-risk individuals who may not meet conventional criteria. Polygenic risk scores (PRSs) and molecular biomarkers further refine stratification, enabling more personalized and effective screening intervals. Incorporating these innovations into clinical workflows, alongside shared decision-making (SDM) and robust data infrastructure, represents a paradigm shift in lung cancer prevention. However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. This review explores the current landscape and future directions of precision medicine in lung cancer screening, emphasizing the need for interdisciplinary collaboration and population-specific strategies to realize its full potential in reducing the global burden of lung cancer. Full article

Background/Objectives: In 2024, Lithuania developed a national lung cancer screening program (the Program), targeting individuals aged 50 to 70 years, regardless of their smoking history, with screenings conducted once every three years. The Program aims not only to actively detect lung nodules (lung cancer) but also to identify clinically significant concomitant findings. The pilot study aimed to evaluate the screening process’s feasibility and organizational efficiency of the screening process, as well as its potential clinical effectiveness. Methods: Three family medicine centers were selected for participation. The Coordinating Center contacted individuals aged 50 to 70 sequentially and invited them to participate, regardless of smoking status. In total, 1014 individuals were prospectively enrolled and underwent low-dose chest computed tomography (LDCT) screening between 26 September 2024 and 14 February 2025. Results: Of the individuals invited, 76.1% agreed to participate. Lung-RADS v2022 category 4 nodules were identified in 1.4% of participants (n = 14), including six smokers and eight non-smokers. Additionally, one participant with a Lung-RADS category 2 nodule was diagnosed with squamous cell carcinoma originating from peripheral lung changes. Newly identified significant incidental findings were detected in 25.9% of participants: 5.1% had pulmonary or mediastinal findings (most commonly emphysema, interstitial lung changes, and bronchiectasis), 18.7% had cardiovascular findings (usually coronary artery calcification, aortic valve calcification, and aorta dilation), and 2.1% had other clinically relevant conditions (e.g., thyroid nodules, diaphragmatic changes). Following assessment by family physicians, 17.6% of all participants were referred to medical specialists, including pulmonologists, cardiologists, and others. Conclusions: This pilot study demonstrated that the Lithuanian lung cancer screening model is feasible, well-organized, and clinically valuable. The findings support the Program’s readiness for broader implementation at the national level. Full article

Background: Chronic obstructive pulmonary disease (COPD) and lung cancer are the leading causes of death globally, which share common risk factors such as age and smoking exposure. In high-income countries, low-dose computed tomography (LDCT) lung cancer screening programs have decreased lung cancer mortality and facilitated the detection of emphysema, a key radiological indicator of COPD. This study aimed to assess the prevalence of emphysema during a pilot LDCT screening program for lung cancer in a middle-income country with a high smoking prevalence. Methods: A secondary analysis of the Lung Cancer Screening Database of the Autonomous Province of Vojvodina, Serbia, from 20 September 2020 to 30 May 2022. Persons aged 50–74 years, with a smoking history of ≥30 pack-years/or ≥20 pack-years with additional risks (chronic lung disease, prior pneumonia, malignancy other than lung cancer, family history of lung cancer, and professional exposure to carcinogens) were offered LDCT. Results: Of 1288 participants, mean age of 62.1 ± 6.7 years and 535 males (41.5%), 386 (30.0%) had emphysema. The majority of patients with emphysema (301/386, 78.0%) had no prior history of chronic lung diseases. Compared to the patients without emphysema, the patients with emphysema reported more shortness of breath (140/386, 36.3% vs. 276/902, 30.6%, p = 0.046), chronic cough (117/386, 30.3% vs. 209/902, 23.17% p = 0.007), purulent sputum expectoration (70/386, 18.1% vs. 95/902, 10.53%, p < 0.001), and weight loss (45/386, 11.7% vs. 63/902, 7.0%, p = 0.005). The patients with emphysema had more exposure to smoking (pack/years, 43.8 ± 18.8 vs. 39.3 ± 18.1, p < 0.001) and higher prevalence of solid or semisolid lung nodules (141/386, 36.5% vs. 278/902 30.8%, p = 0.04). Conclusions: Almost one-third of the patients who underwent the LDCT screening program in a middle-income country had emphysema that was commonly undiagnosed despite being associated with a significant symptom burden. Spirometry screening should be considered in high-risk populations. Full article

Background: Lung cancer is the leading cause of cancer-related mortality globally, with late-stage diagnoses contributing to poor survival rates. While lung cancer screening with low-dose computed tomography (LDCT) has proven effective in reducing mortality among heavy smokers, its limitations, including high false-positive rates and resource intensiveness, restrict widespread use. Liquid biopsy, particularly using microRNA (miRNA) biomarkers, offers a promising adjunct to current screening strategies. This study aimed to evaluate the predictive power of a panel of serum miRNA biomarkers for lung cancer detection. Patients and Methods: A case-control study was conducted at two tertiary hospitals, enrolling 82 lung cancer cases and 123 controls. We performed an extensive literature review to shortlist 25 candidate miRNAs, of which 16 showed a significant two-fold increase in expression compared to the controls. Machine learning techniques, including Random Forest, K-Nearest Neighbors, Neural Networks, and Support Vector Machines, were employed to identify the top six miRNAs. We then evaluated predictive models, incorporating these biomarkers with lung nodule characteristics on LDCT. Results: A prediction model utilising six miRNA biomarkers (mir-196a, mir-1268, mir-130b, mir-1290, mir-106b and mir-1246) alone achieved area under the curve (AUC) values ranging from 0.78 to 0.86, with sensitivities of 70–78% and specificities of 73–85%. Incorporating lung nodule size significantly improved model performance, yielding AUC values between 0.96 and 0.99, with sensitivities of 92–98% and specificities of 93–98%. Conclusions: A prediction model combining serum miRNA biomarkers and nodule size showed high predictive power for lung cancer. Integration of the prediction model into current lung cancer screening protocols may improve patient outcomes. Full article

Lung cancer is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths Each year, more people die of lung cancer than colon, breast, and prostate cancer combined. The early detection of lung cancer is critical for improving patient outcomes, and automation through advanced image analysis techniques can significantly assist radiologists. This paper presents the development and evaluation of a computer-aided diagnostic system for lung cancer screening, focusing on pulmonary nodule segmentation in low-dose CT images, by employing HighRes3DNet. HighRes3DNet is a specialized 3D convolutional neural network (CNN) architecture based on ResNet principles which uses residual connections to efficiently learn complex spatial features from 3D volumetric data. To address the challenges of processing large CT volumes, an efficient patch-based extraction pipeline was developed. This method dynamically extracts 3D patches during training with a probabilistic approach, prioritizing patches likely to contain nodules while maintaining diversity. Data augmentation techniques, including random flips, affine transformations, elastic deformations, and swaps, were applied in the 3D space to enhance the robustness of the training process and mitigate overfitting. Using a public low-dose CT dataset, this approach achieved a Dice coefficient of 82.65% on the testing set for 3D nodule segmentation, demonstrating precise and reliable predictions. The findings highlight the potential of this system to enhance efficiency and accuracy in lung cancer screening, providing a valuable tool to support radiologists in clinical decision-making. Full article

X-ray computed tomography (CT) is vital for medical diagnostics, but frequent radiation exposure raises concerns, driving the adoption of low-dose CT (LDCT) to mitigate risks. However, LDCT often introduces noise, compromising diagnostic accuracy. This paper proposes a pure vision transformer (PViT) for LDCT denoising, enhanced with a gradient–Laplacian attention module (GLAM) to improve edge preservation and fine structural detail reconstruction. The model’s robustness was validated across five diverse datasets (piglet, head, abdomen, chest, thoracic), demonstrating consistent performance in preserving anatomical structures. Extensive ablation studies on attention configurations and loss functions further substantiated the contributions of each module. Quantitative evaluation using PSNR and SSIM, alongside radiologist assessment, confirmed significant noise suppression and sharper anatomical boundaries, particularly in regions with fine details such as organ interfaces and bone structures. Additionally, in benchmark comparisons against state-of-the-art LDCT models (RED-CNN, TED-Net, DSC-GAN, DRL-EMP) and traditional methods (BM3D), the model exhibited lower parameter and stable training performance. These findings highlight the model’s robustness, efficiency, and clinical applicability, making it a promising solution for improving LDCT image quality while maintaining computational efficiency. Full article

Background/Objectives: Lung cancer is the primary cause of cancer-related deaths. Most patients are typically diagnosed at advanced stages. Low-dose computed tomography (LDCT) has been proven to reduce lung cancer mortality, but screening programs using LDCT are associated with a high number of false positives and unnecessary thoracotomies. It is therefore imperative that a certain diagnosis is refined, especially in cases of solitary pulmonary nodules that are difficult to technically access for an accurate preoperative diagnosis. Extracellular vesicles (EVs) involved in intercellular communication may be an innovative biomarker for diagnosis and therapeutic strategies in lung cancer, regarding their ability to carry tumor-specific cargo. The aim of the LUCEx study is to determine if extracellular vesicle cargoes from both lung tissue and blood could provide complementary information to screen lung cancer patients and enable personalized follow-up after the surgery. Methods: The LUCEx study is a prospective study aiming to recruit 600 patients with lung cancer and 50 control subjects (false positives) undergoing surgery after diagnostic imaging for suspected pulmonary nodules using computed tomography (CT) scans. These patients will undergo curative surgery at the Department of Thoracic Surgery of the Miguel Servet Hospital in Zaragoza, Spain, and will be followed-up for at least 5 years. At baseline, samples from both tumor distal lung tissue and preoperative peripheral blood will be collected and processed to compare the quantity and content of EVs, particularly their micro-RNA (miRNA) cargo. At the third and fifth years of follow-up, CT scans, functional respiratory tests, and blood extractions will be performed. Discussion: Extracellular vesicles and their miRNA have emerged as promising tools for the diagnosis and prognosis of several diseases, including cancer. The LUCEx study, based on an observational clinical cohort, aims to understand the role of these vesicles and their translational potential as complementary tools for imaging diagnosis and prognosis. Full article

This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland–Altman analysis. The phantom’s lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to −196 HU (p < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, p < 0.001, Jamovi, version 2.3.28). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (κ = −0.398, 95% CI [−0.644 to −0.152], p < 0.002, SPSS Statistics, version 30). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials. Full article

Background: Low-dose computed tomography (LDCT) has been widely adopted for lung cancer screening due to its proven ability to reduce lung cancer mortality, especially among high-risk populations. Methods: This retrospective study aims to evaluate the impact of LDCT screening on non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, following the introduction of LDCT in 2013. The study examines the correlation between LDCT screening volume and changes in the distribution of NSCLC stages, particularly early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, it explores the differences in histopathological subtypes, focusing on adenocarcinoma and squamous cell carcinoma, and assesses the impact of early detection on five-year survival rates. Results: The results show a significant increase in early-stage NSCLC diagnoses, particularly in adenocarcinoma cases, where early-stage diagnoses rose from 10.4% in 2010 to 38.7% in 2019. However, the number of stage IV cases remained stable, indicating that LDCT may not substantially reduce late-stage diagnoses. Pearson’s correlation analysis demonstrated a strong positive correlation between LDCT screening and early-stage NSCLC detection, particularly for adenocarcinoma (p < 0.001), though the early detection of squamous cell carcinoma and small cell carcinoma remained limited. Conclusions: The study concludes that LDCT screening plays a crucial role in improving early NSCLC detection and five-year survival rates. Future research should focus on optimizing screening strategies to capture more at-risk populations and enhance the detection of harder-to-diagnose subtypes like squamous cell carcinoma. Full article

Background: Lung cancer screening guidelines prioritize individuals with a history of smoking due to their higher risk of the disease. Methods: Our study examines the awareness and interest in low-dose computed tomography (LDCT) lung cancer screening among different smoking statuses using data from the National Cancer Institute’s Health Information National Trends Survey (HINTS) 6 (2022). We analyzed data from HINTS 6, including 3915 participants on smoking status, LDCT screening, and telehealth use. Participants were categorized as current smokers, former smokers, and non-smokers. Results: Current smokers had the highest likelihood of being recommended for LDCT screening (OR: 7.1, aOR: 10.4) compared with non-smokers. Former smokers also had increased odds of screening recommendations (OR: 3.1, aOR: 3.4) than non-smokers. Despite higher screening recommendations, current smokers exhibited significantly lower interest in cancer screening (interest rating score: 2.1) compared with non-smokers (interest rating score: 2.4) and former smokers (interest rating score: 2.5). Current smokers rated their telehealth care experiences more positively in terms of care quality compared with non-smokers. Conclusions: Our findings underscore a gap in cancer screening interest among current smokers despite their higher likelihood of being recommended for LDCT screening. The favorable perception of telehealth among current smokers provides an opportunity to enhance engagement and promote LDCT scan through telehealth care. Full article

Background: Lung cancer (LC) is the leading cause of cancer-related deaths. Although low-dose computed tomography (LD-CT) reduces mortality, its clinical use is limited by cost, radiation, and false positives. Therefore, there is an urgent need for non-invasive and cost-effective biomarkers. The Raf Kinase Inhibitor Protein (RKIP) plays a crucial role in cancer development and progression and may also contribute to regulating the tumor–immune system axis. This protein has recently been described in biological fluids. Therefore, we conducted a pilot case–control study to assess RKIP and phosphorylated RKIP (pRKIP) levels in the urine and blood of LC patients. Methods: A novel enzyme linked immunosorbent assay (ELISA) assay was used to measure RKIP and pRKIP levels in urine and blood samples of two cohorts of LC patients and healthy controls (HSs). Furthermore, the biomarkers levels were correlated with tumor characteristics. Results: Serum, but not urine, levels of RKIP were significantly elevated in LC patients, distinguishing them from low- and high-risk healthy subjects with 93% and 74% accuracy, respectively. The RKIP/pRKIP ratio (RpR score) showed an accuracy of 90% and 79% in distinguishing LC patients from HS and HR-HS, respectively. Additionally, the RpR score correlated better with dimension, stage, and lymph node involvement in the tumor group. Conclusions: The serum RKIP and pRKIP profile may be a promising novel biomarker for early-stage LC. Full article

Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both humans and animals, yet radiation exposure remains a significant concern, especially in animal imaging. Low-dose CT (LDCT) minimizes radiation exposure but often compromises image quality due to a reduced signal-to-noise ratio (SNR). Recent advancements in deep learning, particularly with CycleGAN, offer promising solutions for denoising LDCT images, though challenges in preserving anatomical detail and image sharpness persist. This study introduces a novel framework tailored for animal LDCT imaging, integrating deep learning techniques within the CycleGAN architecture. Key components include BlurPool for mitigating high-resolution image distortion, PixelShuffle for enhancing expressiveness, hierarchical feature synthesis (HFS) networks for feature retention, and spatial channel squeeze excitation (scSE) blocks for contrast reproduction. Additionally, a multi-scale discriminator enhances detail assessment, supporting effective adversarial learning. Rigorous experimentation on veterinary CT images demonstrates our framework’s superiority over traditional denoising methods, achieving significant improvements in noise reduction, contrast enhancement, and anatomical structure preservation. Extensive evaluations show that our method achieves a precision of 0.93 and a recall of 0.94. This validates our approach’s efficacy, highlighting its potential to enhance diagnostic accuracy in veterinary imaging. We confirm the scSE method’s critical role in optimizing performance, and robustness to input variations underscores its practical utility. Full article

Lung cancer mortality rates, particularly non-small cell lung cancer (NSCLC), continue to present a significant global health challenge, and the adoption of lung cancer screening remains limited, often influenced by inequities in access to healthcare. Despite clinical evidence demonstrating the efficacy of annual screening with low-dose computed tomography (LDCT) and recommendations from medical organizations including the U.S. Preventive Services Task Force (USPSTF), the national lung cancer screening uptake remains around 5% among eligible individuals. Advancements in the clinical management of NSCLC have recently become more personalized with the implementation of blood-based biomarker testing. Extensive research into tumor-derived cell-free DNA (cfDNA) through fragmentation offers a novel method for improving early lung cancer detection. This review assesses the screening landscape, explores obstacles to lung cancer screening, and discusses how a plasma whole genome fragmentome test (pWGFrag-Lung) can improve lung cancer screening participation and adherence. Full article