Fu-Zong Wu

Lung cancer screening has been widely studied, and strong evidence supports its role in reducing mortality among heavy smokers. The 2011 National Lung Screening Trial demonstrated a 20% reduction in lung cancer mortality, with further validation from European trials, such as NELSON and MILD. In 2015, the United States Centers for Medicare & Medicaid Services approved lung cancer screening as a reimbursable service, later expanding the criteria in 2021 to include individuals aged 50–80 years with a ≥20 pack-year smoking history. While screening models such as the Prostate, Lung, Colorectal, and Ovarian (PLCO) have effectively stratified risk among smokers, emerging research on non-smokers remains inconclusive. This review highlights five key issues in lung cancer screening in Eastern and Western countries. First, the screening rates differ significantly between regions owing to variations in healthcare policies and awareness. Second, subsolid nodule (SSN) prevalence varies between Eastern and Western populations, influencing screening strategies. Third, differences in SSN growth thresholds affect clinical decision-making and patient outcomes. Fourth, there are variations in the management of SSNs, particularly in follow-up recommendations and intervention strategies. Fifth, overdiagnosis remains a critical concern, with distinct challenges in each region owing to screening frequency and healthcare infrastructure. Additionally, microsimulation models predict a decline in smoking-related lung cancer but an increase in non-smoking-related cases, emphasizing the need for tailored screening approaches. Addressing these five issues is crucial for optimizing lung cancer screening strategies and balancing early detection with the risk of overdiagnosis.

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.

Background: This study aimed to create a nomogram to predict a double-zero score for obstructive coronary artery disease (CAD) in a hospital-based cohort initially scoring zero. We compared its diagnostic performance with the Framingham risk score (FRS) and models for atherosclerotic cardiovascular disease (ASCVD).Methods: We retrospectively reviewed the clinical features and laboratory profiles of 634 participants with baseline zero coronary artery calcium scores. The target population consisted of individuals with a double-zero score. The primary endpoint was the diagnosis of obstructive CAD, defined as CAD-RADS ≥3 or vulnerable plaque formation on the second cardiac CT. The control group had a double-zero score, with no or less than 50% coronary stenosis. We developed a nomogram using a least absolute shrinkage and selection operation-derived logistic model. We assessed the models’ discrimination and calibration abilities using the Hosmer–Lemeshow test.Results: Participants were monitored for an average period of 4.26 ± 2.30 years and were randomly allocated to training and validation sets at a ratio of 2.8:1. The study results indicated that 5.13% (24 of 467) in the training cohort and 4.19% (7 of 167) in the validation cohort developed a double-zero score with obstructive CAD progression. This nomogram incorporated four predictors: “systolic blood pressure,” “hypertension,” “body fat percentage,” and “HbA1c.” The nomogram demonstrated superior diagnostic performance compared to the FRS and ASCVD models, with lower values of Akaike information criterion and Bayesian information criterion values. The nomogram's discriminative ability, measured by the area under the curve, was 0.792 in the training cohort and 0.824 in the validation cohort.Conclusions: The validated nomogram provides valuable predictive potential for identifying high-risk subclinical coronary atherosclerosis, thereby supporting personalized primary prevention and education strategies.

The coronavirus disease 2019 (COVID-19) pandemic has disrupted healthcare systems, significantly affecting preventive services such as low-dose computed tomography (LDCT) for lung cancer screening. We aimed to evaluate the pandemic’s impact on LDCT screening practices at Kaohsiung Veterans General Hospital, focusing on the changes in participation rates, Lung-RADS categories, and lung cancer diagnoses to guide the development of interventions for improving screening programs and early detection during health crises. A retrospective cohort of 56,730 individuals who underwent health examinations between 2017 and 2023 was analyzed. Data on demographics, smoking history, family history of lung cancer, and eligibility for LDCT subsidies in Taiwan were obtained. Screening utilization and outcomes were cross-referenced with cancer registries and imaging databases. A subset of 17,743 individuals who underwent LDCT were examined to determine the pre- and post-COVID19 differences in smoking prevalence, family history, high-risk Lung Imaging Reporting and Data System (Lung-RADS) categories (3 or 4), and lung cancer diagnoses. Following the implementation of the level 3 alert, notable shifts were observed in smoking habits and lung cancer screening eligibility. The prevalence of heavy smokers (≥30 pack-years) declined from 6.9% before the alert to 6.1% after (p = 0.002). Conversely, the proportion of individuals with a family history of lung cancer qualifying for LDCT screening increased significantly from 6.0% to 6.6% (p = 0.009). Additionally, the prevalence of lung cancer diagnoses among individuals with high-risk Lung-RADS categories (Lung-RADS 3 or 4) decreased significantly from 21.5% before the alert to 13.4% after (p = 0.037). The pandemic disrupted LDCT screening, reducing access for high-risk smokers while increasing non-smoker participation. High-risk nodules declined but partially recovered post-pandemic. Future policies must prioritize high-risk individuals, optimize resources, and enhance early detection to improve outcomes and crisis preparedness.

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.

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Owing to the widespread opportunistic LDCT screening leading to increased overdiagnosis in Asian countries, such as South Korea, mainland China, and Taiwan, this study seeks to analyze the divergence in SSN prevalence between Eastern and Western nations, focusing on the influence of SSN on the growing overdiagnosis trend, notably among females. This retrospective study collected data from 4166 participants who underwent baseline LDCT in a hospital-based cohort between January 2014 and August 2021. Clinical parameters, including age, sex, lung imaging reporting and data system (Lung-RADS) categories, smoking history, pack-year dose, and SSN characteristics, were extracted from electronic medical records. Additionally, a narrative review and pooled analysis integrated relevant published studies on the prevalence of subsolid nodules and sex disparities. The study encompassed 4166 participants, with females accounting for 49.3% and males for 50.7%, with a mean age of 53.38 ± 10.89. The prevalence of SSNs was significantly higher in females (20.1%) than in males (12.6%). Pooled analysis across seven studies revealed a significantly higher prevalence of SSN in Eastern countries (12.6%) compared to the prevalence in Western countries (3.6%) (test for subgroup differences: p < 0.01; I² = 100%). Additionally, a notable sex difference was observed in the prevalence of SSNs (risk ratio = 0.489, 95% CI: 0.301-0.796, p < 0.01; reference group: male group). Apart from differences in clinical management and health literacy regarding SSNs between Eastern and Western countries, the high prevalence of SSNs in Asian nations, particularly among females, significantly contributes to the issue of overdiagnosis in opportunistic lung cancer screening in Asian countries. Tailored sex-specific strategies and risk prediction models are essential for effective screening optimization.