Objective: To systematically evaluate the long-term trajectory of pulmonary function test (PFT) and CT findings in COVID-19 survivors. Materials and Methods: A systematic literature search of PubMed and EMBASE was performed to identify studies published from January 2020 to June 2024 reporting PFT and/or chest CT outcomes at ≥6 months post-COVID-19, up to 36 months. The reference lists of relevant articles were also manually reviewed. Two investigators independently extracted study characteristics, patient demographics, and PFT and CT outcomes at prespecified follow-up intervals (6, 12, 24, and 36 months). Multivariate meta-analyses were conducted to evaluate temporal trends in lung function and radiological abnormalities. Sensitivity analyses, including stratification by disease severity and pooled analyses of studies with multiple follow-up time points, were performed to confirm the robustness of the findings. Results: In total, 152 studies (n = 25,766; mean age, 56.7 ± 13.2 years; 14,999 men) were included: 133 reporting PFT outcomes and 80 reporting CT findings. Diffusion capacity (DLCO) impairment was the most common abnormality, showing gradual improvement from 42% at 6 months to 35% at 36 months (P = 0.008) with a corresponding increase in the % predicted DLCO. Similarly, the prevalence of forced vital capacity (FVC) impairment decreased over time, accompanied by an increase in the % predicted FVC. On chest CT, the proportion of patients with no relevant findings remained stable at 30%–40% (P = 0.14).The prevalence of ground-glass opacities (GGO) decreased from 32% at 6 months to 20% at 36 months (P = 0.01), while that of fibrosis persisted at 27%–47% without a significant change (P = 0.28). Subgroup analysis based on disease severity revealed similar temporal trends in both low-severity and high-severity cohorts. Conclusion DLCO, FVC, and GGO findings improved gradually up to 36 months post-COVID-19; however, over one-third of the patients continued to exhibit reduced DLCO. Fibrosis persists with limited evidence of resolution over a 3-year period, suggesting a stable but nonprogressive pattern.

Artificial intelligence-based segmentation and quantification is a hot topic in cardiovascular imaging. However, the development of clinically useful artificial intelligence-based models for congenital heart disease is challenging owing to morphologic heterogeneity and limited data for training. Although cardiovascular computed tomography is increasingly used in evaluating patients with congenital heart disease, only a few reports have evaluated artificial intelligence-based segmentation algorithms for congenital heart disease. To be familiarized with this trend, we need to understand state-of-the-art segmentation techniques for cardiovascular digital twins as well as basic concept, current challenges, and potential solutions of artificial intelligence-based methods. Additionally, in this review, our learnings from the literature and our pilot study are summarized, and future directions are suggested.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories. Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups. Results: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08–21.98). Conclusion Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia.Appropriate therapeutic strategies should be tailored based on these disease trajectories.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories. Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups. Results: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08–21.98). Conclusion Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia.Appropriate therapeutic strategies should be tailored based on these disease trajectories.

Background: Currently, little is known about the relationship between the temporal radiographic latent trajectories, which are based on the extent of coronavirus disease 2019 (COVID-19) pneumonia and clinical outcomes. This study aimed to elucidate the differences in the temporal trends of critical laboratory biomarkers, utilization of critical care support, and clinical outcomes according to temporal radiographic latent trajectories. Methods: We enrolled 2,385 patients who were hospitalized with COVID-19 and underwent serial chest radiographs from December 2019 to March 2022. The extent of radiographic pneumonia was quantified as a percentage using a previously developed deep-learning algorithm. A latent class growth model was used to identify the trajectories of the longitudinal changes of COVID-19 pneumonia extents during hospitalization. We investigated the differences in the temporal trends of critical laboratory biomarkers among the temporal radiographic trajectory groups. Cox regression analyses were conducted to investigate differences in the utilization of critical care supports and clinical outcomes among the temporal radiographic trajectory groups. Results: The mean age of the enrolled patients was 58.0 ± 16.9 years old, with 1,149 (48.2%) being male. Radiographic pneumonia trajectories were classified into three groups: The steady group (n = 1,925, 80.7%) exhibited stable minimal pneumonia, the downhill group (n = 135, 5.7%) exhibited initial worsening followed by improving pneumonia, and the uphill group (n = 325, 13.6%) exhibited progressive deterioration of pneumonia. There were distinct differences in the patterns of temporal blood urea nitrogen (BUN) and C-reactive protein (CRP) levels between the uphill group and the other two groups. Cox regression analyses revealed that the hazard ratios (HRs) for the need for critical care support and the risk of intensive care unit admission were significantly higher in both the downhill and uphill groups compared to the steady group. However, regarding in-hospital mortality, only the uphill group demonstrated a significantly higher risk than the steady group (HR, 8.2; 95% confidence interval, 3.08–21.98). Conclusion Stratified pneumonia trajectories, identified through serial chest radiographs, are linked to different patterns of temporal changes in BUN and CRP levels. These changes can predict the need for critical care support and clinical outcomes in COVID-19 pneumonia.Appropriate therapeutic strategies should be tailored based on these disease trajectories.

Purpose: We aim to determine whether preoperative percutaneous needle aspiration or biopsy (PCNA/Bx) increases recurrence risk and reduces survival in stage I lung cancer patients, using a nationwide lung cancer registry. Materials and Methods: We retrospectively included 3,452 patients diagnosed with stage I lung cancer who underwent curative surgery between 2014 and 2019, as recorded in the Korean Association of Lung Cancer Registry. To balance the characteristics of patients with and without PCNA/Bx, we applied inverse probability of treatment weighting. We used cumulative incidence plots and a weighted subdistribution hazard model to analyze time to recurrence. Recurrence-free survival and overall survival were analyzed using Kaplan-Meier curves and weighted Cox proportional hazard ratio models. Results: In patients with adenocarcinoma, the use of PCNA/Bx was associated with a 1.9-fold increase (95% confidence interval [CI], 1.5 to 2.4) in the risk of recurrence and a 1.7-fold decrease (95% CI, 1.3 to 2.2) in recurrence-free survival. Subgroup analysis based on pathologic pleural invasion revealed that the risk of recurrence increased when PCNA/Bx was performed, with 2.1-fold (95% CI, 1.5 to 2.8) in patients without pleural invasion and 1.6-fold (95% CI, 1.0 to 2.4) in those with pleural invasion. No association was found between the use of PCNA/Bx and overall survival. Conclusion Preoperative PCNA/Bx was associated with increased recurrence risks in stage I adenocarcinoma, regardless of pathologic pleural invasion status. In early lung cancer cases where adenocarcinoma is strongly suspected and curative surgery is feasible, the use of transthoracic biopsy should be approached with caution.