Radiologic Abnormalities in Prolonged SARS-CoV-2 Infection: A Systematic Review.

We systematically reviewed radiological abnormalities in patients with prolonged SARS-CoV-2 infection, defined as persistently positive polymerase chain reaction (PCR) results for SARS-CoV-2 for > 21 days, with either persistent or relapsed symptoms. We extracted data from 24 patients (median age, 54.5 [interquartile range, 44-64 years]) reported in the literature and analyzed their representative CT images based on the timing of the CT scan relative to the initial PCR positivity. Our analysis focused on the patterns and distribution of CT findings, severity scores of lung involvement on a scale of 0-4, and the presence of migration. All patients were immunocompromised, including 62.5% (15/24) with underlying lymphoma and 83.3% (20/24) who had received anti-CD20 therapy within one year. Median duration of infection was 90 days. Most patients exhibited typical CT appearance of coronavirus disease 19 (COVID-19), including ground-glass opacities with or without consolidation, throughout the follow-up period. Notably, CT severity scores were significantly lower during ≤ 21 days than during > 21 days (P < 0.001). Migration was observed on CT in 22.7% (5/22) of patients at ≤ 21 days and in 68.2% (15/22) to 87.5% (14/16) of patients at > 21 days, with rare instances of parenchymal bands in previously affected areas. Prolonged SARS-CoV-2 infection usually presents as migrating typical COVID-19 pneumonia in immunocompromised patients, especially those with impaired B-cell immunity.

Is the Fast Track Safe? A Safety Evaluation of the COVID-19 Drugs with Real-World Data.

The US FDA has given emergency use authorization to multiple Covid-19 drugs. We conducted disproportionality analysis to detect adverse reaction rates (ADRs) in the US FDA's Adverse Event Reporting System. We discovered not only severe ADRs but also unique ADRs and effectiveness that might be explained by pharmacokinetics. This shows that real-time safety reviews could be effective methods for comparing the safety and efficacy of fast-track authorized drugs.

Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs.

PURPOSE: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS: GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.

CT analysis of thoracolumbar body composition for estimating whole-body composition.

BACKGROUND: To evaluate the correlation between single- and multi-slice cross-sectional thoracolumbar and whole-body compositions. METHODS: We retrospectively included patients who underwent whole-body PET-CT scans from January 2016 to December 2019 at multiple institutions. A priori-developed, deep learning-based commercially available 3D U-Net segmentation provided whole-body 3D reference volumes and 2D areas of muscle, visceral fat, and subcutaneous fat at the upper, middle, and lower endplate of the individual T1-L5 vertebrae. In the derivation set, we analyzed the Pearson correlation coefficients of single-slice and multi-slice averaged 2D areas (waist and T12-L1) with the reference values. We then built prediction models using the top three correlated levels and tested the models in the validation set. RESULTS: The derivation and validation datasets included 203 (mean age 58.2 years; 101 men) and 239 patients (mean age 57.8 years; 80 men). The coefficients were distributed bimodally, with the first peak at T4 (coefficient, 0.78) and the second peak at L2-3 (coefficient 0.90). The top three correlations in the abdominal scan range were found for multi-slice waist averaging (0.92) and single-slice L3 and L2 (0.90, each), while those in the chest scan range were multi-slice T12-L1 averaging (0.89), single-slice L1 (0.89), and T12 (0.86). The model performance at the top three levels for estimating whole-body composition was similar in the derivation and validation datasets. CONCLUSIONS: Single-slice L2-3 (abdominal CT range) and L1 (chest CT range) analysis best correlated with whole-body composition around 0.90 (coefficient). Multi-slice waist averaging provided a slightly higher correlation of 0.92.

A time-dependent subdistribution hazard model for major dental treatment events in cancer patients: a nationwide cohort study.

BACKGROUND: Dental care in cancer patients tends to be less prioritized. However, limited research has focused on major dental treatment events in cancer patients after the diagnosis. This study aimed to examine dental treatment delays in cancer patients compared to the general population using a national claims database in South Korea. METHOD: The Korea National Health Insurance Service-National Sample Cohort version 2.0, collected from 2002 to 2015, was analyzed. Treatment events were considered for stomatitis, tooth loss, dental caries/pulp disease, and gingivitis/periodontal disease. For each considered event, time-dependent hazard ratios and associated 95% confidence intervals were calculated by applying a subdistribution hazard model with time-varying covariates. Mortality was treated as a competing event. Subgroup analyses were conducted by type of cancer. RESULTS: The time-dependent subdistribution hazard ratios (SHRs) of stomatitis treatment were greater than 1 in cancer patients in all time intervals, 2.04 within 30 days after cancer diagnosis, and gradually decreased to 1.15 after 5 years. The SHR for tooth loss was less than 0.70 within 3 months after cancer diagnosis and increased to 1 after 5 years. The trends in SHRs of treatment events for other dental diseases were similar to those observed for tooth loss. Subgroup analyses by cancer type suggested that probability of all dental treatment event occurrence was higher in head and neck cancer patients, particularly in the early phase after cancer diagnosis. CONCLUSION: Apart from treatments that are associated with cancer therapy, dental treatments in cancer patients are generally delayed and cancer patients tend to refrain from dental treatments. Consideration should be given to seeking more active and effective means for oral health promotion in cancer patients.

Effect of weekend catch-up sleep on high-sensitivity C-reactive protein levels according to bedtime inconsistency: a population-based cross-sectional study.

To investigate the associations of weekend catch-up sleep (WCS) and high-sensitivity C-reactive protein (hs-CRP) levels according to bedtime inconsistency in the Korean population. In this cross-sectional study using the Korea National Health and Nutrition Examination Survey (2016-2018) with 17,665 participants, four groups were defined: no-WCS (WCS within ± 1 h of weekday sleep time), moderate WCS (1 ≤ , < 3 h), severe WCS (≥ 3 h), and inverse WCS (≤ - 1 h). An inconsistent bedtime was defined as a > 2 h difference between weekend and weekday bedtimes. Outcomes were divided into quartiles based on the hs-CRP level: Lowest (< 0.34), Middle-low (≥ 0.34, < 0.55), Middle-high (≥ 0.55, < 1.10), Highest (≥ 1.10). Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were calculated using multinomial logistic regression, controlling for relevant covariates. Moderate WCS was associated with a lower risk for the highest hs-CRP levels than no WCS (aOR = 0.87, 95% CI 0.78-0.97), and a similar association was observed only in participants with consistent bedtimes (aOR = 0.88, 95% CI 0.78-0.99). Significant interactions of those associations of WCS and hs-CRP levels with bedtime inconsistency were found. These findings provide evidence that people with inconsistent bedtimes would have limited protective effect of WCS on hs-CRP.

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors.

OBJECTIVES: Because climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases. METHODS: We obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the relief-based feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R2) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model. RESULTS: Features with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients. CONCLUSION: We successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.

BACKGROUND: In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency. METHODS: The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets. RESULTS: Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated. CONCLUSIONS: Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.

CT quantification of the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis.

OBJECTIVES: To quantify the heterogeneity of fibrosis boundaries in idiopathic pulmonary fibrosis (IPF) using the Gaussian curvature analysis for evaluating disease severity and predicting survival. METHODS: We retrospectively included 104 IPF patients and 52 controls who underwent baseline chest CT scans. Normal lungs below - 500 HU were segmented, and the boundary was three-dimensionally reconstructed using in-house software. Gaussian curvature analysis provided histogram features on the heterogeneity of the fibrosis boundary. We analyzed the correlations between histogram features and the gender-age-physiology (GAP) and CT fibrosis scores. We built a regression model to predict diffusing capacity of carbon monoxide (DLCO) using the histogram features and calculated the modified GAP (mGAP) score by replacing DLCO with the predicted DLCO. The performances of the GAP, CT-GAP, and mGAP scores were compared using 100 repeated random-split sets. RESULTS: Patients with moderate-to-severe IPF had more numerous Gaussian curvatures at the fibrosis boundary, lower uniformity, and lower 10th to 30th percentiles of Gaussian curvature than controls or patients with mild IPF (all p < 0.0033). The 20th percentile was most significantly correlated with the GAP score (r = - 0.357; p < 0.001) and the CT fibrosis score (r = - 0.343; p = 0.001). More numerous Gaussian curvatures, higher entropy, lower uniformity, and 10th to 30th percentiles (p < 0.001-0.041) were associated with mortality. The mGAP score was comparable to the GAP and CT-GAP scores for survival prediction (mean C-indices, 0.76 vs. 0.79 vs. 0.77, respectively). CONCLUSIONS: Gaussian curvatures of fibrosis boundaries became more heterogeneous as the disease progressed, and heterogeneity was negatively associated with survival in IPF. KEY POINTS: • Gaussian curvature of the fibrotic lung boundary was more heterogeneous in patients with moderate-to-severe IPF than those with mild IPF or normal controls. • The 20th percentile of the Gaussian curvature of the fibrosis boundary was linearly correlated with the GAP score and the CT fibrosis score. • A modified GAP score that replaced the diffusing capacity of carbon monoxide with a composite measure using histogram features of the Gaussian curvature of the fibrosis boundary showed a comparable ability to predict survival to both the GAP and the CT-GAP score.

Author Correction: Inspiratory Lung Expansion in Patients with Interstitial Lung Disease: CT Histogram Analyses.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Development of an algorithm for evaluating the impact of measurement variability on response categorization in oncology trials.

BACKGROUND: Radiologic assessments of baseline and post-treatment tumor burden are subject to measurement variability, but the impact of this variability on the objective response rate (ORR) and progression rate in specific trials has been unpredictable on a practical level. In this study, we aimed to develop an algorithm for evaluating the quantitative impact of measurement variability on the ORR and progression rate. METHODS: First, we devised a hierarchical model for estimating the distribution of measurement variability using a clinical trial dataset of computed tomography scans. Next, a simulation method was used to calculate the probability representing the effect of measurement errors on categorical diagnoses in various scenarios using the estimated distribution. Based on the probabilities derived from the simulation, we developed an algorithm to evaluate the reliability of an ORR (or progression rate) (i.e., the variation in the assessed rate) by generating a 95% central range of ORR (or progression rate) results if a reassessment was performed. Finally, we performed validation using an external dataset. In the validation of the estimated distribution of measurement variability, the coverage level was calculated as the proportion of the 95% central ranges of hypothetical second readings that covered the actual burden sizes. In the validation of the evaluation algorithm, for 100 resampled datasets, the coverage level was calculated as the proportion of the 95% central ranges of ORR results that covered the ORR from a real second assessment. RESULTS: We built a web tool for implementing the algorithm (publicly available at http://studyanalysis2017.pythonanywhere.com/ ). In the validation of the estimated distribution and the algorithm, the coverage levels were 93 and 100%, respectively. CONCLUSIONS: The validation exercise using an external dataset demonstrated the adequacy of the statistical model and the utility of the developed algorithm. Quantification of variation in the ORR and progression rate due to potential measurement variability is essential and will help inform decisions made on the basis of trial data.

Inspiratory Lung Expansion in Patients with Interstitial Lung Disease: CT Histogram Analyses.

This study aimed to evaluate inspiratory lung expansion in patients with interstitial lung disease (ILD) using histogram analyses based on advanced image registration between inspiratory and expiratory CT scans. We included 16 female ILD patients and eight age- and sex-matched normal controls who underwent full-inspiratory and expiratory CT scans. The CT scans were sequentially aligned based on the surface, landmarks, and attenuation of the lung parenchyma. Histogram analyses were performed on the degree of lung expansion (DLE) of each pixel between the aligned images in x-, y-, z-axes, and 3-dimensionally (3D). The overall mean registration error was 1.9 mm between the CT scans. The DLE3D in ILD patients was smaller than in the controls (mean, 17.6 mm vs. 26.9 mm; p = 0.023), and less heterogeneous in terms of standard deviation, entropy, and uniformity (p < 0.05). These results were mainly due to similar results in the DLEZ of the lower lungs. A forced vital capacity tended to be weakly correlated with mean (r2 = 0.210; p = 0.074), and histogram parameters (r2 = 0.194-0.251; p = 0.048-0.100) of the DLE3D in the lower lung in ILD patients. Our findings indicate that reduced and less heterogeneous inspiratory lung expansion in ILD patients can be identified by using advanced accurate image registration.

Combination of sodium-glucose cotransporter 2 inhibitor and dipeptidyl peptidase-4 inhibitor in type 2 diabetes: a systematic review with meta-analysis.

Sodium glucose cotransporter 2 (SGLT2) inhibitors and dipeptidyl peptidase-4 (DPP4) inhibitors have complementary mode of action. For the meta-analysis comparing the efficacy and safety between SGLT2 inhibitor plus DPP4 inhibitor (SGLT2i/DPP4i) and placebo plus DPP4 inhibitor (PCB/DPP4i) in patients with type 2 diabetes mellitus (T2DM), we selected randomized controlled trials from electronic databases by predefined criteria. The primary outcome of interest was the change in glycated hemoglobin A1c (HbA1c) from baseline. Of 605 potentially relevant studies, 7 eligible RCTs comprising 2,082 patients were included.SGLT2i/DPP4i showed a greater reduction in HbA1c (weighted mean difference -0.6%, 95% CI -0.7 to -0.5%), fasting plasma glucose, 2 h postprandial plasma glucose, and body weight compared to PCB/DPP4i. The risk of hypoglycemia increased in SGLT2i/DPP4i compared to that in PCB/DPP4i only when insulin or sulfonylureas were included as a background therapy. The risk of urinary tract infection was not increased in SGLT2i/DPP4i; however, the risk of genital infection increased upon adding SGLT2 inhibitors to pre-existing DPP4 inhibitors. In conclusion, compared to PCB/DPP4i, SGLT2i/DPP4i achieved better glycemic control and greater weight reduction without increasing the risk of hypoglycemia and urinary tract infection in patients with inadequately controlled T2DM.

Comparison of non-insulin antidiabetic agents as an add-on drug to insulin therapy in type 2 diabetes: a network meta-analysis.

We aimed to evaluate the comparative efficacy and safety of dipeptidyl peptidase-4 inhibitors (DPP4i), glucagon-like peptide-1 receptor agonists (GLP-1RA), sodium-glucose co-transporter 2 inhibitors (SGLT2i), or thiazolidinedione (TZD) as an adjunctive treatment in patients with poorly controlled type 2 diabetes mellitus (T2DM) on insulin therapy. We searched Medline, Embase, the Cochrane Library, and ClinicalTrials.gov through April 2016. Bayesian network meta-analyses were performed with covariate adjustment. The primary outcome was the change in glycated hemoglobin A1c (HbA1c) from baseline. Fifty randomized controlled trials covering 15,494 patients were included. GLP-1RA showed the greatest HbA1c-lowering effect compared to the control (-0.84%; 95% credible interval, -1.00% to -0.69%), followed by TZD (-0.73%; -0.93 to -0.52%), SGLT2i (-0.66%; -0.84% to -0.48%), and DPP4i (-0.54%; -0.68% to -0.39%). SGLT2i showed the greatest fasting plasma glucose reduction. GLP-1RA and SGLT2i showed greater body weight reduction, whereas TZD increased body weight. TZD was ranked the highest in terms of insulin dose reduction. The risk of hypoglycemia was increased with TZD or GLP-1RA. The study provides the best available evidence on the comparative efficacy and safety of non-insulin anti-diabetic agents on top of pre-existing insulin therapy for inadequately controlled T2DM patients.

Efficacy and safety of combination therapy with an α-glucosidase inhibitor and a dipeptidyl peptidase-4 inhibitor in patients with type 2 diabetes mellitus: A systematic review with meta-analysis.

AIMS/INTRODUCTION: The combination of dipeptidyl peptidase-4 (DPP4) inhibitors and α-glucosidase inhibitors (AGIs) might provide an additive or synergistic glucose-lowering effect, as they have a complementary mode of action. In the present study, we examined the efficacy and safety of the addition of a DPP4 inhibitor to patients with type 2 diabetes inadequately controlled with an AGI. MATERIALS AND METHODS: We carried out an electronic search of MEDLINE, EMBASE, the Cochrane Library and Clinicaltrials.gov through October 2016. Randomized controlled trials written in English that compared DPP4 inhibitors plus AGI (DPP4i/AGI) and placebo plus AGI (PCB/AGI) in patients with type 2 diabetes were selected. Data on the study characteristics, efficacy and safety outcomes were extracted, and the risk of potential biases was assessed. The efficacy and safety of DPP4i/AGI and PCB/AGI were compared. RESULTS: Of 756 potentially relevant published articles and 40 registered trials, five studies including 845 patients randomized to DPP4i/AGI and 832 patients randomized to PCB/AGI were included for meta-analysis. Compared with PCB/AGI, DPP4i/AGI showed a greater reduction in glycated hemoglobin (weighted mean difference -1.2%, 95% confidence interval -1.6 to -0.8), fasting plasma glucose and 2-h postprandial plasma glucose levels, with no increase in bodyweight. The risks of hypoglycemia and gastrointestinal adverse events were similar between DPP4i/AGI and PCB/AGI. CONCLUSIONS: The addition of a DPP4 inhibitor to patients with type 2 diabetes inadequately controlled with an AGI achieved better glycemic control without further increasing the risk of weight gain and hypoglycemia.

Comparison between SGLT2 inhibitors and DPP4 inhibitors added to insulin therapy in type 2 diabetes: a systematic review with indirect comparison meta-analysis.

BACKGROUND: Both sodium glucose cotransporter 2 (SGLT2) inhibitors and dipeptidyl peptidase-4 (DPP4) inhibitors can be used to treat patients with type 2 diabetes mellitus (T2DM) that is inadequately controlled with insulin therapy, and yet there has been no direct comparison of these two inhibitors. METHODS: We searched MEDLINE, EMBASE, LILACS, the Cochrane Central Register of Controlled Trials and ClinicalTrials.gov through June 2015. Randomized controlled trials published in English that compare SGLT2 inhibitor plus insulin (SGLT2i/INS) with placebo plus insulin or DPP4 inhibitor plus insulin (DPP4i/INS) with placebo plus insulin in patients with T2DM were selected. Data on the study characteristics, efficacy and safety outcomes were extracted. We compared the efficacy and safety between SGLT2i/INS and DPP4i/INS indirectly with covariates adjustment. Risk of potential bias was assessed. RESULTS: Fourteen eligible randomized controlled trials comprising 6980 patients were included (five SGLT2 inhibitor studies and nine DPP4 inhibitor studies). Covariate-adjusted indirect comparison using meta-regression analyses revealed that SGLT2i/INS achieved greater reduction in HbA1c [weighted mean difference (WMD) -0.24%, 95% confidence interval (CI) -0.43 to -0.05%], fasting plasma glucose (WMD -18.0 mg/dL, 95% CI -28.5 to -7.6 mg/dL) and body weight (WMD -2.38 kg, 95% CI -3.18 to -1.58 kg) from baseline than DPP4i/INS without increasing the risk of hypoglycaemia (relative risks 1.19, 95% CI 0.78 to 1.82). CONCLUSIONS: Sodium glucose cotransporter 2 inhibitors achieved better glycaemic control and greater weight reduction than DPP4 inhibitors without increasing the risk of hypoglycaemia in patients with T2DM that is inadequately controlled with insulin. There has been no direct comparison of SGLT2 inhibitors and DPP4 inhibitors in patients with T2DM inadequately controlled with insulin therapy. In this study, we performed indirect meta-analysis comparing SGLT2 inhibitors and DPP4 inhibitors added to insulin therapy. Without increasing hypoglycaemia, SGLT2 inhibitors showed better glycaemic control and greater weight reduction than DPP4 inhibitors in patients with T2DM inadequately controlled with insulin. The results of the current study could serve as the best available evidence in selecting oral agents to improve glycaemic control in insulin-treated T2DM patients. Copyright © 2016 John Wiley & Sons, Ltd.

Ossification of the Medial Clavicular Epiphysis on Chest Radiographs: Utility and Diagnostic Accuracy in Identifying Korean Adolescents and Young Adults under the Age of Majority.

The aim of our study was to evaluate the utility and diagnostic accuracy of the ossification grade of medial clavicular epiphysis on chest radiographs for identifying Korean adolescents and young adults under the age of majority. Overall, 1,151 patients (age, 16-30) without any systemic disease and who underwent chest radiography were included for ossification grading. Two radiologists independently classified the ossification of the medial clavicular epiphysis from chest radiographs into five grades. The age distribution and inter-observer agreement on the ossification grade were assessed. The diagnostic accuracy of the averaged ossification grades for determining whether the patient is under the age of majority was analyzed by using receiver operating characteristic (ROC) curves. Two separate inexperienced radiologists assessed the ossification grade in a subgroup of the patients after reviewing the detailed descriptions and image atlases developed for ossification grading. The median value of the ossification grades increased with increasing age (from 16 to 30 years), and the trend was best fitted by a quadratic function (R-square, 0.978). The inter-observer agreements on the ossification grade were 0.420 (right) and 0.404 (left). The area under the ROC curve (AUC) was 0.922 (95% CI, 0.902-0.942). The averaged ossification scores of 2.62 and 4.37 provided 95% specificity for a person < 19 years of age and a person ≥ 19 years of age, respectively. A preliminary assessment by inexperienced radiologists resulted in an AUC of 0.860 (95% CI, 0.740-0.981). The age of majority in Korean adolescents and young adults can be estimated using chest radiographs.

Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging.

Purpose To evaluate histogram and texture parameters on pretreatment dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images in lung cancer in terms of temporal change, optimal time for analysis, and prognostic potential. Materials and Methods This retrospective study was approved by the institutional review board, and the requirement to obtain informed consent was waived. Thirty-eight patients with pathologically proved lung cancer undergoing standard pretreatment DCE MR imaging were included. A fat-suppressed, T1-weighted, volume-interpolated breath-hold MR sequence was performed every 30 seconds for 300 and 480 seconds after contrast material administration. A region of interest was manually drawn in the largest cross-sectional area of the tumor on DCE MR images to extract semiquantitative perfusion, histogram, and texture parameters. Predictability of 2-year progression-free survival (PFS) was analyzed by using the Kaplan-Meier method and Cox regression analysis. Results MR histogram and texture parameters increased rapidly 30-60 seconds after contrast material administration. Standard deviation and entropy then plateaued, whereas skewness and kurtosis rapidly decreased. Univariate Cox regression analysis revealed that standard deviation and entropy were significant predictors of survival; their statistical significance was preserved from 60 to 300 seconds, with the smallest P values (P ≤ .001) occurring from 120 to 180 seconds. At multivariate Cox regression analysis, entropy was the sole significant predictor of 2-year PFS (hazard ratio at 180 seconds, 10.098 [95% confidence interval: 1.579, 64.577], P = .015; hazard ratio at 120 seconds: 11.202 [95% confidence interval: 1.761, 71.260], P = .010). Conclusion Histogram and texture parameter changes varied after contrast material injection. The 120-180-second window after contrast material injection was optimal for MR imaging-derived texture parameter and entropy at DCE MR imaging. (©) RSNA, 2016 Online supplemental material is available for this article.