Association between the cardiothoracic ratio on chest X-rays and the respiratory function in patients with interstitial lung diseases: A cross-sectional study.

Objective Patients with advanced interstitial lung disease (ILD) struggle to undergo spirometry to evaluate the respiratory function. The cardiothoracic ratio (CTR) on chest radiography can potentially reflect the lung volume; however, this has not yet been fully established. This study aimed to clarify the relationship between the CTR and the respiratory function in patients with interstitial lung diseases. Patients and Methods We reviewed 120 consecutive patients with idiopathic interstitial lung disease who were admitted to our department between April 2018 and March 2023 and who underwent chest radiography, spirometry, and echocardiography. A multiple linear regression analysis was used to identify the factors associated with the CTR. Correlations between the CTR and the respiratory or cardiac function were assessed using Pearson's correlation coefficient. Results A multiple linear regression analysis showed the percent vital capacity (ß = -0.598, p <0.001), age (ß = 0.405, p <0.001), and female sex (ß = 0.177, p = 0.047) to be independently associated with the CTR, whereas no relationship was observed between the left ventricular ejection fraction, body mass index, and smoking habits. The CTR was significantly negatively correlated with the vital capacity (r = -0.490, p <0.001). Conclusions An increased CTR might reflect a decreased vital capacity, but not a decreased cardiac function, in patients with interstitial lung diseases. Measuring the CTR can thus be beneficial for predicting progression in patients with ILD.

Automatic cardiothoracic ratio calculation based on lung fields abstracted from chest X-ray images without heart segmentation.

Introduction: The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR's right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images. Meanwhile, manual CTR measurement based on the P-A CXR image requires experienced radiologists and is time-consuming and laborious. Methods: Based on the above, this article proposes a novel, fully automatic CTR calculation method based on lung fields abstracted from the P-A CXR images using convolutional neural networks (CNNs), overcoming the limitations to heart segmentation and avoiding errors in heart segmentation. First, the lung field mask images are abstracted from the P-A CXR images based on the pre-trained CNNs. Second, a novel localization method of the heart's right and left border points is proposed based on the two-dimensional projection morphology of the lung field mask images using graphics. Results: The results show that the mean distance errors at the x-axis direction of the CTR's four key points in the test sets T1 (21 × 512 × 512 static P-A CXR images) and T2 (13 × 512 × 512 dynamic P-A CXR images) based on various pre-trained CNNs are 4.1161 and 3.2116 pixels, respectively. In addition, the mean CTR errors on the test sets T1 and T2 based on four proposed models are 0.0208 and 0.0180, respectively. Discussion: Our proposed model achieves the equivalent performance of CTR calculation as the previous CardioNet model, overcomes heart segmentation, and takes less time. Therefore, our proposed method is practical and feasible and may become an effective tool for initially evaluating cardiac diseases.

Measurement of Cardiothoracic Ratio on Chest X-rays Using Artificial Intelligence-A Systematic Review and Meta-Analysis.

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and meta-analysis evaluate the efficacy of artificial intelligence (AI) in automating CTR determination to enhance patient care and streamline diagnostic processes. They are concentrated on comparing the performance of AI models in determining the CTR against human assessments, identifying the most effective models for potential clinical implementation. This study was registered with PROSPERO (no. CRD42023437459). No funding was received. Methods: A comprehensive search of medical databases was conducted in June 2023. The search strategy adhered to the PICO framework. Inclusion criteria encompassed original articles from the last decade focusing on AI-assisted CTR assessment from standing-position CXRs. Exclusion criteria included systematic reviews, meta-analyses, conference abstracts, paediatric studies, non-original articles, and studies using imaging techniques other than X-rays. After initial screening, 117 articles were reviewed, with 14 studies meeting the final inclusion criteria. Data extraction was performed by three independent investigators, and quality assessment followed PRISMA 2020 guidelines, using tools such as the JBI Checklist, AMSTAR 2, and CASP Diagnostic Study Checklist. Risk of bias was assessed according to the Cochrane Handbook guidelines. Results: Fourteen studies, comprising a total of 70,472 CXR images, met the inclusion criteria. Various AI models were evaluated, with differences in dataset characteristics and AI technology used. Common preprocessing techniques included resizing and normalization. The pooled AUC for cardiomegaly detection was 0.959 (95% CI 0.944-0.975). The pooled standardized mean difference for CTR measurement was 0.0353 (95% CI 0.147-0.0760). Significant heterogeneity was found between studies (I2 89.97%, p < 0.0001), with no publication bias detected. Conclusions: Standardizing methodologies is crucial to avoid interpretational errors and advance AI in medical imaging diagnostics. Uniform reporting standards are essential for the further development of AI in CTR measurement and broader medical imaging applications.

Deep Learning in Cardiothoracic Ratio Calculation and Cardiomegaly Detection.

Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role.

MRI Reference Ranges for Fetal Cardiothoracic Ratio of Diameter, Area, and Circumference from 21 to 38 Weeks Gestational Age.

BACKGROUND: Whether fetal cardiothoracic ratio (CTR) is constant or increasing with gestational age (GA) is controversial. The majority of the fetal CTR data has been obtained through ultrasound. PURPOSE: To retrospectively analyze CTR of diameter, area, and circumference on prenatal MR images in a low-risk population of singleton pregnancies, and to clarify its diagnostic value. STUDY TYPE: Retrospective. SUBJECTS: 1024 low-risk singleton pregnancies undergoing MRI. FIELD STRENGTH: Balanced steady state free precession sequence and single shot-fast spin echo sequence at 1.5 Tesla. ASSESSMENT: Pregnancy clinical data were recorded and diameter, area, and circumference of the fetal heart and thorax were measured by two researchers with 6 and 7 years of radiology experience, respectively, and their variation with GA was investigated. The relationship between CTRs with GA was also investigated. Finally, the value of CTR in the diagnosis of fetuses with abnormal development was explored by using receiver operating characteristic (ROC) curves. STATISTICAL TESTS: Linear regression and ROC curves. A P value <0.05 was considered significant. RESULTS: There were significant positive linear correlations (R2 > 0.7, P < 0.0001) between the diameter, area, and circumference of the heart and thorax with GA. The CTRs remain constant values and do not change with GA. The 5th, 50th, and 95th percentiles of the CTR in 21-38 weeks GA were 0.32, 0.39, and 0.48 respectively. The corresponding percentiles for the area ratio were 0.15, 0.21 and 0.27, respectively, and for the circumference ratio were 0.40, 0.46, and 0.52, respectively. Based on ROC curves of CTR with three methods, the area under curves (AUCs) were up to 0.95, the sensitivity and the specificity were more than 88%. DATA CONCLUSION: Reference ranges of fetal CTR were established using MRI, which remain constant. These may be helpful in making a definitive diagnosis in fetuses with abnormal development. TECHNICAL EFFICACY: Stage 2.

Adjustment of scan delay for bolus tracking with cardiothoracic ratio of CT scout image for hepatic artery phase of hepatic dynamic CT.

This study aimed to determine the scan delay for bolus tracking in the hepatic artery phase (HAP) of hepatic dynamic computed tomography (CT) using the cardiothoracic ratio (CTR) from CT scout images. We retrospectively studied 188 patients who underwent hepatic dynamic CT, 24 of whom had scan delays adjusted for CTR. The contrast enhancement of the abdominal aorta, portal vein, hepatic vein, and hepatic parenchyma was calculated for HAP. The adequacy of the scan timing for HAP was assessed using three classifications: early, appropriate, or late. The effect of HAP on scan timing adequacy was determined using multivariate logistic regression analysis, and the optimal cutoff value of CTR was evaluated using receiver operating characteristic analysis. The trigger times for bolus tracking (odds ratio: 1.58) and CTR (odds ratio: 1.23) were significantly affected by the appropriate scan timing of the HAP. The optimal cutoff value of CTR was 59.3%. The scan timing of HAP with a scan delay of 15 s was 14% of early and 86% of appropriate, and the proportion of early in CTR ≥ 60% (early, 52%; appropriate, 48%) was higher than that in CTR < 60% (early, 6%; appropriate, 94%). Adjusting the scan delay to 20 s in CTR ≥ 60% increased the proportion of appropriate (early, 4%; appropriate, 96%). The CTR of a CT scout image is an effective index for determining the scan delay for bolus tracking. Adjusting the scan delay by CTR can provide appropriate HAP images in more patients. Trial registration number: R-080; date of registration: 9 March 2023, retrospectively registered.

Correlations of sST2 and Gal-3 with Cardiothoracic Ratio in Patients with Chronic Kidney Disease.

Chronic kidney disease (CKD) frequently correlates with cardiovascular complications. Soluble suppression of tumorigenicity 2 (sST2) and Galectin-3 (Gal-3) are emerging as cardiac markers with potential relevance in cardiovascular risk prediction. The cardiothoracic ratio (CTR), a metric easily obtainable from chest radiographs, has traditionally been used to assess cardiac size and the potential for cardiomegaly. Understanding the correlation between these cardiac markers and the cardiothoracic ratio (CTR) could provide valuable insights into the cardiovascular prognosis of CKD patients. This study aimed to explore the relationship between sST2, Gal-3, and the CTR in individuals with CKD. Plasma concentrations of sST2 and Gal-3 were assessed in a cohort of 123 CKD patients by enzyme-linked immunosorbent assay (ELISA). On a posterior-to-anterior chest X-ray view, the CTR was determined by comparing the widths of the heart to that of the thorax. The mean concentration of sST2 in the study participants ranged from 775.4 to 4475.6 pg/mL, and the mean concentration of Gal-3 ranged from 4.7 to 9796.0 ng/mL. Significant positive correlations were observed between sST2 and the CTR (r = 0.291, p < 0.001) and between Gal-3 and the CTR (r = 0.230, p < 0.01). Our findings indicate that elevated levels of sST2 and Gal-3 are associated with an increased CTR in CKD patients. This relationship may enable better cardiovascular risk evaluation for CKD patients. Further studies are warranted to explore the clinical implications of these associations.

Comparing three cardiothoracic ratio measurement techniques and creating multivariable scoring system to predict Bart's hydrops fetalis at 17-22 weeks' gestation.

To assess the diagnostic performance of three cardiothoracic (CT) ratio techniques, including diameter, circumference, and area, for predicting hemoglobin (Hb) Bart's disease between 17 and 22 weeks' gestation, and to create a multivariable scoring system using multiple ultrasound markers. Before invasive testing, three CT ratio techniques and other ultrasound markers were obtained in 151 singleton pregnancies at risk of Hb Bart's disease. CT diameter ratio demonstrated the highest sensitivity among the other techniques. Significant predictors included CT diameter ratio > 0.5, middle cerebral artery-peak systolic velocity (MCA-PSV) > 1.5 multiples of the median, and placental thickness > 3 cm. MCA-PSV exhibited the highest sensitivity (97.8%) in predicting affected fetuses. A multivariable scoring achieved excellent sensitivity (100%) and specificity (84.9%) for disease prediction. CT diameter ratio exhibited slightly outperforming the other techniques. Increased MCA-PSV was the most valuable ultrasound marker. Multivariable scoring surpassed single-parameter analysis in predictive capabilities.

Diagnostic value of cardiothoracic ratio in patients with non-ischaemic cardiomyopathy: comparison to cardiovascular magnetic resonance imaging.

PURPOSE: To determine the reliability of the cardiothoracic ratio (CTR) as a simple method to assess the cardiac size and function in patients with non-ischemic cardiomyopathy (NICM). METHODS: In a sample of 91 patients (66 patients with diagnosed non-ischemic cardiomyopathy and 25 controls) we calculated the CTR on a posteroanterior chest radiograph and ventricular and atrial size based on accepted cardiovascular magnetic resonance (CMR) imaging values. Left and right ventricular ejection fraction was also calculated. The CTR and cardiac chamber size were compared between patients with NICM and healthy individuals. The distinction between normal and increased cardiac chamber size was made using published normal CMR reference values stratified by age and gender. RESULTS: CTR values were higher in the NICM group (50.7±5.5 % Vs. 45.3±4.7 %, p<0.001). Likewise, LVEDVi, LV indexed mass, LA indexed volume, LA indexed area, and RA indexed area were higher, and LVEF and RVEF were lower in patients with non-ischemic cardiomyopathy (p < 0.05). In patients with non-ischemic cardiomyopathy, the greatest correlation between CTR and CMR values was with LVEDVi (ρ=0.4, p < 0.001), LA indexed volume (ρ=0.5, p < 0.001), LA indexed area (ρ=0.5, p < 0.001) and RA indexed area (ρ=0.4, p < 0.001). However, the correlation strength was only moderate. CONCLUSION: Despite patients with NICM had higher CTR values than the control group, a substantial proportion of these patients showed normal CTRs (<50 %). This fact limits the usefulness of CTR to reliably predict NICM. Correlation between CTR and heart chamber dilation on CMR was only weak to moderate.

Novel approach to prenatal predictors of outcomes for fetuses with severe Ebstein anomaly.

OBJECTIVE: Ebstein anomaly (EA) is a cardiac malformation with highly variable presentation and severity with limited perinatal management options. We present incorporation of fetal lung measurements into a multidisciplinary evaluation for counseling and predicting postnatal outcomes in patients with severe EA. METHODS: Five fetuses with severe fetal EA were reviewed. Third trimester sonographic observed/expected total lung area (O/E TLA) and lung to head ratio (O/E LHR), fetal MRI total fetal lung volume ratio (O/E-TFLV), echocardiographic cardio-thoracic ratio (CT ratio), sonographic estimated fetal weight (EFW) by Hadlock formula and presence of hydrops, were used to guide perinatal management. RESULTS: Three of five had appropriate fetal growth, were delivered at term in a cardiac operative suite, and underwent immediate intervention with good neonatal outcomes. Two had severe fetal growth restriction (FGR), CT ratios > 0.8 and O/E LHR and TLA < 25%. One of which delivered prematurely with neonatal demise and one suffered in utero demise at 34 weeks. CONCLUSIONS: FGR, hydrops, increased CT ratio and reduced O/E LHR and TFLV are potential prognosticators of poor outcomes in severe EA, and should be validated in larger cohorts that would allow for a statistical analysis of the predictive utility of these measurements.

Pulmonary hypoplasia is associated with severe morbidityThere are limited prognosticating tools to risk stratify and guide management in cases of severe prenatal Ebstein anomaliesFetal MRI may improve prognostication for fetuses with EA.

Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation.

Background: A reproducible and accurate automated approach to measuring cardiothoracic ratio on chest radiographs is warranted. This study aimed to develop a deep learning-based model for estimating the cardiothoracic ratio on chest radiographs without requiring self-annotation and to compare its results with those of manual measurements. Methods: The U-net architecture was designed to segment the right and left lungs and the cardiac shadow, from chest radiographs. The cardiothoracic ratio was then calculated using these labels by a mathematical algorithm. The initial model of deep learning-based cardiothoracic ratio measurement was developed using open-source 247 chest radiographs that had already been annotated. The advanced model was developed using a training dataset of 729 original chest radiographs, the labels of which were generated by the initial model and then screened. The cardiothoracic ratio of the two models was estimated in an independent test set of 120 original cases, and the results were compared to those obtained through manual measurement by four radiologists and the image-reading reports. Results: The means and standard deviations of the cardiothoracic ratio were 52.4% and 9.8% for the initial model, 51.0% and 9.3% for the advanced model, and 49.8% and 9.4% for the total of four manual measurements, respectively. The intraclass correlation coefficients (ICCs) of the cardiothoracic ratio ranged from 0.91 to 0.93 between the advanced model and the manual measurements, whereas those for the initial model and the manual measurements ranged from 0.77 to 0.82. Conclusions: Deep learning-based cardiothoracic ratio estimation on chest radiographs correlated favorably with the results obtained through manual measurements by radiologists. When the model was trained on additional local images generated by the initial model, the correlation with manual measurement improved even more than the initial model alone.

The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs.

OBJECTIVE: Prior studies on models based on deep learning (DL) and measuring the cardiothoracic ratio (CTR) on chest radiographs have lacked rigorous agreement analyses with radiologists or reader tests. We validated the performance of a commercially available DL-based CTR measurement model with various thoracic pathologies, and performed agreement analyses with thoracic radiologists and reader tests using a probabilistic-based reference. MATERIALS AND METHODS: This study included 160 posteroanterior view chest radiographs (no lung or pleural abnormalities, pneumothorax, pleural effusion, consolidation, and n = 40 in each category) to externally test a DL-based CTR measurement model. To assess the agreement between the model and experts, intraclass or interclass correlation coefficients (ICCs) were compared between the model and two thoracic radiologists. In the reader tests with a probabilistic-based reference standard (Dawid-Skene consensus), we compared diagnostic measures-including sensitivity and negative predictive value (NPV)-for cardiomegaly between the model and five other radiologists using the non-inferiority test. RESULTS: For the 160 chest radiographs, the model measured a median CTR of 0.521 (interquartile range, 0.446-0.59) and a mean CTR of 0.522 ± 0.095. The ICC between the two thoracic radiologists and between the model and two thoracic radiologists was not significantly different (0.972 versus 0.959, p = 0.192), even across various pathologies (all p-values > 0.05). The model showed non-inferior diagnostic performance, including sensitivity (96.3% versus 97.8%) and NPV (95.6% versus 97.4%) (p < 0.001 in both), compared with the radiologists for all 160 chest radiographs. However, it showed inferior sensitivity in chest radiographs with consolidation (95.5% versus 99.9%; p = 0.082) and NPV in chest radiographs with pleural effusion (92.9% versus 94.6%; p = 0.079) and consolidation (94.1% versus 98.7%; p = 0.173). CONCLUSION: While the sensitivity and NPV of this model for diagnosing cardiomegaly in chest radiographs with consolidation or pleural effusion were not as high as those of the radiologists, it demonstrated good agreement with the thoracic radiologists in measuring the CTR across various pathologies.

Cardiothoracic ratio and left ventricular ejection fraction relationship: A meta-analysis study.

OBJECTIVES: To determine the overall effect size, identify the study with the strongest effect size, and examine the age group with the strongest relationships between the variables. METHODS: In this study, a meta-analytical analysis was carried out by bringing together 13 studies from around the world examining the statistical relationships between cardiothoracic ratio (CTR) and left ventricular ejection fraction (LVEF). Thus, it is hoped that the results will contribute to studies on the relationships between CTR and LVEF and bring a holistic view to these relationships. To determine CTR, studies were identified through a review of the literature, and those that reported a correlation between the variables under investigation were included in the analysis process. The date range of this study 01.11.2022-15.01.2023. RESULTS: According to the findings, when all the results were analysed together, the mean effect size for CTR and LVEF correlation was found to be r=-0.12. When all studies were considered separately, generally small negative correlations were observed between CTR and LVEF. It is possible to say that there is no publication bias in the studies. CONCLUSION: This study is a meta-analytic study combining 13 studies examining the statistical relationships between CTR and LVEF. The results of this study are expected to make a valuable contribution to the field of research on the relationship between CTR and LVEF, providing a more comprehensive understanding of these associations. PROSPERO Reg. No.: 392207.

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients.

Cardiomegaly is associated with poor clinical outcomes and is assessed by routine monitoring of the cardiothoracic ratio (CTR) from chest X-rays (CXRs). Judgment of the margins of the heart and lungs is subjective and may vary between different operators. METHODS: Patients aged > 19 years in our hemodialysis unit from March 2021 to October 2021 were enrolled. The borders of the lungs and heart on CXRs were labeled by two nephrologists as the ground truth (nephrologist-defined mask). We implemented AlbuNet-34, a U-Net variant, to predict the heart and lung margins from CXR images and to automatically calculate the CTRs. RESULTS: The coefficient of determination (R2) obtained using the neural network model was 0.96, compared with an R2 of 0.90 obtained by nurse practitioners. The mean difference between the CTRs calculated by the nurse practitioners and senior nephrologists was 1.52 ± 1.46%, and that between the neural network model and the nephrologists was 0.83 ± 0.87% (p < 0.001). The mean CTR calculation duration was 85 s using the manual method and less than 2 s using the automated method (p < 0.001). CONCLUSIONS: Our study confirmed the validity of automated CTR calculations. By achieving high accuracy and saving time, our model can be implemented in clinical practice.

Extubation failure after cardiac surgery in children with Down syndrome.

Extubation failure (EF) after cardiac surgery is associated with poorer outcomes. Approximately 50% of children with Down syndrome (DS) have congenital heart disease. Our primary aim was to describe the frequency of EF and identify risk factors for its occurrence in a population of patients with DS after cardiac surgery. Secondary aims were to describe complications, length of hospital stay, and mortality rates. This report was a retrospective case-control study and was carried out in a national reference congenital heart disease repair center of Chile. This study includes all infants 0-12 months old with DS who were admitted to pediatric intensive care unit after cardiac surgery between January 2010 and November 2020. Patients with EF (cases) were matched 1:1 with children who did not fail their extubation (controls) using the following criteria: age at surgery, sex, and type of congenital heart disease. Overall, 27/226 (11.3%) failed their first extubation. In the first analysis, before matching of cases and controls was made, we found association between EF and younger age (3.8 months vs 5 months; p = 0.003) and presence of coarctation of the aorta (p = 0.005). In the case-control univariate analysis, we found association between an increased cardiothoracic ratio (CTR) (p = 0.03; OR 5 (95% CI 1.6-16.7) for a CTR > 0.59) and marked hypotonia (27% vs 0%; p = 0.01) with the risk of EF. No differences were found in ventilatory management. CONCLUSIONS: In pediatric patients with DS, EF after cardiac surgery is associated with younger age, presence of aortic coarctation, higher CTR reflecting the degree of cardiomegaly and hypotonia. Recognition of these factors may be helpful when planning extubation for these patients. WHAT IS KNOWN: • Extubation failure after cardiac surgery is associated with higher morbidity and mortality rates. Some studies report higher rates of extubation failure in patients with Down syndrome. WHAT IS NEW: • In children with Down syndrome, extubation failure after cardiac surgery is associated with younger age, presence of aortic coarctation, higher CTR reflecting cardiomegaly and severe hypotonia.

A study on computed tomography cardiothoracic ratio in predicting left ventricular systolic dysfunction.

OBJECTIVE: A cardiothoracic ratio ≥0.50 is widely used as an indicator of cardiomegaly, but associations between the cardiothoracic ratio and left ventricular systolic dysfunction (LVSD) have not been investigated previously. We conducted this study to investigate the relationship between cardiothoracic ratio measured using computed tomography (CT) and left ventricular ejection fraction (LVEF), and to determine the optimal cardiothoracic ratio for predicting left ventricular systolic dysfunction (LVSD). METHODS: A retrospective cross-sectional study was performed using data from patients who underwent both chest CT and echocardiography at the emergency department from January 1 to December 31, 2021. The patients were classified as normal, or having mild, moderate, and severe LVSD based on their LVEF, and the cardiothoracic ratios of each group were compared. The receiver operating characteristic (ROC) curve analyses were used to identify the optimal cardiothoracic ratio for prediction of mild, moderate, and severe LVSD. RESULTS: The final study population included 444 patients. The median CT-measured cardiothoracic ratio was 0.54 for patients with normal LVEF, and 0.60 for patients with LVSD (P<0.001). The optimal CT-measured cardiothoracic ratios for predicting mild, moderate, and severe LVSD were 0.56, 0.59, and 0.60, and their areas under the ROC curve were 0.653, 0.690, and 0.680, and negative predictive values were 90%, 94%, and 98%, respectively. CONCLUSION: The best cutoff value for a CT-measured cardiothoracic ratio suggestive of LVSD was 0.56, which is very different from the 0.50 value typically considered an abnormal cardiothoracic ratio. The CT-measured cardiothoracic ratio ≥0.56 can be used as a rough indicator of mild LVSD, and a ratio <0.60 can exclude severe LVSD with a high degree of confidence.

A morphological study comparing heart size measurement on chest radiograph with echocardiography in local population.

OBJECTIVE: To investigate heart size on chest X-ray via cardiothoracic ratio, and to correlate it with echocardiographic measurements. METHODS: The comparative analytical, cross-sectional study was conducted at the Pakistan Navy Station Shifa Hospital, Karachi, between January 2021 and July 2021. The radiological parameters were measured on chest X-rays posterior-anterior view, and the echocardiographic parameters were measured using 2-dimensional transthoracic echocardiography. The absence or presence of cardiomegaly on both imaging modalities was modelled as a binary categorical variable and compared. Data was analysed using SPSS 23. RESULTS: Of the 79 participants, 44(55.7%) were males and 35(44.3%) were females. The mean age of the sample was 52.71±14.54 years. There were 28(35.44%) enlarged hearts on chest X-ray and 46(58.22%) on echocardiography. The sensitivity and specificity of chest X-ray were 54.35% and 90.90%, respectively. The positive and negative predictive values were 89.28% and 58.82%, respectively. The accuracy of chest X-ray in identifying an enlarged heart was 69.62%. CONCLUSIONS: The cardiac silhouette on a chest X-ray could demonstrate heart size through simple measurements with high specificity and reasonable accuracy. However, a normal heart size on chest X-ray may not have a normal function.

Individualized estimates of intensity-modulated radiotherapy plans after breast conservation surgery for left-sided breast cancer.

OBJECTIVES: The purpose of this study was to explore the influence of individual patient factors, such as volume of the planning target volume (PTV) (VPTV), cardiothoracic ratio (CTR), central lung distance (CLD), and maximal heart distance (MHD), on the design of treatment plans in terms of target dose coverage, integral dose, and dose to organs at risk (OAR) in early breast cancer. METHODS: Ninety-six patients were selected for this study. Radiation doses of 50 Gy and a simultaneous dose of 60 Gy in 25 fractions were administered to the whole breast and tumor bed, respectively. The intensity modulation plan (IMRT) of each patient uses both physical parameters and an equivalent uniform dose (EUD) to optimize the target function. Univariate and multivariate linear regression were used to analyze the relationship between predictive impact factors and OAR percent dose volume, conformity index (CI), and homogeneity index (HI). RESULTS: The average CI and HI values of the left breast cancer plan were 0.595 ± 0.071 (0.3-0.72) and 1.095 ± 0.023 (1.06-1.18), respectively. The CTR (B = 0.21, P = 0.045), VPTV (B = 0.63, P = 0.000), volume of the lung (Vlung) (B = - 0.29, P = 0.005), and MHD (B = 0.22, P = 0.041) were identified as factors influencing the CI index of the left breast cancer intensity modulation plan. VPTV (B = 1.087, P = 0.022) was identified as the influencing factor of the HI index of the left breast cancer intensity modulation plan. volume of the heart (Vheart) (B = - 0.43, P = 0.001) and CLD (B = 0.28, P = 0.008) were influencing factors of the volume of lung (Vlung20) of the lung. The prediction formulas for left-sided breast cancer are noted as follows: CI = 0.459 + 0.19CTR-0.16CLD, Vlung10 = 35.5-0.02Vheart; and Vlung20 = 21.48 + 2.8CLD-0.018Vheart. CONCLUSIONS: CTR, CLD, and MHD can predict the rationality of the parameters of the left breast cancer IMRT. The calculation formula generated based on this information can help the physicist choose the optimal radiation field setting method and improve the quality of the treatment plan.

High Ultrafiltration Rate Is Associated with Increased All-Cause Mortality in Incident Hemodialysis Patients with a High Cardiothoracic Ratio.

A high ultrafiltration rate (UFR) is associated with increased mortality in hemodialysis patients. However, whether a high UFR itself or heart failure with fluid overload followed by a high UFR causes mortality remains unknown. In this study, 2615 incident hemodialysis patients were categorized according to their initial cardiothoracic ratios (CTRs) to assess whether UFR was associated with mortality in patients with high or low CTRs. In total, 1317 patients (50.4%) were women and 1261 (48.2%) were diabetic. During 2246 (1087−3596) days of follow-up, 1247 (47.7%) cases of all-cause mortality were noted. UFR quintiles 4 and 5 were associated with higher risks of all-cause mortality than UFR quintile 2 in fully adjusted Cox regression analysis. As the UFR increased by 1 mL/kg/h, the risk of all-cause mortality increased 1.6%. Subgroup analysis revealed that in UFR quintile 5, hazard ratios (HRs) for all-cause mortality were 1.91, 1.48, 1.22, and 1.10 for CTRs of >55%, 50−55%, 45−50%, and <45%, respectively. HRs for all-cause mortality were higher in women and patients with high body weight. Thus, high UFRs may be associated with increased all-cause mortality in incident hemodialysis patients with a high CTR, but not in those with a low CTR.