Is there any diagnostic value of anteroposterior chest radiography in predicting cardiac chamber enlargement?

Development of a radiopaque tiltmeter to improve reproducibility for Fowler's position on chest radiography

INTRODUCTION

We have developed a novel radiopaque tiltmeter (ROT) that can indicate patient tilt during a radiography examination and display it on X-ray images. This study evaluated the effect of variation of patient tilt on the reproducibility of Fowler's position for chest radiography and the accuracy of the ROT.

METHODS

We evaluated the reproducibility of Fowler's position based on changes from the first day in the central venous catheter (CVC) tip position and the cardiothoracic ratio (CTR) with and without a digital tiltmeter to verify its efficacy in patients who underwent mobile chest radiography. The ROT contains radiopaque liquid consisting of white barium sulfate solution and oil and has a scale bar of 15°-75° with increments of 15° to indicate ROT tilt. The ROT tilt was increased from 10° to 80° in increments of 10°. We then evaluated (1) the difference between the ROT tilt and the tilt measured with a digital tiltmeter, and (2) the ROT tilt displayed on the X-ray image.

RESULTS

With regard to reproducibility in Fowler's position, changes in the CVC tip position were 2.8 ± 3.9 mm and 10.7 ± 10.6 mm with and without the tiltmeter, respectively (p < 0.05) and the respective rates of change in the CTR were 0.7% ± 0.6% and 4.0% ± 2.1% (p < 0.05). Differences between the ROT tilt and the tilt measured by the digital tiltmeter were within ±2.5°. All ROT tilts displayed on the X-ray images were recognized exactly as each tilt.

CONCLUSION

Our novel ROT had the potential to accurately indicate patient tilt during chest radiography, which could be helpful in terms of reproducibility and precise follow-up.

IMPLICATIONS FOR PRACTICE

Use of the ROT for determination of patient tilt can improve reproducibility in Fowler's position, allowing more accurate serial X-ray imaging.

Radiological Cardiothoracic Ratio as a Potential Predictor of Right Ventricular Enlargement in Patients with Suspected Pulmonary Embolism Due to COVID-19

The aim of the study was to determine the usefulness of the radiological cardiothoracic ratio (CTR) as a predictor of right ventricular enlargement in patients with suspected pulmonary embolism during COVID-19. The study group consisted of 61 patients with confirmed COVID-19, suspected of pulmonary embolism based on physical examination and laboratory tests (age: 67.18 ± 12.47 years). Computed tomography angiography (CTA) of pulmonary arteries and chest radiograph in AP projection with cardiothoracic ratio assessment were performed in all patients. Right ventricular enlargement was diagnosed by the ratio of right ventricular to left ventricular (RV/LV) dimensions in pulmonary CTA with two cut-off points: ≥0.9 and ≥1.0. Heart silhouette enlargement was found when CTR on the chest radiograph in the projection AP > 0.55. The mean values of RV/LV and CTR in the studied group were 0.96 ± 0.23 and 0.57 ± 0.05, respectively. Pulmonary embolism was diagnosed in 45.9%. Right ventricular enlargement was documented in 44.3% or 29.5% depending on the adopted criterion RV/LV ≥ 0.9 or RV/LV ≥ 1.0. Heart silhouette enlargement was found in 60.6%. Patients with confirmed pulmonary embolism (PE+) had a significantly higher RV/LV ratio and CTR than patients with excluded pulmonary embolism (PE−) (RV/LV: PE+ 1.08 ± 0.24, PE− 0.82 ± 0.12; CTR: PE+ 0.60 ± 0.05, PE− 0.54 ± 0.04; p < 0.05). The correlation analysis showed a statistically significant positive correlation between the RV/LV ratio and CTR (r = 0.59, p < 0.05). Based on the ROC curves, CTR values were determined as the optimal cut-off points for the prediction of right ventricular enlargement (RV/LV ≥ 0.9 or RV/LV ≥ 1.0), being 0.54 and 0.55, respectively. The sensitivity, specificity, and accuracy of the CTR criterion >0.54 as a predictor of RV/LV ratio ≥0.9 were 0.412, 0.963, and 0.656, respectively, while those of the CTR criterion >0.55 as a predictor of RV/LV ratio ≥1.0 were 0.488, 0.833, and 0.590, respectively. In summary, in patients with suspected pulmonary embolism during COVID-19, the radiographic cardiothoracic ratio can be considered as a prognostic factor for right ventricular enlargement, especially as a negative predictor of right ventricular enlargement in the case of lower CTR values.

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

OBJECTIVE

To develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE), on chest radiographs METHODS: For training and internal validation of DLCE-LAE and -LVE, 5,045 chest radiographs (CRs; 2,463 normal and 2,393 LAE) and 1,012 CRs (456 normal and 456 LVE) matched with the same-day echocardiography were collected, respectively. External validation was performed using 107 temporally independent CRs. Reader performance test was conducted using the external validation dataset by five cardiothoracic radiologists without and with the results of DLCE. Classification performance of DLCE was evaluated and compared with those of the readers and conventional radiographic features, including cardiothoracic ratio, carinal angle, and double contour. In addition, DLCE-LAE was tested on 5,277 CRs from a healthcare screening program cohort.

RESULTS

DLCE-LAE showed areas under the receiver operating characteristics curve (AUROCs) of 0.858 on external validation. On reader performance test, DLCE-LAE showed better results than pooled radiologists (AUROC 0.858 vs. 0.651; p < .001) and significantly increased their performance when used as a second reader (AUROC 0.651 vs. 0.722; p < .001). DLCE-LAE also showed a significantly higher AUROC than conventional radiographic findings (AUROC 0.858 vs. 0.535-0.706; all ps < .01). In the healthcare screening cohort, DLCE-LAE successfully detected 71.0% (142/200) CRs with moderate-to-severe LAE (93.5% [29/31] of severe cases), while yielding 11.8% (492/4,184) false-positive rate. DLCE-LVE showed AUROCs of 0.966 and 0.594 on internal and external validation, respectively.

CONCLUSION

DLCE-LAE outperformed and improved cardiothoracic radiologists' performance in detecting LAE and showed promise in screening individuals with moderate-to-severe LAE in a healthcare screening cohort.

KEY POINTS

• Our deep learning algorithm outperformed cardiothoracic radiologists in detecting left atrial enlargement on chest radiographs. • Cardiothoracic radiologists improved their performance in detecting left atrial enlargement when aided by the algorithm. • On a healthcare-screening cohort, our algorithm detected 71.0% (142/200) radiographs with moderate-to-severe left atrial enlargement while yielding 11.8% (492/4,184) false-positive rate.

A novel method of donor‒recipient size matching in pediatric heart transplantation: A total cardiac volume‒predictive model

BACKGROUND

The pediatric heart transplant community uses weight-based donor-to-recipient size matching almost exclusively, despite no evidence to validate weight as a reliable surrogate of cardiac size. Donor size mismatch is the second most common reason for the refusal of donor hearts in current practice (∼30% of all refusals). Whereas case-by-case segmentation of total cardiac volume (TCV) by computed tomography (CT) for direct virtual transplantation is an attractive option, it remains limited by the unavailability of donor chest CT. We sought to establish a predictive model for donor TCV on the basis of anthropomorphic and chest X-ray (CXR) cardiac measures.

METHODS

Banked imaging studies from 141 subjects with normal CT chest angiograms were obtained and segmented using 3-dimensional modeling to derive TCV. CXR data were available for 62 of those subjects. A total of 3 predictive models of TCV were fit through multiple linear regression using the following variables: Model A (weight only); Model B (weight, height, sex, and age); Model C (weight, height, sex, age, and 1-view anteroposterior CXR maximal horizontal cardiac width).

RESULTS

Model C provided the most accurate prediction of TCV (optimism corrected R2 = 0.99, testing set R2 = 0.98, mean absolute percentage error [MAPE] = 8.6%) and outperformed Model A (optimism corrected R2 = 0.94, testing set R2 = 0.94, MAPE = 16.1%) and Model B (optimism corrected R2 = 0.97, testing set R2 = 0.97, MAPE = 11.1%).

CONCLUSIONS

TCV can be predicted accurately using readily available anthropometrics and a 1-view CXR from donor candidates. This simple and scalable method of TCV estimation may provide a reliable and consistent method to improve donor size matching.