Chest Radiologists Research Articles

Opportunistic Identification of Coronary Artery Calcium and Valve/Vascular Calcifications on Chest X-Ray: Improvements With Single-Exposure Dual-Energy Imaging.

Purpose: To evaluate whether single-exposure, dual-energy chest X-ray (DEX) improves visualization of coronary artery calcium (CAC) and valve/vascular calcifications compared to conventional X-ray. Materials and Methods: Sixty-one bone-marrow transplant patients (22- 79 years; median 61; IQR 15; w/m, 24/37), underwent single-exposure dual-energy X-ray (Reveal 35C, KA imaging) in pa and lateral projection, followed by a standard-of-care chest CT. Two DEX pairs (pa/lateral) were calculated: a composite image (COMP) and a bone image with soft-tissue subtraction (BI). The COMP pair was reviewed by 2 chest radiologists, assessing the presence/absence of CAC and valve/vascular calcifications on a confidence scale from -2 (confidently not present) to 2 (confidently present). Subsequently, the BI pair was revealed, and readers reevaluated both pairs (COMP and BI) jointly using the identical scale. CTCAC scores were categorized according to the CAC-DRS (0-3) and served as standard of reference, valve/vascular calcifications were categorized on CT as present or absent. Results: For detecting CAC on DEX in any CAC-DRS category (1-3), in category 2-3, in category 3, and for valve/vascular calcifications, the ROC-AUC (combined for both readers) for COMP images was 0.74 (CI: 0.64-0.84), 0.81 (CI: 0.68-0.94), 0.84 (CI: 0.69-0.98), and 0.90 (CI: 0.83-0.99), and for the BI images 0.91 (CI: 0.83-0.98), 0.94 (CI: 0.86- 1.00), 0.89 (CI: 0.77-1.00), and 0.98 (CI: 0.96-1.00), with P = .0003, P = .044, P = .42, and P = .55, respectively. The Intraclass-Correlation-Coefficient (ICC) for CAC on COMP/BI was 0.973/0.954, and for valve/vascular calcifications 0.971/0.965. Conclusion: Single-exposure, dual-energy acquisition improves diagnostic confidence for coronary artery calcium and valve/vascular calcification identification on chest X-rays.

Comparison of different iterative reconstruction algorithms with contrast-enhancement boost technique on the image quality of CT pulmonary angiography for obese patients

ObjectiveTo evaluate the effect of the contrast-enhancement-boost (CE-boost) postprocessing technique on improving the image quality of obese patients in computed tomography pulmonary angiography (CTPA) compared to hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR) algorithms.MethodsThis prospective study was conducted on 100 patients who underwent CTPA for suspected pulmonary embolism. Non-obese patients with a body mass index (BMI) under 25 were designated as group 1, while obese patients (group 2) had a BMI exceeding 25. The CE-boost images were generated by subtracting non-contrast HIR images from contrast-enhanced HIR images to improve the visibility of pulmonary arteries further. The CT value, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantitatively assessed. Two chest radiologists independently reviewed the CT images (5, best; 1, worst) across three subjective characteristics including diagnostic confidence, subjective image noise, and vascular contrast. The Friedman test and Dunn-Bonferroni correction were used for statistical analysis.ResultsThe CE-boost had significantly higher CT values than HIR and MBIR in both groups (all p < 0.001). The MBIR yielded the lowest image noise compared with HIR and CE-boost (all p < 0.001). The SNR and CNR of main pulmonary artery (MPA) were significantly higher in CE-boost than in MBIR (all p < 0.05), with HIR showing the lowest values (all p < 0.001). Group 2 MBIR received significantly better subjective image noise scores, while the diagnostic confidence and vascular contrast scored highest with the group 2 CE-boost (all p < 0.05).ConclusionCompared to the HIR algorithm, both the CE-boost technique and the MBIR algorithm can improve the image quality of CTPA in obese patients. CE-boost had the greatest potential in increasing the visualization of pulmonary artery and its branches.

Comparison of Endoscopic Ultrasound and CT Scan in the Diagnosis of Esophageal Duplication Cysts.

Esophageal duplication cysts (EDCs) are rare congenital malformations, often discovered incidentally during endoscopy or on computed tomography (CT) scans. The role of endoscopic ultrasound (EUS) and CT scan in the diagnosis of these lesions and indications for surgical treatment are underreported. The aim of this study was to investigate these topics in a cohort of patients. Between January 2001 and October 2020, 82 patients had a suspicion of esophageal duplication cyst on endoscopic ultrasound. Thirty four of these patients were referred for surgical enucleation of the lesion, but three patients were lost to follow-up. At the end, 31 patients, who underwent surgical treatment for their suspected EDC were included in this study. Clinical features, EUSfindings, CT images, surgical treatment, and outcome were collected from hospital health records. CT images were re-evaluated by a chest radiologist. Type of surgery, surgical complications, and final histological diagnosis were reported. The patients referred for surgery were younger (p = 0.0001) and had larger lesions (> 2cm; p = 0.005) than the patients who had non-operative follow-up. From thirty-one operated patients, eighteen (58%) had post-operative histological diagnosis of duplication cyst. On EUS the final histological diagnosis was correct in 58% (18/31) of all the operated cases and on CT scan 57% (17/30). CT scan misdiagnosed three of the EDCs but found two leiomyomas correctly. None of these patients developed malignancy. According to this study, neither EUS without fine-needle biopsy nor CT scan alone can differentiate EDCs from other mediastinal masses.

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

Variability in chest radiology interpretation between thoracic and non-thoracic radiologists: Implications for pulmonary fibrosis care

Variability in chest radiology interpretation between thoracic and non-thoracic radiologists: Implications for pulmonary fibrosis care

CLASSIFICATION OF X-RAY AND CT IMAGES IN DIFFERENT COLOR SPACES USING ROBUST CNN

Since deep learning models have been successfully used in many fields, they have been used to identify sick and healthy people in X-ray or Computed Tomography (CT) chest radiology images. In this study, Covid-19 and pneumonia classification is performed on both X-ray and CT images using the robust Convolutional Neural Network (CNN). BGR, HSV, and CIE LAB color space transformations are applied to X-ray and CT images to show that the model performs a successful classification independent of dataset characteristics. The binary classification accuracy rates of Covid-19 and pneumonia for X-ray images and CT images are 98.7% and 98.4%, 97.6% and 99.4%, respectively. Precision, Recall, Specificity, F1 score, and Mean Squared Error metrics are calculated for each X-ray and CT dataset. In addition, 5-fold cross-validation proved accuracy of the model. Although X-ray and CT chest radiology images are transformed into different color spaces, the proposed model performed a successful classification. Thus, even if the image characteristics of the radiology device brands change, the computer-based system will be able to make successful disease diagnoses at low cost where expert personnel are insufficient.

Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

Inter-observer consistency on subsolid nodule follow-up recommendation based on National Comprehensive Cancer Network (NCCN) guidelines in low-dose computed tomography (LDCT) lung cancer screening.

Follow-up management of pulmonary nodules is a crucial component of lung cancer screening. Consistency in follow-up recommendations is essential for effective lung cancer screening. This study aimed to assess inter-observer agreement on National Comprehensive Cancer Network (NCCN) guideline-based follow-up recommendation for subsolid nodules from low-dose computed tomography (LDCT) screening. A retrospective collection of LDCT reports from 2014 to 2017 for lung cancer screening was conducted using the Radiology Information System and keyword searches, focusing on subsolid nodules. A total of 110 LDCT cases containing subsolid nodules were identified. Two senior radiologists provided standardized follow-up recommendation. Follow-up recommendation was categorized into four groups (0-, 3-, 6-, and 12-month). To ensure overall balance and representativeness of the follow-up categories, 60 scans from 60 participants were included (distribution ratio 1:1:2:2). Cases were categorised into follow-up recommendation groups by five observers following NCCN guidelines. Fleiss' kappa statistic was used to evaluate inter-observer agreement. Overall accuracy rate for follow-up recommendation among five observers was 72.3%. Chest radiologists' overall agreement was significantly higher than radiology residents (P<0.01). The overall agreement among the five observers was moderate, with a Fleiss' kappa of 0.437. For all paired readers, the mean Cohen's kappa value was 0.603, with 95% confidence interval (CI) from 0.489 to 0.716. Chest radiologists demonstrated substantial agreement, evidenced by a Cohen's kappa of 0.655 (95% CI: 0.503-0.807). In contrast, the mean Cohen's kappa among radiology residents was 0.533 (95% CI: 0.501-0.565). The majority of cases with discrepancies, accounting for 73.5%, were associated with the same risk-dominant nodules. A higher proportion of part-solid nodule was a risk factor for discrepancies. Of the 600 paired readings, major discrepancies and substantial discrepancies were observed in 27.5% and 4.8% (29/600) of the cases. In subsolid nodules, category evaluation of observer follow-up recommendation based on NCCN guidelines achieved moderate consistency. Disagreements were mainly caused by measurement and type disagreements of identical risk-dominant nodules. Part-solid nodule was a contributor for discrepancies in follow-up recommendation. Major and substantial management discrepancies were 27.5% and 4.8% in the paired evaluations.

A deep convolutional neural network approach using medical image classification

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfected population. In this paper, an automatic COVID-19 detection model using respiratory sound and medical image based on internet of health things (IoHT) is proposed. In this model, primarily to screen those people having suspected Coronavirus disease, the sound of coughing used to detect healthy people and those suffering from COVID-19, which finally obtained an accuracy of 94.999%. This approach not only expedites diagnosis and enhances accuracy but also facilitates swift screening in public places using simple equipment. Then, in the second step, in order to help radiologists to interpret medical images as best as possible, we use three pre-trained convolutional neural network models InceptionResNetV2, InceptionV3 and EfficientNetB4 and two data sets of chest radiology medical images, and CT Scan in a three-class classification. Utilizing transfer learning and pre-existing knowledge in these models leads to notable improvements in disease diagnosis and identification compared to traditional techniques. Finally, the best result obtained for CT-Scan images belonging to InceptionResNetV2 architecture with 99.414% accuracy and for radiology images related to InceptionV3 and EfficientNetB4 architectures with the accuracy is 96.943%. Therefore, the proposed model can help radiology specialists to confirm the initial assessments of the COVID-19 disease.

Examination of Firefighting as an Occupational Exposure Criteria for Lung Cancer Screening.

Firefighting is known to be carcinogenic to humans. However, current lung cancer screening guidelines do not account for occupational exposure. We hypothesize that firefighting is an independent risk factor associated with the development of high-risk lung nodules on low-dose CT (LDCT). Members of a firefighter's union underwent LDCT at a single institution between April 2022 and June 2023 within a lung cancer screening program. Results were interpreted by designated chest radiologists and reported using the Lung-RADS scoring system. Demographic and radiographic data were recorded, and summary statistics are reported. 1347 individuals underwent lung cancer screening, with a median age of 51years (IQR 42-58), including 56 (4.2%) females. Overall, 899 (66.7%) were never smokers, 345 (25.6%) were former smokers, and 103 (7.7%) were current smokers. There were 41 firefighters (3.0%) who had high-risk (Lung-RADS 3 or 4) nodules requiring intervention or surveillance, of which 21 (1.5%) were Lung-RADS 3 and 20 (1.5%) that were Lung-RADS 4. Of the firefighters with high-risk nodules, only 6 (14.6%) were eligible for LDCT based on current screening guidelines. There were 7 high-risk nodules (0.5%) that required procedural intervention, 6 (85.7%) of which were from the non-screening eligible cohort. There were also 20 never-smoking firefighters (57.1%) with high-risk nodules that were non-screening eligible. Firefighting, even in the absence of smoking history, may be associated with the development of high-risk lung nodules on LDCT. Carefully selected occupational exposures should be considered in the development of future lung cancer screening guidelines.

Impact of Long-Term Non-Communicable Diseases on SARS-COV-2 Hospitalized Patients Supported by Radiological Imaging in Southern Pakistan.

COVID-19 patients with already existing chronic medical conditions are more likely to develop severe complications and, ultimately, a higher risk of mortality. This study analyzes the impacts of pre-existing chronic illnesses such as diabetes (DM), hypertension, and cardiovascular diseases (CVDs) on COVID-19 cases by using radiological chest imaging. The data of laboratory-confirmed COVID-19-infected hospitalized patients were analyzed from March 2020 to December 2020. Chest X-ray images were included to further identify the differences in X-ray patterns of patients with co-morbid conditions and without any co-morbidity. The Pearson chi-square test checks the significance of the association between co-morbidities and mortality. The magnitude and dimension of the association were calibrated by the odds ratio (OR) at a 95% confidence interval (95% CI) over the patients' status (mortality and discharged cases). A univariate binary logistic regression model was applied to examine the impact of co-morbidities on death cases independently. A multivariate binary logistic regression model was applied for the adjusted effects of possible confounders. For the sensitivity analysis of the model, receiver operating characteristic (ROC) was applied. Patients with different comorbidities, including diabetes (OR = 33.4, 95% CI: 20.31-54.78, p < 0.001), cardiovascular conditions (OR = 24.14, 95% CI: 10.18-57.73, p < 0.001), and hypertension (OR = 16.9, 95% CI: 10.20-27.33,p < 0.001), showed strong and significant associations. The opacities present in various zones of the lungs clearly show that COVID-19 patients with chronic illnesses such as diabetes, hypertension, cardiovascular disease, and obesity experience significantly worse outcomes, as evidenced by chest X-rays showing increased pneumonia and deterioration. Therefore, stringent precautions and a global public health campaign are crucial to reducing mortality in these high-risk groups.

Impact of Simulated Reduced-Dose Chest CT on Diagnosing Pulmonary T1 Tumors and Patient Management.

To determine the diagnostic performance of simulated reduced-dose chest CT scans regarding pulmonary T1 tumors and assess the potential impact on patient management, a repository of 218 patients with histologically proven pulmonary T1 tumors was used. Virtual reduced-dose images were simulated at 25%- and 5%-dose levels. Tumor size, attenuation, and localization were scored by two experienced chest radiologists. The impact on patient management was assessed by comparing hypothetical LungRADS scores. The study included 210 patients (41% females, mean age 64.5 ± 9.2 years) with 250 eligible T1 tumors. There were differences between the original and the 5%-but not the 25%-dose simulations, and LungRADS scores varied between the dose levels with no clear trend. Sensitivity of Reader 1 was significantly lower using the 5%-dose vs. 25%-dose vs. original dose for size categorization (0.80 vs. 0.85 vs. 0.84; p = 0.007) and segmental localization (0.81 vs. 0.86 vs. 0.83; p = 0.018). Sensitivities of Reader 2 were unaffected by a dose reduction. A CT dose reduction may affect the correct categorization and localization of pulmonary T1 tumors and potentially affect patient management.

Unveiling Silent Consequences: Impact of Pulmonary Tuberculosis on Lung Health and Functional Wellbeing after Treatment.

Background: Pulmonary tuberculosis (TB) remains a major public health issue in India, with high incidence and mortality. The current literature on post-TB sequelae functional defects focuses heavily on spirometry, with conflicting obstruction vs. restriction data, lacks advanced statistical analysis, and has insufficient data on diffusion limitation and functional impairment. Objective: This study aimed to thoroughly evaluate post-tubercular sequelae after treatment, assessing chest radiology, spirometry, diffusing capacity, and exercise capacity. Methods: A total of 85 patients were studied at a university teaching hospital in Mysuru. The data collected included characteristics, comorbidities, smoking history, and respiratory symptoms. The investigations included spirometry, DLCO, chest X-rays with scoring, and 6MWT. Results: Of the patients, 70% had abnormal X-rays post-treatment, correlating with reduced lung function. Additionally, 70% had impaired spirometry with obstructive/restrictive patterns, and 62.2% had reduced DLCO, with females at higher risk. Smoking increased the risk of sequelae. Conclusions: Most patients had residual radiological/lung function abnormalities post-treatment. Advanced analyses provide insights into obstructive vs. restrictive defects. Ongoing research should explore pathogenetic mechanisms and therapeutic modalities to minimize long-term post-TB disability.

Next-generation digital chest tomosynthesis.

The objective of this study was to demonstrate the performance characteristics and potential utility of a novel tomosynthesis device as applied to imaging the chest, specifically relating to lung nodules. The imaging characteristics and quality of a novel digital tomosynthesis prototype system was assessed by scanning, a healthy volunteer, and an andromorphic lung phantom with different configurations of simulated pulmonary nodules. The adequacy of nodule detection on the phantoms was rated by chest radiologists using a standardized scale. Results from using this tomosynthesis device demonstrate in plane resolution of 16lp/cm, with estimated effective radiation doses of 90% less than low dose CT. Nodule detection was adequate across various anatomic locations on a phantom. These proof-of-concept tests showed this novel tomosynthesis device can detect lung nodules with low radiation dose to the patient. This technique has potential as an alternative to low dose chest CT for lung nodule screening and tracking.

Association of Ground Glass Opacities with Systemic Inflammation and Progression of Emphysema.

Ground glass opacities (GGO) in the absence of interstitial lung disease are understudied. To assess the association of GGO with white blood cells (WBCs) and progression of quantified chest CT emphysema. We analyzed data of participants in the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS). Chest radiologists and pulmonologists labeled regions of the lung as GGO and adaptive multiple feature method (AMFM) trained the computer to assign those labels to image voxels and quantify the volume of the lung with GGO (%GGOAMFM). We used multivariable linear regression, zero-inflated negative binomial, and proportional hazards regression models to assess the association of %GGOAMFM with WBC, changes in %emphysema, and clinical outcomes. Among 2,714 participants, 1,680 had COPD and 1,034 had normal spirometry. Among COPD participants, based on the multivariable analysis, current smoking and chronic productive cough was associated with higher %GGOAMFM. Higher %GGOAMFM was cross-sectionally associated with higher WBCs and neutrophils levels. Higher %GGOAMFM per interquartile range at visit 1 (baseline) was associated with an increase in emphysema at one-year follow visit by 11.7% (Relative increase; 95%CI 7.5-16.1%;P<0.001). We found no association between %GGOAMFM and one-year FEV1 decline but %GGOAMFM was associated with exacerbations and all-cause mortality during a median follow-up time of 1,544 days (Interquartile Interval=1,118-2,059). Among normal spirometry participants, we found similar results except that %GGOAMFM was associated with progression to COPD at one-year follow-up. Our findings suggest that GGOAMFM is associated with increased systemic inflammation and emphysema progression.

Contrast agent-free functional magnetic resonance imaging with matrix pencil decomposition to quantify abnormalities in lung perfusion and ventilation in patients with cystic fibrosis.

Previous studies showed that contrast-enhanced (CE) morpho-functional magnetic resonance imaging (MRI) detects abnormalities in lung morphology and perfusion in patients with cystic fibrosis (CF). Novel matrix pencil decomposition MRI (MP-MRI) enables quantification of lung perfusion and ventilation without intravenous contrast agent administration. To compare MP-MRI with established morpho-functional MRI and spirometry in patients with CF. Thirty-nine clinically stable patients with CF (mean age 21.6 ± 10.7 years, range 8-45 years) prospectively underwent morpho-functional MRI including CE perfusion MRI, MP-MRI and spirometry. Two blinded chest radiologists assessed morpho-functional MRI and MP-MRI employing the validated chest MRI score. In addition, MP-MRI data were processed by automated software calculating perfusion defect percentage (QDP) and ventilation defect percentage (VDP). MP perfusion score and QDP correlated strongly with the CE perfusion score (both r = 0.81; p < 0.01). MP ventilation score and VDP showed strong inverse correlations with percent predicted FEV1 (r = -0.75 and r = -0.83; p < 0.01). The comparison of visual and automated parameters showed that both MP perfusion score and QDP, and MP ventilation score and VDP were strongly correlated (r = 0.74 and r = 0.78; both p < 0.01). Further, the MP perfusion score and MP ventilation score, as well as QDP and VDP were strongly correlated (r = 0.88 and r = 0.86; both p < 0.01). MP-MRI detects abnormalities in lung perfusion and ventilation in patients with CF without intravenous or inhaled contrast agent application, and correlates strongly with the well-established CE perfusion MRI score and spirometry. Automated analysis of MP-MRI may serve as quantitative noninvasive outcome measure for diagnostic monitoring and clinical trials.

Zoonoses: Chest Radiologist's View.

Zoonoses: Chest Radiologist's View.

Understanding the Added Value of High-Resolution CT Beyond Chest X-Ray in Determining Extent of Physiologic Impairment

BackgroundSarcoidosis staging has primarily relied on the Scadding chest radiographic system, although chest CT is finding increased clinical use. Research QuestionWhether standardized CT assessment provides additional understanding of lung function beyond Scadding stage and demographics is unknown and the focus of this study. Study Design and MethodsWe used the NHLBI study Genomics Research in Alpha-1 Anti-Trypsin Deficiency and Sarcoidosis (GRADS) sarcoidosis cases(N=351) with Scadding stage and Chest CT scans obtained in a standardized manner. One chest radiologist scored all CT scans with a visual scoring system, with a subset read by another chest radiologist. We compared demographic features, Scadding stage and CT findings, and correlation between these measures. Associations between spirometry and DLCO and CT and Scadding stage were determined using regression analysis (N=318). Agreement between readers was evaluated using Cohen’s Kappa. ResultsCT features were inconsistent with Scadding stage in about ∼40% of cases. Most CT features assessed on visual scoring were negatively associated with lung function. Associations persisted for FEV1 and DLCO when adjusting for Scadding stage, although some CT feature associations with FVC became insignificant. Scadding stage was primarily associated with FEV1 and inclusion of CT features reduced significance in association between Scadding and lung function. Multivariable regression modeling to identify radiologic measures explaining lung function included Scadding stage for FEV1 and FEV1/FVC (P<0.05) and marginally for DLCO (P<0.15). Combinations of CT measures accounted for Scadding stage for FVC. Correlations among Scadding and CT features were noted. Agreement between readers was poor to moderate for presence/absence of CT features and poor for degree/location of abnormality. InterpretationCT features explained additional variability in lung function beyond Scadding stage, with some CT features obviating the associations between lung function and Scadding. Whether CT features/phenotypes/endotypes could be useful for managing patients with sarcoidosis needs more study.

Incidence of radiographic and clinically significant pneumothorax or hemothorax after thoracic discectomy via mini-open lateral retropleural approach without prophylactic chest tube placement.

The mini-open lateral retropleural (MO-LRP) approach is an effective option for surgically treating thoracic disc herniations, but the approach raises concerns for pneumothorax (PTX). However, chest tube placement causes insertion site tenderness, necessitates consultation services, increases radiation exposure (requires multiple radiographs), delays the progression of care, and increases narcotic requirements. This study examined the incidence of radiographic and clinically significant PTX and hemothorax (HTX) after the MO-LRP approach, without the placement of a prophylactic chest tube, for thoracic disc herniation. This study was a single-institution retrospective evaluation of consecutive cases from 2017 to 2022. Electronic medical records were reviewed, including postoperative chest radiographs, radiology and operative reports, and postoperative notes. The presence of PTX or HTX was determined on chest radiographs obtained in all patients immediately after surgery, with interval radiographs if either was present. The size was categorized as large (≥ 3 cm) or small (< 3 cm) based on guidelines of the American College of Chest Physicians. PTX or HTX was considered clinically significant if it required intervention. Thirty patients underwent thoracic discectomy via the MO-LRP approach. All patients were included. Twenty patients were men (67%), and 10 (33%) were women. The patients ranged in age from 25 to 74 years. The most commonly treated level was T11-12 (n = 11, 37%). Intraoperative violation of parietal pleura occurred in 5 patients (17%). No patient had prophylactic chest tube placement. Fifteen patients (50%) had PTX on postoperative chest radiographs; 2 patients had large PTXs, and 13 had small PTXs. Both patients with large PTXs had expansion on repeat radiographs and were treated with chest tube insertion. Of the 13 patients with a small PTX, 1 required 100% oxygen using a nonrebreather mask; the remainder were asymptomatic. One patient, who had no abnormal findings on the immediate postoperative chest radiograph, developed an incidental HTX on postoperative day 6 and was treated with chest tube insertion. Thus, 3 patients (10%) required a chest tube: 2 for expanding PTX and 1 for delayed HTX. Most patients who undergo thoracic discectomy via the MO-LRP approach do not develop clinically significant PTX or HTX. PTX and HTX in this patient population should be treated with a chest tube only when there are postoperative clinical and radiographic indications.

Performance of AI to exclude normal chest radiographs to reduce radiologists' workload.

This study investigates the performance of a commercially available artificial intelligence (AI) system to identify normal chest radiographs and its potential to reduce radiologist workload. Retrospective analysis included consecutive chest radiographs from two medical centers between Oct 1, 2016 and Oct 14, 2016. Exclusions comprised follow-up exams within the inclusion period, bedside radiographs, incomplete images, imported radiographs, and pediatric radiographs. Three chest radiologists categorized findings into normal, clinically irrelevant, clinically relevant, urgent, and critical. A commercial AI system processed all radiographs, scoring 10 chest abnormalities on a 0-100 confidence scale. AI system performance was evaluated using the area under the ROC curve (AUC), assessing the detection of normal radiographs. Sensitivity was calculated for the default and a conservative operating point. the detection of negative predictive value (NPV) for urgent and critical findings, as well as the potential workload reduction, was calculated. A total of 2603 radiographs were acquired in 2141 unique patients. Post-exclusion, 1670 radiographs were analyzed. Categories included 479 normal, 332 clinically irrelevant, 339 clinically relevant, 501 urgent, and 19 critical findings. The AI system achieved an AUC of 0.92. Sensitivity for normal radiographs was 92% at default and 53% at the conservative operating point. At the conservative operating point, NPV was 98% for urgent and critical findings, and could result in a 15% workload reduction. A commercially available AI system effectively identifies normal chest radiographs and holds the potential to lessen radiologists' workload by omitting half of the normal exams from reporting. The AI system is able to detect half of all normal chest radiographs at a clinically acceptable operating point, thereby potentially reducing the workload for the radiologists by 15%. The AI system reached an AUC of 0.92 for the detection of normal chest radiographs. Fifty-three percent of normal chest radiographs were identified with a NPV of 98% for urgent findings. AI can reduce the workload of chest radiography reporting by 15%.