Chest Computed Tomography Research Articles

Comparing spirometry, impulse oscillometry with computed tomography for assessing small airway dysfunction in subjects with and without chronic obstructive pulmonary disease

BackgroundStudies on consistency among spirometry, impulse oscillometry (IOS), and histology for detecting small airway dysfunction (SAD) remain scarce. Considering invasiveness of lung histopathology, we aimed to compare spirometry and IOS with chest computed tomography (CT) for SAD detection, and evaluate clinical characteristics of subjects with SAD assessed by these three techniques.MethodsWe collected baseline data from the Early COPD (ECOPD) study. CT-defined SAD was defined as parametric response mapping quantifying SAD (PRMfSAD) ≥ 15%. Spirometry-defined SAD was defined as at least two of maximal mid-expiratory flow (MMEF), forced expiratory flow 50% (FEF50), and forced expiratory flow 75% (FEF75) less than 65% of predicted. IOS-defined SAD was defined as peripheral airway resistance R5 − R20 > 0.07 kPa/L/s. The consistency of spirometry, IOS and CT for diagnosing SAD was assessed using Kappa coefficient. Correlations among the three techniques-measured small airway function parameters were assessed by Spearman correlation analysis.Results2055 subjects were included in the final analysis. There was low agreement in SAD assessment between spirometry and CT (Kappa = 0.126, 95% confidence interval [CI]: 0.106 to 0.146, p < 0.001), between IOS and CT (Kappa = 0.266, 95% CI: 0.219 to 0.313, p < 0.001), as well as among spirometry, IOS, and CT (Kappa = 0.056, 95% CI: 0.029 to 0.082, p < 0.001). The correlation was moderate (|r|: 0.5 to 0.7, p < 0.05) between spirometry and CT-measured small airway function parameters, and weak (|r|< 0.4, p < 0.05) between IOS and CT-measured small airway function parameters. Only spirometry-defined SAD group had more lower lung function (FEV1/FVC: adjusted difference=-10.7%, 95% CI: -13.5% to -7.8%, p < 0.001) and increased airway wall thickness (Pi 10: adjusted difference = 0.3 mm, 95% CI: 0 to 0.6 mm, p = 0.046) than only CT-defined SAD group. Only IOS-defined SAD group had better lung function (FEV1/FVC: adjusted difference = 3.9%, 95% CI: 1.9 to 5.8%, p < 0.001), less emphysema (inspiratory LAA− 950: adjusted difference=-2.1%, 95% CI:-3.1% to -1.1%, P < 0.001; PRMEmph: adjusted difference=-2.3%, 95% CI: -3.2% to -1.4%, p < 0.001), and thicker airway wall (Pi 10: adjusted difference = 0.2 mm, 95% CI: 0.1 mm to 0.4 mm, p = 0.005) than only CT-defined SAD group.ConclusionsThere was low consistency in the assessment of SAD between spirometry and CT, between IOS and CT, as well as among spirometry, IOS, and CT.Clinical trial numberNot applicable.

Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT.

Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs. We conducted a pilot study to explore which DRR technique best approaches the image quality of chest X-Rays (CXR). We quantitatively and qualitatively compared four DRR techniques. A retrospective convenience sample of 217 patients who underwent both ultra-low-dose (ULD) chest CT and CXR was used. Four DRRs were generated per ULDCT, and CheXNet, a neural network trained to detect 14 diseases, was applied to CXRs and DRRs to compute area under the curve (AUC) scores. For qualitative assessment, six radiologists rated the image quality of the four DRRs generated from six ULDCTs on a Likert scale from 1 to 6 ('not diagnostic quality' to 'diagnostic quality') and provided feedback, which was analysed using inductive category development. CheXNet's AUC for CXRs was 0.80, while DRR techniques ranged from 0.75 to 0.82 (p > 0.26). Radiologists rated the diagnostic quality of the DRRs between 3.0 and 3.5 on average. The SoftMip technique scored highest in both the quantitative (AUC = 0.82) and the qualitative (score = 3.5) evaluation. DRRs showed comparable disease detection performance to CXRs, suggesting non-inferiority. However, radiologists expressed concerns about DRR image quality, particularly in terms of resolution, noise, and overall look-and-feel. Addressing these limitations with advanced techniques may further align DRRs with the diagnostic standards of CXRs.

Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.

Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation. This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance. We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests. The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (χ21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (χ21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (χ21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (χ21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (χ21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%. The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems.

Exploring the feasibility of millimeter-wave sensors for non-invasive respiratory motion visualization in diagnostic imaging and therapy.

Management of respiratory motion during radiation therapy is essential for accurate dose delivery and minimizing the risk to organs. In diagnostic imaging, respiratory monitoring is required for confirmation of breath-hold and four-dimensional computed tomography (CT) reconstruction. Although respiratory monitoring systems are widely used in radiation therapy, they are not often used for diagnostic imaging, where they could improve image quality. The purpose of this study was to use a millimeter-wave sensor (MWS) to noninvasively visualize respiratory motion, confirm breath-holding, and explore the potential for clinical implementation of an MWS in diagnostic x-ray imaging, CT, and radiation therapy. A 24GHz microMWS was used in this study. The MWS directionality was determined using a radio-wave dark-box system. An antenna directionality test evaluated the effective azimuthal and elevational beamwidths. Respiratory waveforms were detected by optimizing the fast Fourier transform threshold and the cutoff frequencies of the bandpass filter. To confirm the reproducibility of the MWS, the detected waveforms were compared with those of a respiratory motion phantom (QUASAR), the amplitude of motion of which could be controlled. The time from valley to peak of the waveforms obtained by normalized MWS and the QUASAR were compared. The MWS was used to acquire respiratory waveforms of 20 healthy volunteers (including an infant and a child) in geometries adopted during chest CT (supine position; anteroposterior view; source-to-surface distance, 400mm) and chest x-ray imaging (standing position; posteroanterior view; source-to-surface distance, 1800mm). The effective azimuthal and elevational beamwidths of the MWS were approximately±20° and±40°, respectively. By optimizing the acquisition parameters (high-sensitivity setting; with noise cancelling; frequency range, 10-20min-1), the waveforms detected using the MWS approximately matched those of the respiratory motion phantom at all amplitudes. The MWS was also used to confirm breath-holding in 18 volunteers in both supine (anteroposterior view) and standing (posteroanterior view) positions. In addition, for an infant and a child who were unable to follow the instruction to stop breathing, a visual count of their inhalations matched the number of respiratory cycles measured using the MWS. The 24GHz MWS successfully monitored respiratory motion and breath-holding during radiographic and CT imaging. With effective directionality and stability, this system holds promise for clinical management of respiratory motion during diagnostic imaging and radiation therapy.

Paraneoplastic pemphigus involved oral mucosa only: a case report and literature review

Paraneoplastic pemphigus (PNP) is a rare and potentially fatal autoimmune disease that primarily affects the mucocutaneous tissues. Here we present the clinical features of a patient with the lichenoid variant of PNP involving the oral mucosa only. The patient exhibited clinical manifestations resembling both lichen planus and pemphigus, yet these lesions remained unresponsive to corticosteroid therapy. A chest computed tomography (CT) scan revealed Castleman disease. Following surgical removal of the tumor, the patient’s oral mucosa healed completely, with no recurrence observed in a 4-year follow-up period. These findings emphasize the importance of considering PNP as a potential diagnosis in patients with refractory oral erosions displaying lichen planus-like and pemphigus-like features, particularly when unresponsive to corticosteroid therapy. A thoracic-abdominal-pelvic CT scan is recommended to facilitate accurate diagnosis and appropriate treatment.

Erector Spinae Muscle to Epicardial Visceral Fat Ratio on Chest CT Predicts the Severity of Coronavirus Disease 2019.

Chest computed tomography (CT) is a valuable tool for diagnosing and predicting the severity of coronavirus disease 2019 (COVID-19) and assessing extrapulmonary organs. Reduced muscle mass and visceral fat accumulation are important features of a body composition phenotype in which obesity and muscle loss coexist, but their relationship with COVID-19 outcomes remains unclear. In this study, we aimed to investigate the association between the erector spinae muscle (ESM) to epicardial adipose tissue (EAT) ratio (ESM/EAT) on chest CT and disease severity in patients with COVID-19. We analysed data from 1074 COVID-19 patients enrolled in the Japan COVID-19 Task Force database. The primary outcome was the rate of critical outcomes (requiring high-flow oxygen therapy, invasive ventilator support or death). The incidence of critical outcomes was compared between patients with high and low ESM/EAT ratios. The low ESM/EAT group (n = 353) had a higher incidence of critical outcomes (13.3% vs. 5.13%, p < 0.001) and mortality (2.55% vs. 0.69%, p = 0.019) than the high ESM/EAT group (n = 721). In multivariable analysis, the low ESM/EAT ratio was associated with critical outcomes (adjusted odds ratio [aOR] 2.11, 95% confidence interval [CI] 1.22-3.66) independently of the known COVID-19 severity factors including age, sex, body mass index (BMI), smoking history, lifestyle-related comorbidities and pneumonia volume. The low ESM/EAT ratio in COVID-19 patients can be obtained on chest CT and used to predict critical outcomes after disease onset, demonstrating the importance of detailed body composition assessments in COVID-19 practice.

Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals.

Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease. To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST). We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4±5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [BMI]<25kg/m2) and overweight (BMI≥25kg/m2). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year. There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8-6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52-2.45]; p=0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01-1.65]; p=0.04). SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.

Magnetic resonance imaging of focal organizing pneumonia: differential diagnosis with peripheral lung carcinoma.

Computed tomography (CT) is the most common way to evaluate focal organizing pneumonia (FOP); however, sometimes it is difficult to differentiate FOP and peripheral lung carcinoma (PLC). To clarify the MRI manifestation of FOP and the value of MR in the differential diagnosis of FOP and PLC in comparison to CT. Chest MR (3D T1WI, T2WI TSE, DWI) and CT images of 72 patients (50 men: mean age=64.7 years; 22 women: mean age=64.9 years; 36 FOPs and 36 PLCs) were retrospectively analyzed. Two experienced radiologists reviewed all CT and MR images and graded CT and MR findings completely independently. The apparent diffusion coefficient (ADC) value was measured by the two radiologists independently. Paired sample t-test and Fisher's exact test were used to compare the ADC values and MR features between the two groups. Finally, the ROC curve was used to evaluate the diagnostic efficiency of MR. The ADC value of FOP was larger than PLC (P < 0.05). Necrosis, abscess cavity, broad contact with the pleura, and focal pleural effusion were more common in FOP (P < 0.05). PLC patients showed more (P < 0.05) irregular margins, pleural indentation, and lymphadenopathy. ADC value can be used to differentiate FOP and PLC, and the cutoff value is 1048 × 10-6mm2/s. The sensitivity, specificity, AUC and accuracy of diagnosis of CT, MR was (61.1%, 88.9%, 0.820, and 75%) vs (72.2%, 97.2%, 0.950, and 93.1%), respectively. Compared with CT, MR can increase radiologists' confidence in the differential diagnosis of FOP and PLC.

Evaluating physician concordance in interpretation of tracheobronchomalacia diagnosis and phenotyping using dynamic expiratory chest computed tomography

Tracheobronchomalacia (TBM) presents diagnostic challenges due to its nonspecific symptoms and variability in diagnostic methods. This study evaluates physician concordance in TBM diagnosis and phenotyping using chest computed tomography (CT) scans with dynamic expiratory views. We conducted a retrospective cross-sectional study at Mayo Clinic Rochester, analyzing 150 patients with dynamic expiratory CT scans. Three specialists—a thoracic radiologist, a bronchoscopist, and a pulmonologist—reviewed identical CT scans, blinded to prior interpretations. Inter-rater agreement was assessed using Fleiss’s Kappa for TBM diagnosis and Cohen’s Kappa for TBM phenotype classification into six categories: No TBM, Excessive Dynamic Airway Collapse (EDAC), Crescent Type, Circumferential Type, Saber-Sheath Type, and Mixed Type. Among the 150 patients, 54 (36%) were diagnosed with TBM or EDAC. TBM was more prevalent in males, older individuals, and smokers. Agreement among specialists was substantial for TBM diagnosis (Fleiss’s Kappa = 0.61, p < 0.001) but moderate for phenotype classification (Fleiss’s Kappa = 0.52, p < 0.001). The highest concordance was between the thoracic radiologist and the pulmonologist (Cohen’s Kappa = 0.68), while the lowest was between the bronchoscopist and other specialists. There is substantial agreement in TBM diagnosis using chest CT scans with dynamic expiratory views, but moderate variability in phenotyping. Standardizing criteria and integrating pulmonary function testing could enhance diagnostic consistency and clinical relevance.

The association between body composition and overall survival in patients with advanced non-small cell lung cancer

Nutritional status is associated with prognosis in a variety of cancers. Studies analyzing the association between the measurements of skeletal muscle and adipose tissue obtained from Computerized Tomography (CT) images at the time of diagnosis of advanced non-small cell lung cancer (NSCLC) and overall survival (OS) are relatively few. Data from 425 patients diagnosed with advanced NSCLC between January 2016 and December 2017 were retrospectively analyzed, with an average follow-up of 15.3 months. To outline the patient’s chest CT plain image at the time of diagnosis,skeletal muscle and subcutaneous fat at the level of both thoracic vertebrae were quantified in terms of mass and quantity by the pectoral muscle index (PMI), pectoral muscle density (PMD), subcutaneous fat index (SFI), subcutaneous fat density (SFD), paravertebral muscle index (PVMI), and paravertebral muscle density (PVMD). The SFI value in the female survival group is significantly lower than that in the death group (P = 0.049), and the PVMI value in the overall survival group is significantly lower than that in the death group (P < 0.001). After adjusting for clinical variables such as gender, smoking status, clinical staging, degree of differentiation, and radiotherapy history, the multivariable Cox regression analysis showed that an increase in SFI significantly improves the overall survival rate of patients (Hazard Ratio [HR] = 1.410, 95% Confidence Interval [CI]: 1.042–1.908, P = 0.026). Conversely, a decrease in PVMD is significantly associated with improved overall survival and prognosis (HR = 0.762, 95% CI: 0.579–0.982, P = 0.048). No association was found between body mass index (BMI) and chest muscle status indicators and overall survival (P > 0.05). CT-measured body composition parameters provide precise prognostic information and are superior to BMI; an increased OS rate in advanced NSCLC is associated with a greater SFI and a lower PVMD.

Prognostic value of chest computer tomography combined with serum platelet count, c-reactive protein levels and oxygenation index in severe community-acquired pneumonia

Prognostic value of chest computer tomography combined with serum platelet count, c-reactive protein levels and oxygenation index in severe community-acquired pneumonia

Cocaine-Induced Limbic Encephalopathy Manifesting as Acute Amnesia: A Case Report

Background: Cocaine has been shown to cause cytotoxic neuronal damage, which has been implicated in cases of leukoencephalopathy. We present a case of cocaine-induced toxic encephalopathy resulting in predominant lesions to the gray matter on magnetic resonance imaging (MRI). Case Presentation: A 70-year-old female presented acutely with confusion, agitation, and disorientation. She was markedly hypertensive with other vital signs within normal range. On presentation to the emergency department, she was uncooperative and had an unsteady gait but showed no focal neurological deficits. Her lab work was positive for elevated cardiac troponins, elevated D-dimer, and a urine drug screen positive only for cocaine. Head computed tomography (CT) showed no hemorrhage and head CT angiogram showed no abnormalities and no significant vascular stenosis. Chest X-ray and CT showed diffuse ground glass opacities compatible with atypical pneumonia. Antibiotics were initiated to treat the pneumonia and antihypertensives were administered to manage her blood pressure. She was also given IV thiamine. Brain MRI showed restricted diffusion involving bilateral hippocampi, thalami, putamen, caudate, and right occipital lobe, findings suspicious for cytotoxic edema. After acute stabilization, the patient demonstrated profound anterograde and retrograde amnesia, which improved gradually over days to weeks. She was eventually discharged to a skilled nursing facility. Conclusion: To our knowledge, this is the first reported case of profound amnesia secondary to cocaine-induced toxic encephalopathy with bilateral hippocampal involvement. These symptoms correlate with the implicated neuroanatomical structures. This case demonstrates that cocaine may be implicated in toxic encephalopathy affecting the brain’s gray matter and highlights a unique presentation of these findings.

Coronary Artery Calcification on Chest Computed Tomography as a Predictor of Cardiovascular Adverse Events in Patients With COVID-19 - A Multicenter Retrospective Study in Japan.

Coronary artery calcification (CAC) detected through chest computed tomography (CT) strongly predicts cardiovascular events in asymptomatic individuals undergoing primary prevention. Few studies with limited sample sizes have investigated the predictive value of CAC for cardiovascular complications in COVID-19. This study examined the impact of CAC on cardiovascular complications using a large-scale COVID-19 database. This multicenter retrospective cohort study used data from the Japan COVID-19 Task Force database. After exclusion based on missing information, 1,109 patients with COVID-19 were included. The Agatston score was used to evaluate CAC, dividing the population into 3 groups based on calcification degree (no, moderate, and severe CAC). The primary outcome was cardiovascular complications; the secondary outcome was critical outcomes. The severe CAC group had a higher rate of cardiovascular complications than the other groups. Multivariable analysis, considering COVID-19 severity factors, identified severe CAC as independently associated with cardiovascular complications but not with critical outcomes. Subgroup analysis revealed that, in patients without hypertension, diabetes, cardiovascular disease, or chronic kidney disease, severe CAC was significantly correlated with cardiovascular complications, whereas this association was not observed in patients with these underlying conditions. Patients with COVID-19 and severe CAC had increased cardiovascular complications, and identifying cardiovascular and pulmonary findings on chest CT is essential. Measuring CAC via non-electrocardiogram-gated CT helps predict patient risk.

Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.

For emergency department (ED) patients, lung cancer may be detected early through incidental lung nodules (ILNs) discovered on chest CTs. However, there are significant errors in the communication and follow-up of incidental findings on ED imaging, particularly due to unstructured radiology reports. Natural language processing (NLP) can aid in identifying ILNs requiring follow-up, potentially reducing errors from missed follow-up. We sought to develop an open-access, three-step NLP pipeline specifically for this purpose. This retrospective used a cohort of 26,545 chest CTs performed in three EDs from 2014 to 2021. Randomly selected chest CT reports were annotated by MD raters using Prodigy software to develop a stepwise NLP "pipeline" that first excluded prior or known malignancy, determined the presence of a lung nodule, and then categorized any recommended follow-up. NLP was developed using a RoBERTa large language model on the SpaCy platform and deployed as open-access software using Docker. After NLP development it was applied to 1000 CT reports that were manually reviewed to determine accuracy using accepted NLP metrics of precision (positive predictive value), recall (sensitivity), and F1 score (which balances precision and recall). Precision, recall, and F1 score were 0.85, 0.71, and 0.77, respectively, for malignancy; 0.87, 0.83, and 0.85 for nodule; and 0.82, 0.90, and 0.85 for follow-up. Overall accuracy for follow-up in the absence of malignancy with a nodule present was 93.3%. The overall recommended follow-up rate was 12.4%, with 10.1% of patients having evidence of known or prior malignancy. We developed an accurate, open-access pipeline to identify ILNs with recommended follow-up on ED chest CTs. While the prevalence of recommended follow-up is lower than some prior studies, it more accurately reflects the prevalence of truly incidental findings without prior or known malignancy. Incorporating this tool could reduce errors by improving the identification, communication, and tracking of ILNs.

A Structured, Anatomy-Based Chest CT Interpretation Curriculum for Pulmonary Fellows Covering the Main Patterns of Parenchymal Lung Disease.

Chest computed tomography (CT) interpretation is a key competency for pulmonary fellows, with many resources intended for radiologists but very few for this specific group. We endeavored to create a curriculum to teach chest CT interpretation to first-year pulmonary fellows. We assembled a team of two pulmonologists, one radiologist, and a fellow with computer drafting software experience. We reviewed the literature, used principles of cognitive load theory to outline the content of our curriculum, collected original CT images exemplifying key patterns of disease, created original illustrations using computer drafting programs, and outlined frameworks to identify chest CT patterns and build differential diagnoses. We divided the material into five short videos and provided 18 practice cases to be reviewed asynchronously. We then organized a 1-hour in-person review session facilitated by a chest radiologist. We created a survey to assess our curriculum. The material presented here has been delivered to three consecutive classes of first-year pulmonary and critical care medicine fellows at our institution. Nineteen fellows in three cohorts reviewed the curriculum. Twelve fellows (63% response rate) completed the postcurriculum survey. Overall, there was a significant improvement in comfort, with the calculated paired sample t test showing a mean comfort of 3.2 precurriculum and a mean comfort of 4.5 postcurriculum (p < .001). This self-guided, interactive curriculum provides a structured approach connecting key lung anatomy to patterns of disease and is an effective way to teach chest CT interpretation to pulmonary fellows.

Tuberculous longitudinal extensive transverse myelitis: 2 cases and literature review

Context This study aimed to investigate the characteristics, diagnosis, and management of tuberculous longitudinally extensive transverse myelitis (TB-LETM), a rare manifestation of tuberculosis. Findings We analyzed two rare cases of TB-LETM and discussed their clinical manifestations and imaging findings in the context of the relevant literature. Patient 1, a 23-year-old female, presented with quadriplegia and dysuria, and spinal magnetic resonance imaging (MRI) revealed lesions extending from C1 to T3. Patient 2, an 18-year-old female, reported acute-onset numbness and weakness in both lower limbs, with MRI showing lesions from T1 to T4, along with multiple intracranial leptomeningeal enhancements. Both patients had elevated cerebrospinal fluid (CSF) cell counts and protein levels, and positive blood T-cell spot test (T-SPOT.TB), but no microbiological evidence of Mycobacterium tuberculosis was found. Diagnosis was based on clinical presentation, medical history, chest computed tomography (CT) findings, and CSF analysis. Over the past decade, 14 cases of TB-LETM have been reported. Common symptoms include fever, acute paralysis, sensory deficits in the lower limbs, and dysuria. Elevated CSF protein, lymphocytosis, and decreased glucose levels are essential for differentiating TB-LETM from autoimmune disorders. Conclusion Longitudinally extensive transverse myelitis (LETM), especially when involving the cervical and thoracic cords, should be considered a potential manifestation of tuberculosis infection. Analysis of cerebrospinal fluid, MRI findings, and T-SPOT testing can provide crucial diagnostic insights. A combination of anti-tuberculosis therapy and corticosteroids has proven clinically effective in managing this condition.

Association of Pulmonary Involvement at Baseline with Exercise Intolerance and Worse Physical Functioning 8 Months Following COVID-19 Pneumonia.

Objectives: We aimed to describe the cardiopulmonary function during exercise and the health-related quality of life (HRQoL) in patients with a history of COVID-19 pneumonia, stratified by chest computed tomography (CT) findings at baseline. Methods: Among 77 consecutive patients with COVID-19 who were discharged from the Pulmonology Ward between March 2020 and April 2021, 28 (mean age 54.3 ± 8.6 years, 8 females) agreed to participate to the current study. The participants were analyzed in two groups based on pulmonary involvement (PI) at baseline chest CT applying a threshold of 25%. A consequent artificial intelligence (AI)-guided total opacity score (TOS) was calculated in a subgroup of 22 patients. A cardiopulmonary exercise test (CPET) was conducted on average 8.4 (±1.9) months after discharge from the hospital. HRQoL was defined using the short-form (SF-36) questionnaire. The primary outcome was exercise intolerance that was defined as a peak oxygen uptake (V'O2peak) < 80% predicted. Secondary outcomes were ventilatory limitation, defined as breathing reserve < 15%, circulatory limitation, defined as oxygen pulse predicted below 80%, and deconditioning, defined as exercise intolerance in the absence of ventilatory and circulatory limitations. Other secondary outcomes included the SF-36 domains. Results: In all, 15 patients had at least 25% PI (53.6%) at baseline chest CT. Exercise intolerance was observed in ten patients (35.7%), six due to deconditioning and four due to circulatory limitation; none had ventilatory limitation. AI-guided TOS was 30.1 ± 24.4% vs. 6.1 ± 4.8% (p < 0.001) at baseline, and 1.7 ± 3.0% vs. 0.2 ± 0.7% (nonsignificant) at follow-up in high and low PI groups, respectively. The physical functioning (PF) domain score of the SF-36 questionnaire was 66.3 ± 19.4 vs. 85.0 ± 13.1 in high and low PI groups, respectively (p = 0.007). Other SF-36 domains did not differ significantly between the groups. A high PI at baseline was inversely correlated with V'O2peak (standardized β coefficient = -0.436; 95% CI -26.1; -0.7; p = 0.040) and with PF scores (standardized β coefficient -0.654; 95% CI -41.3; -7.6; p = 0.006) adjusted for age, sex, body mass index and lung diffusion capacity. Conclusions: One-third of participants experienced exercise intolerance eight months after COVID-19 pneumonia. A higher PI at baseline was significantly associated with exercise intolerance and PF. Notwithstanding, the radiological PI was resolved, and the exercise intolerance was mainly explained not by ventilatory limitation but by circulatory limitation and deconditioning.

Prognostic role of midregional proadrenomedullin in predicting infection in pediatric cancer with febrile neutropenia.

Febrile neutropenia (FN) remains an important complication of cytotoxic chemotherapy for which an urgent and appropriate evaluation is imperative. To assess the diagnostic and prognostic roles of mid-regional pro-adrenomedullin (MR-ProADM) levels in predicting infection in patients with FN. This comparative cross-sectional study included 137 patients with chemotherapy-induced FN. Complete blood count, C-reactive protein (CRP), procalcitonin, and MR-ProADM were evaluated on the 1st day of FN. Chest computed tomography (CT) was performed on the 5th day. MR-ProADM levels were significantly higher in patients with FN than in controls. CRP and MR-ProADM levels were significantly higher and ANC was significantly lower in patients with versus without bacterial infections. CRP, procalcitonin, and MR-ProADM levels were significantly negatively correlated with absolute neutrophil count (ANC). CRP, procalcitonin, and MR-ProADM levels were significantly and positively correlated with FN degree, FN duration, and hospital stay length. A multivariate regression analysis showed that a longer FN duration and hospital stay length, along with elevated CRP, procalcitonin, and MR-ProADM levels, were significant risk factors for mortality. MR-ProADM is a reliable prognostic and diagnostic tool for predicting infection in patients with FN.

Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study

IntroductionA large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.Materials and methodsOur team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals. These participants were divided into a training set (n = 581), an external test set 1 (n = 229), an external test set 2 (n = 198) and an external test set 3 (n = 118). Five CNN models were constructed based on chest CT images to screen patients with osteoporosis and compared with the SMI model to predict the performance of osteoporosis patients.ResultsAll CNN models have good performance in predicting osteoporosis patients. The average F1 score of Densenet121 in the three external test sets was 0.865. The area under the curve (AUC) of Desenet121 in external test set 1, external test set 2, and external test set 3 were 0.827, 0.859, and 0.865, respectively. Furthermore, the Densenet121 model demonstrated a notably superior performance compared to the SMI model in predicting osteoporosis patients.ConclusionsThe CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures.Critical relevance statementThe CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures.Key PointsThe application of unenhanced chest CT is increasing.Most people do not consciously use DXA to screen themselves for osteoporosis.A deep learning model was constructed based on CT images from four institutions.Graphical

Efficacy of Open Reduction and Internal Fixation Assisted by Handheld Ultrasound Combined with 3D Printing Technology in Treating Multiple Rib Fractures.

This study aimed to explore the efficacy of open reduction and internal fixation assisted by handheld ultrasound combined with three-dimensional (3D) printing technology in treating multiple rib fractures. We retrospectively analyzed the clinical data from 84 patients affected with multiple rib fractures admitted to our hospital between August 2022 and April 2024. After excluding four cases, 80 cases were included in this study. Based on the method of preoperative rib fracture localization, patients were divided into three groups: group A (n = 30), group B (n = 26), and group C (n = 24). Group A received 3D reconstruction of ribs on chest Computed Tomography (CT), group B adopted the examination of handheld ultrasound and 3D reconstruction of ribs on chest CT, and group C received handheld ultrasound, 3D reconstruction of ribs on chest CT, and 3D printing technology. The operation-related indicators (such as incision length, exposure time of surgical field, intraoperative blood loss), pulmonary function [total lung capacity (TLC), forced vital capacity (FVC), forced expiratory volume in one second/forced vital capacity (FEV1/FVC), residual volume (RV)], quality of life, degree of pain, and incidence of complications were compared among the three groups. Before surgery, there were no significant differences in pulmonary function, quality of life, and degree of pain among the three groups (p > 0.05). However, after surgery, significant differences in the pairwise comparison of TLC, FVC, FEV1/FVC and RV were observed among the three groups (p < 0.001), with group C indicating the highest levels of observational indicators and group A exhibiting the lowest levels. Furthermore, a significant difference was observed in the pairwise comparison of incision length, exposure time of surgical field, and intraoperative blood loss among the three groups (p < 0.001). Group C had the shortest incision length, the lowest exposure time in the surgical field, and the least intraoperative blood loss, while those in group A were the opposite. After surgery, a significant difference was found in the pairwise comparison of the quality of life and degree of pain among the three groups (p < 0.001). Group C had the highest quality of life and the lowest degree of pain, while Group A had the opposite results. Additionally, there was no significant difference in the incidence of complications among the three groups (p > 0.05). The open reduction and internal fixation assisted by handheld ultrasound combined with 3D printing technology revealed effective curative outcomes in treating multiple rib fractures. This method promotes the formulation of an accurate and personalized surgical plan and seems to have high clinical significance.