Low-dose Chest Research Articles

Computed tomography versus radiography for the detection of rib and skull fractures in paediatric suspected physical abuse: a systematic review.

The diagnosis of suspected physical abuse (SPA) remains a continuous challenge to paediatric healthcare. Several studies have reported that computed tomography (CT) improves the evaluation of SPA. This study aims to systematically review the diagnostic performance of CT compared to radiography in investigating skull and chest fractures for SPA. Multiple databases were searched, using PRISMA methods, from 2008 to August 2024 for relevant studies in English. Two reviewers independently screened and selected relevant studies using Covidence software. The QUADAS-2 tool was used for the quality assessment of the included papers. Sensitivity, specificity and the effective radiation dose of CT and radiography from the included studies were extracted. Pooled sensitivity and specificity were calculated with their respective 95% confidence intervals (CI). GRADE criteria were used to appraise the overall quality of the synthesis. Of the 4057 identified papers, 10 met the inclusion criteria; all 10 included skull and/or chest. The overall sensitivity and specificity of CT were 96.5% (95% CI, 94.9-97.7%) and 99.5% (95% CI, 99.1-99.8%), respectively. Compared to the sensitivity and specificity of radiography, 59.8% (95% CI, 56.2-63.3%) and 99.7% (95% CI, 99.3-99.8%), respectively. Conclusion: CT sensitivity is significantly higher than radiography in detecting rib and skull fractures for SPA. The effective dose for chest LDCT is comparable to that of radiography. Therefore, LDCT should be considered a potential replacement to radiography in SPA investigations for the chest and skull. What is Known • CT shows higher diagnostic performance than radiography in detecting skull and rib fractures in the diagnosis of SPA. What is New • When a head CT scan is acquired for SPA diagnosis at any age, the two-view skull radiograph can be safely eliminated from the Skeletal Survey protocol, likewise, Chest CT can replace chest radiography for SPA diagnosis of rib fractures. • The effective dose and image quality of low-dose chest CT is comparable to that of two-view chest radiography for SPA diagnosis.

Prognostic importance of extensive coronary calcium on lung cancer screening chest computed tomography.

Low-dose chest computed tomography (CT) is used for lung cancer screening, but can also detect coronary artery disease as coronary artery calcium. We sought to determine the prevalence and prognostic utility of coronary artery calcium in a population at high risk of cancer. We reviewed CT scans from consecutive participants screened for lung cancer between March 2017 and November 2018 as part of the Ontario Health Lung Cancer Screening Pilot for People at High Risk. We quantified coronary artery calcium using an estimated Agatston score. We identified the composite primary outcome of all-cause death and cardiovascular events using linked electronic medical record data from The Ottawa Hospital to December 2023. Among 1486 people who underwent screening CT, coronary artery calcium was detected in 1232 (82.9%) and was extensive in 439 (29.5%). On multivariable analysis, extensive coronary artery calcium was associated with the composite primary outcome (hazard ratio [HR] 2.13, 95% confidence interval [CI] 1.35-3.38), all-cause death (HR 2.39, 95% CI 1.34-4.27), and cardiovascular events (HR 2.06, 95% CI 1.13-3.77). Extensive coronary artery calcium remained predictive of cardiovascular events after we adjusted for noncardiovascular death as a competing risk (HR 2.05, 95% CI 1.09-3.85). Among people undergoing low-dose chest CT for lung cancer screening, extensive coronary artery calcium was an independent predictor of all-cause death and cardiovascular events, even after adjustment for noncardiovascular death. The opportunity to identify and reduce risks from coronary artery disease may represent an additional benefit of lung cancer screening.

Effect of contrast media on CT bone density assessment: comparative analysis of low-dose chest CT and abdominopelvic CT

Detecting individuals with low bone mineral density (BMD) before clinical fractures occur may help improve the outcomes of osteoporosis and osteopenia. Although computed tomography (CT) is useful for opportunistic BMD measurement, the modality most suitable for opportunistic screening remains unclear. In this retrospective study, we compared the diagnostic performance of low-dose chest CT (LDCT) and contrast-enhanced abdominopelvic CT (APCT) for measuring BMD at L1 level using dual-energy X-ray absorptiometry (DEXA) as a reference in individuals who underwent LDCT, APCT, and DEXA assessments on the same day. We included 512 individuals (median age: 60 years; interquartile range, 55−65 years; 307 men). Both LDCT (r = 0.706; P < 0.001) and APCT (r = 0.643; P < 0.001) exhibited strong correlation with DEXA T-scores. As T-scores decreased, the relative difference between LDCT and APCT Hounsfield unit values increased (b = − 6.456; P < 0.001). LDCT outperformed APCT in diagnosing both osteoporosis (AUC, 0.865 vs. 0.833; P = 0.035) and low BMD (AUC, 0.844 vs. 0.815; P = 0.006), which may be attributable to the greater effect of intravenous contrast media on CT scan characteristics in individuals with lower T-scores. These results may help inform the selection of imaging methods suitable for screening.

Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients.

Pneumonia, a major infectious cause of morbidity and mortality among children worldwide, is typically diagnosed using low-dose pediatric chest X-ray [CXR (chest radiography)]. In pediatric CXR images, bone occlusion leads to a risk of missed diagnosis. Deep learning-based bone-suppression networks relying on training data have enabled considerable progress to be achieved in bone suppression in adult CXR images; however, these networks have poor generalizability to pediatric CXR images because of the lack of labeled pediatric CXR images (i.e., bone images vs. soft-tissue images). Dual-energy subtraction imaging approaches are capable of producing labeled adult CXR images; however, their application is limited because they require specialized equipment, and they are infrequently employed in pediatric settings. Traditional image processing-based models can be used to label pediatric CXR images, but they are semiautomatic and have suboptimal performance. We developed an efficient labeling approach for fine-tuning pediatric CXR bone-suppression networks capable of automatically suppressing bone structures in CXR images for pediatric patients without the need for specialized equipment and technologist training. Three steps were employed to label pediatric CXR images and fine-tune pediatric bone-suppression networks: distance transform-based bone-edge detection, traditional image processing-based bone suppression, and fully automated pediatric bone suppression. In distance transform-based bone-edge detection, bone edges were automatically detected by predicting bone-edge distance-transform images, which were then used as inputs in traditional image processing. In this processing, pediatric CXR images were labeled by obtaining bone images through a series of traditional image processing techniques. Finally, the pediatric bone-suppression network was fine-tuned using the labeled pediatric CXR images. This network was initially pretrained on a public adult dataset comprising 240 adult CXR images (A240) and then fine-tuned and validated on 40 pediatric CXR images (P260_40labeled) from our customized dataset (named P260) through five-fold cross-validation; finally, the network was tested on 220 pediatric CXR images (P260_220unlabeled dataset). The distance transform-based bone-edge detection network achieved a mean boundary distance of 1.029. Moreover, the traditional image processing-based bone-suppression model obtained bone images exhibiting a relative Weber contrast of 93.0%. Finally, the fully automated pediatric bone-suppression network achieved a relative mean absolute error of 3.38%, a peak signal-to-noise ratio of 35.5dB, a structural similarity index measure of 98.1%, and a bone-suppression ratio of 90.1% on P260_40labeled. The proposed fully automated pediatric bone-suppression network, together with the proposed distance transform-based bone-edge detection network, can automatically acquire bone and soft-tissue images solely from CXR images for pediatric patients and has the potential to help diagnose pneumonia in children.

Changes in Staging and Management of Non-Small Cell Lung Cancer (NSCLC) Patients Following the Implementation of Low-Dose Chest Computed Tomography (LDCT) Screening at Kaohsiung Medical University Hospital.

Low-dose computed tomography (LDCT) has been widely adopted for lung cancer screening due to its proven ability to reduce lung cancer mortality, especially among high-risk populations. This retrospective study aims to evaluate the impact of LDCT screening on non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, following the introduction of LDCT in 2013. The study examines the correlation between LDCT screening volume and changes in the distribution of NSCLC stages, particularly early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, it explores the differences in histopathological subtypes, focusing on adenocarcinoma and squamous cell carcinoma, and assesses the impact of early detection on five-year survival rates. The results show a significant increase in early-stage NSCLC diagnoses, particularly in adenocarcinoma cases, where early-stage diagnoses rose from 10.4% in 2010 to 38.7% in 2019. However, the number of stage IV cases remained stable, indicating that LDCT may not substantially reduce late-stage diagnoses. Pearson's correlation analysis demonstrated a strong positive correlation between LDCT screening and early-stage NSCLC detection, particularly for adenocarcinoma (p < 0.001), though the early detection of squamous cell carcinoma and small cell carcinoma remained limited. The study concludes that LDCT screening plays a crucial role in improving early NSCLC detection and five-year survival rates. Future research should focus on optimizing screening strategies to capture more at-risk populations and enhance the detection of harder-to-diagnose subtypes like squamous cell carcinoma.

Appropriateness Criteria for Radiation Therapy in the Setting of Presumed Early-Stage Lung Cancer

Low-dose chest computed tomography imaging for lung cancer screening is revealing increasing numbers of radiographic early-stage lung cancers. This topic discussion describes when a clinical scenario merits radiation therapy without a histologic diagnosis, with an emphasis on pragmatic algorithms in settings without readily available advanced biopsy techniques.

Artificial intelligence to detect pulmonary congestion on chest computed tomography in consecutive dyspnoeic patients

Abstract Background Pulmonary congestion is a hallmark of acute heart failure (AHF), and essential for the diagnosis, treatment, and risk stratification. We have previously shown that a low-dose chest CT has four times greater odds than chest radiographs to detect pulmonary congestion. However, implementation is limited by shortage of radiologists. We hypothesized that artificial intelligence (AI) can detect pulmonary congestion on chest CT and aimed to develop and validate an AI-algorithm to detect pulmonary congestion on chest CT. Methods We extracted all chest CT-scans ordered acutely (N= 4840 unique patients) by medical departments at X Hospital from 2016-2021. A random subset of these CT-scans (N= 2187) was used to develop an AI-algorithm to detect pulmonary congestion, using the radiology reports as reference. The remaining subset (N= 1094) was used for internal validation. We externally validated the AI-algorithm in a different cohort (X), comprising of 236 consecutive dyspnoeic patients admitted to the emergency department, all examined with a low-dose, non-contrast chest CT (LDCT). The primary outcome was pulmonary congestion adjudicated by clinical on-call radiologists with access to the medical record, and two independent, blinded research thoracic radiologists. The default AHF thresholds for the optimal cut-off, rule-in threshold and rule-out threshold was established from the internal validation. Results Of 236 patients (median age 74 years, 57% male), 62 (26%), 64 (27%) and 60 (26%) had pulmonary congestion on LDCT evaluated by the on-call radiologists and the two thoracic research radiologists, respectively. The AI-algorithm included ten discriminating features of pulmonary congestion, and the results demonstrate an AUC of 0.95 [95%CI: 0.93-0.98] to predict pulmonary congestion by the on-call radiologists. The results were confirmed by the two expert research radiologists, with an AUC of 0.91 [95%CI: 0.88-0.95] and 0.94 [95%CI: 0.90-0.97], respectively. The sensitivity and specificity of the AI-default threshold was 89% [95%CI: 78-95%] and 89% [95%CI: 83-93%], respectively. The rule-in threshold demonstrated a sensitivity of 97% [95%CI: 86-100%] and the rule-out threshold had a specificity of 96% [95%CI: 92-98%], Figure 1. Conclusion We developed and validated a novel AI-algorithm with promising potential for the rapid automatic detection of pulmonary congestion as the underlying cause of dyspnoea in consecutive acute patients.

Editorial Comment: Computer-Aided Detection of Lung Nodules on Low-Dose Pediatric Chest CT-A Cautionary Tale.

Editorial Comment: Computer-Aided Detection of Lung Nodules on Low-Dose Pediatric Chest CT-A Cautionary Tale.

Second generation stationary chest tomosynthesis with faster scan time and wider angular span.

Digital tomosynthesis has shown potential for increasing specificity and sensitivity compared to radiography for low-dose chest imaging. Prior investigation of the s-DCT system indicated potential, but additional iteration with improved scan speed, power, and angular span was necessary for translation. The study aims to demonstrate and characterize a second-generation stationary digital chest tomosynthesis (s-DCT) scanner with increased x-ray energy, tube current, and larger angular span. The second-generation s-DCT system employed a meter-long linear carbon nanotube (CNT) source array integrated with a digital detector and patient imaging table. Tube output, focal spot size, modulation transfer function (MTF), artifact spread function (ASF), and imaging performance were evaluated. A lung phantom with simulated nodules was imaged for clinical task-based demonstration. The scanner achieved a 6s scan time, significantly improved from the prior generation's 16s. The x-ray tube exhibited good current stability, with 20.4±0.6mA tube current and focal spot size aligned with specifications (IEC 0.8). The MTF confirmed enhanced spatial resolution of 2.4lp/mm, comparable to commercial chest tomosynthesis systems. The ASF indicated improved depth resolution (5.2mm, previously 9.5mm). Phantom imaging showcased visualization of both high and low-attenuation lung nodules. The second-generation s-DCT system exhibited improved performance in terms of tube power, scan time, and image quality. Enhanced in-plane and depth resolution, along with faster imaging, suggest potential clinical benefits for improved diagnoses. Further clinical validation is warranted to ascertain the system's clinical utility.

Performance of Lung-Nodule Computer-Aided Detection Systems on Standard-Dose and Low-Dose Pediatric CT Scans: An Intraindividual Comparison.

Background: When applying lung-nodule computer-aided detection (CAD) systems for pediatric CT, performance may be degraded on low-dose scans due to increased image noise. Objective: To conduct an intraindividual comparison of the performance for lung nodule detection of two CAD systems trained using adult data between low-dose and standard-dose pediatric chest CT scans. Methods: This retrospective study included 73 patients (32 female, 41 male; mean age, 14.7 years; age range, 4-20 years) who underwent both clinical standard-dose and investigational low-dose chest CT examinations within the same encounter from November 30, 2018 to August 31, 2020 as part of an earlier prospective study. Fellowship-trained pediatric radiologists annotated lung nodules to serve as the reference standard. Both CT scans were processed using two publicly available lung-nodule CAD systems previously trained using adult data: FlyerScan and Medical Open Network for Artificial Intelligence (MONAI). The systems' sensitivities for nodules measuring 3-30 mm (n=247) were calculated when operating at a fixed frequency of two false-positives per scan. Results: FlyerScan exhibited detection sensitivities of 76.9% (190/247; 95% CI: 73.3-80.8%) on standard-dose scans and 66.8% (165/247; 95% CI: 62.6-71.5) on low-dose scans. MONAI exhibited detection sensitivities of 67.6% (167/247, 95% CI: 61.5-72.1) on standard-dose scans and 62.3% (154/247, 95% CI: 56.1-66.5%) on low-dose scans. The number of detected nodules for standard-dose versus low-dose scans for 3-mm nodules was 33 versus 24 (FlyerScan) and 16 versus 13 (MONAI), 4-mm nodules was 46 versus 42 (FlyerScan) and 39 versus 30 (MONAI), 5-mm nodules was 38 versus 33 (FlyerScan) and 32 versus 31 (MONAI), and 6-mm nodules was 27 versus 20 (FlyerScan) and 24 versus 24 (MONAI). For nodules measuring ≥7 mm, detection did not show a consistent pattern between standard-dose and low-dose scans for either system. Conclusions: Two lung-nodule CAD systems demonstrated decreased sensitivity on low-dose versus standard-dose pediatric CT scans performed in the same patients. The reduced detection at low dose was overall more pronounced for nodules measuring less than 5 mm. Clinical Impact: Caution is needed when using low-dose CT protocols in combination with CAD systems to help detect small lung nodules in pediatric patients.

Opportunistic Screening for Low Bone Mineral Density in Adults with Cystic Fibrosis Using Low-Dose Computed Tomography of the Chest with Artificial Intelligence.

Background: Cystic fibrosis bone disease (CFBD) is a common comorbidity in adult people with cystic fibrosis (pwCF), resulting in an increased risk of bone fractures. This study evaluated the capacity of artificial intelligence (AI)-assisted low-dose chest CT (LDCT) opportunistic screening for detecting low bone mineral density (BMD) in adult pwCF. Methods: In this retrospective single-center study, 65 adult pwCF (mean age 30.1 ± 7.5 years) underwent dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae L1 to L4 to determine BMD and corresponding z-scores and completed LDCTs of the chest within three months as part of routine clinical care. A fully automated CT-based AI algorithm measured the attenuation values (Hounsfield units [HU]) of the thoracic vertebrae Th9-Th12 and first lumbar vertebra L1. The ability of the algorithm to diagnose CFBD was assessed using receiver operating characteristic (ROC) curves. Results: HU values of Th9 to L1 and DXA-derived BMD and the corresponding z-scores of L1 to L4 showed a strong correlation (all p < 0.05). The area under the curve (AUC) for diagnosing low BMD was highest for L1 (0.796; p = 0.001) and Th11 (0.835; p < 0.001), resulting in a specificity of 84.9% at a sensitivity level of 75%. The HU threshold values for distinguishing normal from low BMD were <197 (L1) and <212 (Th11), respectively. Conclusions: Routine LDCT of the chest with the fully automated AI-guided determination of thoracic and lumbar vertebral attenuation values is a valuable tool for predicting low BMD in adult pwCF, with the best results for Th11 and L1. However, further studies are required to define clear threshold values.

A Multidisciplinary Assessment of Standard Low Dose Versus Ultra-low Dose Chest CT Scans for Pectus Excavatum Imaging

AimEvaluation of ultra-low dose chest CT imaging for the assessment of pectus excavatum severity as determined by pediatric radiologists and pediatric surgeons using Haller (HI) and Correction indices (CI). MethodsA single institution, prospective evaluation of patients being evaluated for pectus excavatum were scanned with a standard low-dose chest CT protocol (CARE) followed by a consecutive ultra-low dose CT scan (ULTRA). 3 surgeons and 4 radiologists were instructed to determine HI and CI in each series. The Intraclass Correlation Coefficient (ICC) was used to calculate the agreement level between CARE and ULTRA. Bland–Altman (BA) and scatter plots were also performed to determine bias of each approach. Results32 patients had CARE and ULTRA consecutively. The ICC for HI demonstrated good reliability with a value of 0.89 and excellent reliability for CI with a value of 0.91. The reliability for HI was greater in the surgeon group (0.89) compared to the radiologist group (0.88). The reliability for CI was greater in the radiologist group (0.92) compared to the surgeon group (0.90). The Bland Altman plots for the HI and CI demonstrate no consistent bias for CARE or ULTRA approach when evaluating HI and CI. ConclusionUltra-low dose CT scan imaging compared to standard low-dose CT appears to be a reliable alternative for evaluating PE severity as assessed by HI and CI. This work supports the evaluation and potential development of a standardized CT imaging protocol capable of reducing radiation exposure without sacrificing imaging for PE patients. Level of Evidence2.

Evaluating the effectiveness of artificial intelligence imaging in the qualitative diagnosis of pulmonary nodules

Our study aimed to evaluate the effectiveness of artificial intelligence (AI) image diagnostic systems in the qualitative diagnosis of pulmonary nodules. We analyzed 291 cases from June 2023 to January 2024 at Chongqing University Three Gorges Hospital. All patients in the study underwent low-dose chest computed tomography scans, which identified lung nodules, followed by thoracic surgery for pathological confirmation. We compared the predictive accuracy of AI-based diagnosis with that of physician-based diagnosis in distinguishing between benign and malignant lung nodules. Among the 291 lung nodules examined, 226 were cancerous, and 65 were benign. Receiver operating characteristic (ROC) curves, plotted based on the malignancy probabilities predicted by both methods, revealed that the AI group achieved an area under the ROC curve (AUC) of 0.727, with a sensitivity of 90.27% and a specificity of 58.46%. In comparison, the physician-reading group had an AUC of 0.737, with a sensitivity of 83.19% and a specificity of 66.15%. Our findings demonstrate that the AI diagnostic system effectively calculates malignancy probabilities for lung nodules, highlighting its significant predictive potential. This system can serve as a valuable adjunct tool for clinicians and imaging physicians in the diagnostic process.

Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis

Abstract Purpose To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs. Methods Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland–Altman analysis and intraclass correlation coefficients (ICC). Results The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between − 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970–0.999 and 0.994–0.999, respectively), uniformity (ICCs, 0.985–0.998), entropy (ICCs, 0.931–0.993), elongation (ICCs, 0.978–0.992), and flatness (ICCs, 0.973–0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210–0.831), kurtosis (ICCs, 0.053–0.933), and sphericity (ICCs, 0.368–0.819) displayed relatively low and inconsistent agreement. Conclusion Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.

Low-dose high-resolution chest CT in adults with cystic fibrosis: intraindividual comparison between photon-counting and energy-integrating detector CT

BackgroundRegular disease monitoring with low-dose high-resolution (LD-HR) computed tomography (CT) scans is necessary for the clinical management of people with cystic fibrosis (pwCF). The aim of this study was to compare the image quality and radiation dose of LD-HR protocols between photon-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) in pwCF.MethodsThis retrospective study included 23 pwCF undergoing LD-HR chest CT with PCCT who had previously undergone LD-HR chest CT with EID-CT. An intraindividual comparison of radiation dose and image quality was conducted. The study measured the dose-length product, volumetric CT dose index, effective dose and signal-to-noise ratio (SNR). Three blinded radiologists assessed the overall image quality, image sharpness, and image noise using a 5-point Likert scale ranging from 1 (deficient) to 5 (very good) for image quality and image sharpness and from 1 (very high) to 5 (very low) for image noise.ResultsPCCT used approximately 42% less radiation dose than EID-CT (median effective dose 0.54 versus 0.93 mSv, p < 0.001). PCCT was consistently rated higher than EID-CT for overall image quality and image sharpness. Additionally, image noise was lower with PCCT compared to EID-CT. The average SNR of the lung parenchyma was lower with PCCT compared to EID-CT (p < 0.001).ConclusionIn pwCF, LD-HR chest CT protocols using PCCT scans provided significantly better image quality and reduced radiation exposure compared to EID-CT.Relevance statementIn pwCF, regular follow-up could be performed through photon-counting CT instead of EID-CT, with substantial advantages in terms of both lower radiation exposure and increased image quality.Key PointsPhoton-counting CT (PCCT) and energy-integrating detector system CT (EID-CT) were compared in 23 people with cystic fibrosis (pwCF).Image quality was rated higher for PCCT than for EID-CT.PCCT used approximately 42% less radiation dose and offered superior image quality than EID-CT.Graphical

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

The COVID-19 Pandemic's Effect on Preventive Imaging

This study assessed the effect of the COVID-19 pandemic on preventive care imaging and potential disparities because preventive care may be perceived as nonurgent. The objective was to identify the associations between the COVID-19 pandemic and changes in preventive imaging volumes for patients in general and as affected by race and ethnicities. The authors performed a retrospective observational study by extracting the weekly volumes of all imaging studies between January 7, 2019 and May 1, 2022 from a radiology data warehouse at a tertiary care medical center (n=92,105 preventive imaging studies and 3,493,063 total radiology imaging studies) and compared preshutdown with postshutdown periods using a 2-sample t-test. Additional comparisons stratified by race and ethnicity were performed for mammograms and bone density examinations using interrupted time series models with negative binomial error distribution to assess the immediate level change and trends over time of preventive imaging volumes after shutdown. The authors found a significant decrease in bilateral mammograms, bone density examinations, and aortic ultrasound examinations in the postshutdown period compared with those in the preshutdown period (p<0.001, p=0.003, and p=0.001, respectively). There were no significant changes in low-dose computed tomography chest examinations (p=0.46). The shutdown was associated with a significant immediate decrease in mammograms in the Hispanic and Latino group to approximately three quarters of its preshutdown level (p=0.04). For bone density examinations, postshutdown weekly volumes tripled compared with the preshutdown volumes for Hispanic or Latino patients and doubled for non-Hispanic Black patients (p<0.0001 for both). In comparison, a significant decrease was seen for Other patients (reduced to nearly a fifth of preshutdown volume) (p<0.0001). There was a significant decrease in overall volume of bilateral mammograms, bone density examinations, and aortic ultrasound examinations after pandemic shutdown. For mammograms, some of this decreasing trend was already seen before shutdown (such as for patients with Other race), so the shutdown was only specifically associated with an immediate decrease in mammogram levels in the Hispanic and Latino group. For bone density examinations, the shutdown was unexpectedly associated with an increase in volumes for Hispanic or Latino patients and non-Hispanic Black patients, which was countered by a significant steep decrease in volumes only for patients of Other race. Health systems should carefully explore their preventive screening examination volumes to determine which patients should be actively engaged to assure catch up on recommended care.

Automated Opportunistic Osteoporosis Screening Using Low-Dose Chest CT among Individuals Undergoing Lung Cancer Screening in a Korean Population.

Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021. Trabecular volumetric BMD of L1-2 was automatically calculated using DL and categorized according to the American College of Radiology quantitative computed tomography diagnostic criteria. BMD decreased with age in both men and women. Women had a higher peak BMD in their twenties, but lower BMD than men after 50. Among adults aged 50 and older, the prevalence of osteoporosis and osteopenia was 26.3% and 42.0%, respectively. Osteoporosis prevalence was 18.0% in men and 34.9% in women, increasing with age. Compared to previous data obtained using dual-energy X-ray absorptiometry, the prevalence of osteoporosis, particularly in men, was more than double. The automated opportunistic BMD measurements using LDCT can effectively predict osteoporosis for opportunistic screening and identify high-risk patients. Patients undergoing lung cancer screening may especially profit from this procedure requiring no additional imaging or radiation exposure.

In-training and private physicians' knowledge, attitudes, practices and perceived barriers on the utilization of low dose chest CT scan as lung cancer screening for high-risk patients in a tertiary hospital: Single center experience.

150 Background: Lung cancer is the leading cause of cancer-related deaths worldwide. Aside from smoking cessation efforts and guidelines from all proficient panels, utilizing Low dose CT scan (LDCT) of the chest as lung cancer screening (LCS) has been poorly adopted. Methods: This was conducted after institutional review board approval. The respondents were handed hard copy or electronic consent forms and survey questionnaire depending on the platform of their choice. Results: Majority of the respondents were in-training female physicians with median age of 31 years old who specialized in Internal Medicine practicing between 1-5 years who catered to more than 10 patients per week in outpatient clinics. Nearly half of the respondents answered that the recommended frequency for lung cancer screening (LCS) for high-risk individuals is done bi-annually. Majority agreed that lung cancer screening is effective in decreasing lung cancer mortality and morbidity. Most respondents have initiated discussion about the risks and benefits of low dose chest CT scan (LDCT) as lung cancer screening among eligible high-risk patients. However, most of the respondents disagreed that lung cancer screening is cost effective. The top three perceived barriers on the utilization of Low dose chest CT scan as lung cancer screening reported in descending order revealed that it is not cost-effective, some forgets to screen patients and lung cancer screening has the potential to cause mental anguish or anxiety to patients. Conclusions: This study recommends conducting further studies exploring the socio-economic burden and impact assessment of lung cancer in the Philippines to enable translation of more meaningful strategies for local and national lung cancer screening access, health policies and financing. Promoting continuous lung cancer screening forums, lectures and information dissemination of annual LDCT of the chest among high risk patients will encourage and strengthen interdepartmental and multidisciplinary referral and collaboration in increasing earlier detection of lung cancer and decreasing related morbidity and mortality.

Utility of block-matching and 3D filter for reproducibility of lung density and denoising in low-dose chest CT: A pilot study

PurposeThis study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma. MethodsUsing full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level. ResultsThe 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose = − 926.28 ± 0.32, BM3D = − 926.65 ± 0.32, and low-dose = − 959.43 ± 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05). ConclusionsThe results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.