Chest Chest Computed Tomography Scan Research Articles

Reporting breast density on chest computed tomography.

Women are encouraged to have a yearly mammogram and in addition to screening for breast cancer, the radiologist reports the patient's breast density. High breast density increases a woman's risk of developing breast cancer. The number of chest computed tomography (CT) scans performed each year is increasing. Chest CT scans for lung cancer screening in high-risk patients are the standard of care. Important additional findings can be identified on these exams including coronary artery calcifications, thyroid nodules, and breast density. Our previous research has shown that breast density can be reliably graded on chest CT and is comparable to mammographic grading. However, the inter-reader agreement was higher for chest CT. It is important that thoracic radiologists include the grading of breast density in their chest CT reports. According to mammography literature, this information has proven to be helpful for early detection of breast cancer. Federal legislation recommends notifying both providers and patients about breast density on mammography and so it follows that if we see the same information on chest CT, we should report it so that at the very least the clinician can encourage their patient to have a routine mammogram.

Artificial intelligence in the diagnosis of thoracic aortic aneurysms in a retrospective chest computed tomography scan analysis

BACKGROUND: Aortic aneurysms, known as silent killers, are frequently asymptomatic, leading to vessel rupture and death. Annual rates for ruptures, stratifications, and sudden deaths are up to 3.6%, 3.7%, and 10.8%, respectively. Timely diagnosis and early treatment save a patients life. The use of artificial intelligence (AI) technologies helps to detect aortic aneurysms, which significantly improves the quality of diagnosis and saves patients lives.
 AIM: To assess the efficiency of using AI technologies to detect thoracic aortic aneurysms on chest computed tomography (CT) and determining the possibility of using AI technologies as an assistant to the radiologist during the primary description of radiological images.
 METHODS: The study retrospectively assessed the results of AI technologies aimed at detecting thoracic aortic aneurysms on chest CT scans. No contrast enhancement was performed primarily. The sample consisted of 84,405 observations of patients over the age of 18 years; of these, 86 scans with thoracic aortic aneurysms were selected according to AI data. The selected examinations were retrospectively assessed by radiologists and vascular surgeons for the probable presence of a thoracic aortic aneurysm. In 44 cases, an aortic aneurysm was initially detected by the radiologist. In 31 cases, an aneurysm was not initially described by the radiologist, 6 were excluded from the sample (due to the absence of the radiologists report in the Unified Radiological Information Service), and 5 scans had false-positive results according to AI findings.
 RESULTS: The use of AI technologies allows detection and labeling of pathological changes in the aorta on the images. AI technologies increase the detectability of thoracic aortic aneurysms in the description of chest CT scans by 38.8%. The incidence of ascending aortic aneurysm was 0.3%, which corresponded to the literature data of 0.16%1.6%. According to the results, 22 surgical interventions for aortic stenting were performed.
 CONCLUSIONS: The use of AI in the primary chest CT description may help increase the detectability of clinically significant pathological conditions, such as thoracic aortic aneurysm. Further development of routing for this category of patients in the cito mode for surgical treatment is relevant. Expansion of retrospective screening by chest CT scans using AI systems will improve the quality of diagnosis of concomitant pathologies and prevent adverse outcomes for patients.

Comparison of the duration of generating radiological protocols with keyboard and voice input

BACKGROUND: Speech recognition is becoming increasingly common in the national healthcare system. One of the first specialties to implement this technology on a large scale was radiology. However, the efficiency of voice input and its effect on the length of time required to complete medical records remain unresolved.
 AIM: To assess the efficiency of speech recognition in generating radiological protocols of different modalities and types.
 METHODS: The retrospective study was conducted at the Moscow Reference Center of the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Department of Health. A total of 12,912 radiological reports on fluorography, mammography, chest computed tomography (CT), contrast-enhanced magnetic resonance imaging (MRI) of the brain, and contrast-enhanced CT of the abdomen and pelvis were included in the study by simple random sampling. The size of all samples exceeded 766 reports, calculated with regard to the size of the general population of over 100,000 reports. The Voice2Med software was used to fill in the radiological protocols. Intergroup comparison was performed using the MannWhitney U-test with a statistical significance level of 0.05.
 RESULTS: The average duration of generating fluorographic protocols in the keyboard and voice input groups was 189.9 s (0:03:09) and 236.2 s (0:03:56), respectively (p 0.0001). For mammographic reports, the duration was 387.1 s (0:06:27) and 444.8 s (0:07:24), respectively (p 0.0001). For radiographic reports, it amounted to 247.8 s (0:04:07) and 189.0 s (0: 03:09), respectively (p 0.0001), and for chest CT, it was 379.7 s (0:06:19) and 382.7 s (0:06:22), respectively (p=0.12). For MRI of the brain, the protocols were generated for 709.9 s (0:11:49) and 559.9 s (0: 09:19), respectively (p 0.0001), and for contrast-enhanced chest, abdominal, and pelvic CT scans, it took 2714.6 s (0:45:15) and 1778.4 s (0:29:38), respectively. Voice input slowed down the preparation time of mammographic and fluorographic protocols. This is due to the use of a structured electronic medical document in medical facilities to describe the results of the examinations. Speech recognition showed the greatest efficiency in generating MRI and CT protocols. Such reports contain a large number of pathological changes, both target and incidental findings, which requires a detailed description by the radiologist in the examination protocol.
 CONCLUSIONS: Speech recognition in generating radiological protocols showed different efficiency depending on the modality and type of the radiological protocol filled in using the voice input system. This approach is optimal for describing CT and MRI scans.

Could initial CT chest manifestation in patients hospitalized with COVID 19 pneumonia predict outcome on short term basis.

Chest computed tomography (CT) can be used to monitor the course of the disease or response to therapy. Therefore, our study was designed to identify chest CT manifestations that can predict the outcome of patients on short term follow-up. This was a retrospective study wherein we reviewed chest CT scans of 112 real-time reverse transcription polymerase chain reaction positive patients admitted to our hospital. All 112 patients underwent follow-up chest CT at a time interval of 4 to 42 days. Our study included 83 male and 29 female who were positive for COVID 19 infection and admitted to the hospital with positive chest CT findings. All patients underwent follow-up chest CT, and the outcomes were categorized as resolution, regression, residual fibrosis, progression, or death. These proportions were 5.4%, 48.2%, 24.1%, 14.3%, and 8%, respectively. The only significant factor in determining the complete resolution of chest CT was oligo-segmental affection (P = .0001). The main CT feature that significantly affected the regression of chest CT manifestations was diffuse nodular shadows (P = .039). The CT features noted in patients with residual fibrosis were interstitial thickening, with a P value of .017. The mono-segmental process significantly affected progression (P = .044). The significant factors for fatality were diffuse crazy paving, pleural effusion, and extra-thoracic complications (P = .033, .029, and .007, respectively). The prognostic value of the first admission CT can help assess disease outcomes in the earliest phases of onset. This can improve resource distribution.

Coronary artery calcium identified on non-gated chest CT scans - A wasted opportunity to avoid the tragedy

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Coronary artery calcium (CAC) is an independent predictor of cardiovascular events. While it is traditionally performed utilizing gating with specific acquisition parameters, CAC can be identified in non-gated standard chest computed tomography (CT). This study aimed to assess CAC on chest CTs, evaluating its correlation with coronary lesions on coronary angiography (CAG) and prognosis. Methods We retrospectively reviewed patients (pts) who underwent CAG due to acute coronary syndrome (ACS) who had undergone a prior non-gated non-contrast chest CT. CAC was qualitatively evaluated by visual assessment (mild/moderate/severe) and quantitatively assessed using Agatson score and stratified by terciles. Evaluation was performed by an investigator blinded to CAG report. Results We included 114 pts after reviewing 1000 CAGs: 67% male, mean age 68 years, 78% hypertension, 62% dyslipidemia, 38% chronic kidney disease, 38% diabetes. The mean time difference between CT and CAG was 23 months. CAG was performed due to unstable angina in 33% of pts, NSTEMI in 52% and STEMI in 16%. Significant lesions were found in 57% (69% performed PCI and 17% surgical revascularization). CAC was visual classified as mild, moderate and severe in 31%, 33% and 16% of pts, respectively. Moderate or severe CAC was an independent predictor of significant lesions on CAG [OR 22, 95%CI 8–61, p<0.001] and all-cause mortality [OR 4, 95%CI 2–9, p = 0.001]. Pts with severe CAC had higher peak troponin than those with mild/moderate CAC (1780 vs 315 ng/L, p = 0.024). Quantitative CAC score accurately predicted significant lesions (AUC 0.81, p<0.001; figure 1A), with higher scores in this subgroup (1308 vs 120, p<0.001) and strongly correlated with SYNTAX score (p<0.001). Survival analysis stratified by severity of CAC assessment is shown in figure 1B and 1C. The most severely calcified artery in the CT often matched the culprit vessel of future ACS, with 79%, 60% and 50% concordance for left anterior descending, circumflex, and right coronary artery, respectively. While significant CAC was identified in 80% of CTs, formal reporting was as low as 25%, even with severe CAC, where only 2/18 reports mentioned it. Furthermore, only 62% pts were on statin therapy at the time of CAG. Conclusion CAC evaluation in chest CTs was feasible and strongly associated with the extent/severity of coronary artery disease on CAG, as well as mortality. Notwithstanding, CAC underreporting was frequent and statin therapy underused, suggesting a simple and common opportunity for preventive care.

COVID-19 diagnostic prediction on chest CT scan images using hybrid quantum-classical convolutional neural network

Notwithstanding the extensive research efforts directed towards devising a dependable approach for the diagnosis of coronavirus disease 2019 (COVID-19), the inherent complexity and capriciousness of the virus continue to pose a formidable challenge to the precise identification of affected individuals. In light of this predicament, it is essential to devise a model for COVID-19 prediction utilizing chest computed tomography (CT) scans. To this end, we present a hybrid quantum-classical convolutional neural network (HQCNN) model, which is founded on stochastic quantum circuits that can discern COVID-19 patients from chest CT images. Two publicly available chest CT image datasets were employed to evaluate the performance of our model. The experimental outcomes evinced diagnostic accuracies of 99.39% and 97.91%, along with precisions of 99.19% and 98.52%, respectively. These findings are indicative of the fact that the proposed model surpasses recently published works in terms of performance, thus providing a superior ability to precisely predict COVID-19 positive instances. Communicated by Ramaswamy H. Sarma

Characteristics and Risks of COVID-19 in Pregnant Women & Neonates: A Retrospective, Age-matched Comparison Study—Wuhan Experience in the Management of Peripartum Health

ABSTRACT Background: Coronavirus disease 2019 (COVID-19) has since spread globally causing the ongoing pandemic. However, there are few studies on the symptoms and outcomes of pregnant women with COVID-19 and their newborns. Limited data are available comparing pregnant women with and without COVID-19 pneumonia. Our study aimed to find symptoms unique in pregnancy to help the early detection of COVID-19 in pregnant women and to evaluate its impact on perinatal women and their newborns. Methods: Clinical data were reviewed and collected for 11 pregnant women with laboratory-confirmed COVID-19 (pregnant confirmed cohort) who were consecutively admitted to Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province, from January 26 to February 26, 2020. They were subsequently matched by age with 11 pregnant women suspected of COVID-19 (pregnant suspected group) who had typical changes in chest computed tomography (CT) scans but were repeatedly negative for reverse-transcriptase-polymerase-chain-reaction (RT-PCR), with 11 pregnant women who were not infected (pregnant non-infected group), and with 44 non-pregnant women with confirmed COVID-19 (Non-pregnant confirmed group). Results: The significant symptoms in pregnant confirmed women were mild fever and fatigue. Both the confirmed and suspected women had a fever (45.5% vs. 72.7%, P > 0.05) which were barely distinguishable. The chest CT scans of confirmed women showed unspecific manifestations, such as bilateral pleural effusions and slightly increased densities. Eight of eleven (72.7%) confirmed women did not feel anything unusual until abnormalities were found on chest CT scans on the admission screening test. All three groups had elevated white blood cell count, neutrophil count, lactate dehydrogenase, and reduced total protein. Compared to the normal group, women in the confirmed group had significantly reduced hemoglobin, lymphocyte, albumin, and increased lactate dehydrogenase (P < 0.05). The infection did not increase the risk of premature delivery, premature rupture of the membrane, or comorbidities in pregnancy. The neonates of the three groups of mothers had similar symptoms, except for those of the confirmed group had increased heart rate (130–143 bpm) and mean arterial pressure (93–112 mmHg) than those born to the normal group. The infection of the mother did lead to more complications in their neonates. And there was no evidence of vertical transmission during late pregnancy. Conclusions: Pregnant women were often asymptomatic and had incidental abnormal findings on a chest CT scan on admission. Screening of CT scans in disease prevalent areas is thus of crucial importance. Even after the laboratory confirmation, the manifestation of the CT scan could be atypical, which alerted the necessity of protection for healthcare workers. COVID-19 did not increase the risk of complications in pregnant women and their neonates.

Role of chest computed tomography in the diagnosis of SARS-CoV-2 pneumonia in children

Background: First PCR-confirmed COVID-19 cases among Russian children were reported in early March 2020. Chest computed tomography (CT) is the most sensitive imaging modality in diagnosing early signs of viral lung damage. Limited data are available on clinical and laboratory features of COVID-19 in children and on their correlation with imaging findings. Studying COVID-19 features in children is crucial for modern medicine, which makes our research relevant.Objective: To determine the diagnostic efficacy and correlation between laboratory, clinical, and CT findings in children and adolescents with COVID-19.Materials and methods: From February 2020 to February 2021 372 patients with suspected COVID-19 were admitted to Morozov Children’s City Clinical Hospital (Moscow, Russian Federation). This retrospective study included 158 patients who presented with positive oropharyngeal and nasopharyngeal swab results, IgM and IgG test results, changes in complete blood count, blood biochemistry and coagulation test results, clinical symptoms, and suspected SARS-CoV-2 pneumonia based on chest CT findings.Results: Chest CT sensitivity and specificity for diagnosis of SARS-CoV-2 pneumonia were up to 98% and 85%, respectively. We considered that severe respiratory motion artifacts in young children under general anesthesia adversely affected the diagnostic efficacy. Sensitivity and specificity in this group were up to 89% and 71%, respectively.Conclusions: Viral pneumonia signs on chest CT scans were statistically significantly more frequent in children with cough, respiratory failure signs, lymphopenia, and increased D-dimer and C-reactive protein levels. One of these signs or a combination of them are an indication for chest CT in children with suspected SARS-CoV-2 pneumonia.

Impact of Elexacaftor-Tezacaftor-Ivacaftor on lung disease in cystic fibrosis.

In people with cystic fibrosis (pwCF), the impact of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies, such as Elexacaftor-Tezacaftor-Ivacaftor (ETI), on structural changes in the lungs is unclear. To determine the impact of ETI on clinical parameters and on structural lung disease as measured by the changes in the chest computed tomography (CT) scans in pwCF. Percent predicted forced expiratory volume in one second(ppFEV1), body mass index (BMI), and microbiologic data were collected at initiation and 3-month intervals for 1 year. Chest CT scans before starting ETI therapy (baseline) and at 1-year on ETI therapy were compared by two pulmonologists independently. The sample size was 67 pwCF, 30 (44.8%) males, median age of 25 (16, 33.5) years. Significant increases in ppFEV1 and BMI observed by 3 months of ETI therapy persisted throughout 1 year of ETI therapy (p < 0.001 at all-time points for both). After 1 year on ETI, pwCF had significant reductions in Pseudomonas aeruginosa (-42%) and MRSA (-42%) positivity. None of the pwCF had worsening of chest CT parameters during 1 year of ETI therapy. Comparing chest CT findings at baseline and at 1-year follow-up, bronchiectasis was present in 65 (97%) pwCF and at 1-year follow-up decreased in 7 (11%). Bronchial wall thickening 64 (97%), decreased in 53 (79%). Mucous plugging in 63 (96%), absent in 11 (17%), and decreased in 50 (77%). Hyperinflation/air trapping in 44 (67%), decreased in 11 (18%), absent in 27 (44%) CONCLUSIONS: ETI significantly improved clinical outcomes and lung disease as documented by improvement in chest CT scans.

Airway-Occluding Mucus Plugs and Mortality in Patients With Chronic Obstructive Pulmonary Disease

Airway mucus plugs are common in patients with chronic obstructive pulmonary disease (COPD); however, the association of airway mucus plugging and mortality in patients with COPD is unknown. To determine whether airway mucus plugs identified on chest computed tomography (CT) were associated with increased all-cause mortality. Observational retrospective analysis of prospectively collected data of patients with a diagnosis of COPD in the Genetic Epidemiology of COPD cohort. Participants were non-Hispanic Black or White individuals, aged 45 to 80 years, who smoked at least 10 pack-years. Participants were enrolled at 21 centers across the US between November 2007 and April 2011 and were followed up through August 31, 2022. Mucus plugs that completely occluded airways on chest CT scans, identified in medium- to large-sized airways (ie, approximately 2- to 10-mm lumen diameter) and categorized as affecting 0, 1 to 2, or 3 or more lung segments. The primary outcome was all-cause mortality, assessed with proportional hazard regression analysis. Models were adjusted for age, sex, race and ethnicity, body mass index, pack-years smoked, current smoking status, forced expiratory volume in the first second of expiration, and CT measures of emphysema and airway disease. Among the 4483 participants with COPD, 4363 were included in the primary analysis (median age, 63 years [IQR, 57-70 years]; 44% were women). A total of 2585 (59.3%), 953 (21.8%), and 825 (18.9%) participants had mucus plugs in 0, 1 to 2, and 3 or more lung segments, respectively. During a median 9.5-year follow-up, 1769 participants (40.6%) died. The mortality rates were 34.0% (95% CI, 32.2%-35.8%), 46.7% (95% CI, 43.5%-49.9%), and 54.1% (95% CI, 50.7%-57.4%) in participants who had mucus plugs in 0, 1 to 2, and 3 or more lung segments, respectively. The presence of mucus plugs in 1 to 2 vs 0 and 3 or more vs 0 lung segments was associated with an adjusted hazard ratio of death of 1.15 (95% CI, 1.02-1.29) and 1.24 (95% CI, 1.10-1.41), respectively. In participants with COPD, the presence of mucus plugs that obstructed medium- to large-sized airways was associated with higher all-cause mortality compared with patients without mucus plugging on chest CT scans.

Diagnostic follow-up of indeterminate pulmonary nodules in the Medicare population.

Management of indeterminate pulmonary nodules (IPNs) is associated with redistribution of lung cancer to earlier stages, but most subjects with IPNs do not have lung cancer. The burden of IPN management in Medicare recipients was assessed. Surveillance, Epidemiology, and End Results-Medicare data were analyzed for IPNs, diagnostic procedures, and lung cancer status. IPNs were defined as chest computed tomography (CT) scans with accompanying International Classification of Diseases (ICD) codes of 793.11 (ICD-9) or R91.1 (ICD-10). Two cohorts were defined: persons with IPNs during 2014-2017 comprised the IPN cohort, whereas those with chest CT scans without IPNs during 2014-2017 comprised the control cohort. Excess rates of various procedures due to reported IPNs over 2 years of follow-up (chest CT, positron emission tomography [PET]/PET-CT, bronchoscopy, needle biopsy, and surgical procedures) were estimated using multivariable Poisson regression models comparing the cohorts adjusted for covariates. Prior data on stage redistribution associated with IPN management were then used to define a metric of excess procedures per late-stage case avoided. Totals of 19,009 and 60,985 subjects were included in the IPN and control cohorts, respectively; 3.6% and 0.8% had lung cancer during follow-up. Excess procedures per 100 persons with IPNs over a 2-year follow-up were 63, 8.2, 1.4, 1.9, and 0.9 for chest CT, PET/PET-CT, bronchoscopy, needle biopsy, and surgery, respectively. Corresponding excess procedures per late-stage case avoided were 48, 6.3, 1.1, 1.5, and 0.7 based on an estimated 1.3 late-stage cases avoided per 100 IPN cohort subjects. The metric of excess procedures per late-stage case avoided can be used to measure the benefits-to-harms tradeoff of IPN management.

C1 COMPLETE HEART BLOCK AS A RARE MANIFESTATION OF GRANULOMATOSIS WITH POLYANGITIS: A CASE REPORT

Abstract A 42–year–old male with prior smoking habit presented at our emergency department after atraumatic syncope preceded by severe pain in the left lower limb. One month prior to presentation, due to persistence of nonproductive cough he underwent a chest computed tomography (CT), which showed 2 pulmonary nodules with hypercaptation at PET performed later (figure 1). The 12–lead electrocardiogram showed sinus rhythm with complete atrioventricular block and ventricular escape rhythm at 40 bpm (figura 1). Ecofast revealed a mildly impaired left ventricular systolic function (left ventricular ejection fraction [LVEF] 45%) with basal septal hypokinesis. Laboratory assessment showed leukocytosis (marked neutrophilia and eosinophilia), thrombocytosis anemia, elevated C–reactive protein (193 mg/dl), troponin I (18483 ng/L), NTproBNP (14627 ng/l) and creatinine (2.27 mg/dL) levels. The patient was transported to the catheterization laboratory for temporary pacemaker insertion and coronary angiography, which depicted distal occlusion of the first septal branch (Figure 2A), the first diagonal branch (Figure 2B) and the obtuse marginal branch (Figure 2C). After the procedure due to persistent ischemia of the left lower limb, several unsuccessful procedures to restore limb vascularization by means of locoregional thrombolysis and percutaneous angioplasty were attempted, but a leg amputation above the knee was eventually performed after few days. Neoplastic markers, blood cultures and urine cultures resulted negative, urine sediment examination showed cylinders, leucocytes and microhematuria. A brain/facial bones CT scan was performed, showing left maxillary sinusitis (figure 1). A rheumatologic screening was also performed, showing high titer of cANCA(125 UI/ml) hinting at a systemic vasculitis. The histopathological results of the amputated limb depicted marked necrotizing vasculitis with extensive involvement of small–medium caliber and larger caliber arterial vessels (figure 2). Based on these clinical and laboratory findings, a diagnosis of granulomatosis with polyangiitis (GPA) was made. The patients was treated with ev methylprednisolone and rituximab. After the induction therapy, chest CT scan showed a marked reduction of the size of the pulmonary nodule (Figure 3); renal function progressively improved and physiological atrioventricular conduction being restored

Tumor-associated prognostic factors extractable from chest CT scans in patients with lung cancer.

Accurately predicting the prognosis of patients with lung cancer before or at the time of treatment would offer clinicians an opportunity to tailor management plans more precisely to individual patients. Considering that chest computed tomography (CT) scans are universally acquired in patients with lung cancer for clinical staging or response evaluation, fully extracting and utilizing the prognostic information embedded in this modality would be a reasonable approach. Herein, we review tumor-related prognostic factors that are extractable from CT scans, including the tumor dimensions, presence of ground-glass opacity (GGO), margin characteristics, tumor location, and deep learning-based features. Tumor dimensions include diameter and volume, which are among the most potent prognostic factors in lung cancer. In lung adenocarcinomas, the solid component size on CT scans as well as the total tumor size is associated with the prognosis. The areas of GGO indicate the lepidic component and are associated with better postoperative survival in early-stage lung adenocarcinomas. As for the margin characteristics, which represent the CT manifestation of fibrotic stroma or desmoplasia, tumor spiculation should be evaluated. The tumor location in the central lung is associated with occult nodal metastasis and is a worse prognostic factor per se. Last but not least, deep learning analysis enables prognostic feature extraction beyond the human eyes.

The effect of CFTR modulators on structural lung disease in cystic fibrosis.

Background: Newly developed quantitative chest computed tomography (CT) outcomes designed specifically to assess structural abnormalities related to cystic fibrosis (CF) lung disease are now available. CFTR modulators potentially can reduce some structural lung abnormalities. We aimed to investigate the effect of CFTR modulators on structural lung disease progression using different quantitative CT analysis methods specific for people with CF (PwCF). Methods: PwCF with a gating mutation (Ivacaftor) or two Phe508del alleles (lumacaftor-ivacaftor) provided clinical data and underwent chest CT scans. Chest CTs were performed before and after initiation of CFTR modulator treatment. Structural lung abnormalities on CT were assessed using the Perth Rotterdam Annotated Grid Morphometric Analysis for CF (PRAGMA-CF), airway-artery dimensions (AA), and CF-CT methods. Lung disease progression (0-3years) in exposed and matched unexposed subjects was compared using analysis of covariance. To investigate the effect of treatment in early lung disease, subgroup analyses were performed on data of children and adolescents aged <18years. Results: We included 16 modulator exposed PwCF and 25 unexposed PwCF. Median (range) age at the baseline visit was 12.55 (4.25-36.49) years and 8.34 (3.47-38.29) years, respectively. The change in PRAGMA-CF %Airway disease (-2.88 (-4.46, -1.30), p = 0.001) and %Bronchiectasis extent (-2.07 (-3.13, -1.02), p < 0.001) improved in exposed PwCF compared to unexposed. Subgroup analysis of paediatric data showed that only PRAGMA-CF %Bronchiectasis (-0.88 (-1.70, -0.07), p = 0.035) improved in exposed PwCF compared to unexposed. Conclusion: In this preliminary real-life retrospective study CFTR modulators improve several quantitative CT outcomes. A follow-up study with a large cohort and standardization of CT scanning is needed to confirm our findings.

Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation Using Error-Aware Unified Semisupervised and Active Learning

As the coronavirusdisease 2019 (COVID-19) pandemic continues, fast and automatic COVID-19-related pneumonia lesion segmentation method is in an urgent need. The current state-of-the-art methods for segmentation generally require sufficient amounts of annotated data for training. However, human expert annotation of such lesion on chest computed tomography (CT) scans is time-consuming and labor-intensive due to its heterogeneous appearance, ambiguous boundary, and large number of slices in 3-D CT images. Therefore, the purpose of this study is to present a novel annotation-efficient learning method for COVID-19 pneumonia lesion segmentation on CT. To make the best use of limited human expert annotation resources, we propose an error-aware unified semisupervised and active learning method. A novel error estimation network is proposed to estimate a voxelwise segmentation loss map, which is used to guide learning from unlabeled data for semisupervised learning and choose the most informative images to annotate next for active learning. Validation is carried out on segmenting pneumonia lesions in 110 chest CT scans. The experimental result demonstrates that the proposed method significantly boosts the segmentation accuracy given limited amount of human annotation, compared with a conventional fully supervised baseline (60.9% Dice to 72.0% at 30% labeled data). The performance is also competitive compared with other state-of-the-art annotation-efficient segmentation methods. The proposed method can significantly reduce the annotation effort needed to achieve accurate COVID-19 pneumonia lesion segmentation.

Interstitial lung abnormalities: Real‐world evidence

Interstitial lung abnormalities: Real‐world evidence

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.

Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction. We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans areavailable. A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluationmetrics. Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively. Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLCimage.

Feasibility of Simple Evaluation of Coronary Artery Calcium Using a Number of Chest Computed Tomography Slices: A Multicenter Study

Objective: We aimed to evaluate the visual measurements of coronary artery calcium (CAC) on nonelectrocardiogram (ECG)-gated chest computed tomography (CT) using a simple scoring method that involves counting the number of CT slices containing CAC. Materials and Methods: We analyzed 163 participants who underwent both coronary and chest CT examinations at six centers within 3 months. Agatston scores were calculated on standard ECG-gated scans and classified as none (0), mild (1-99), moderate (100-400), or severe (>400). Next, chest CT images were reconstructed to standard 5.0 mm axial slices. Then, CAC on chest CT scans was measured using two methods: the Weston score (sum of the assigned score of each vessel, range: 0-12) and number of slices showing CAC (Ca-slice#). Results: When the Weston score and Ca-slice# were divided into four levels according to the optimal divisional levels corresponding to the Agatston score classes, good agreements with the 4-grade Agatston score were observed (kappa value=0.610 and 0.794, respectively). The sensitivity and specificity of Ca-slice# ≥9 to identify severe Agatston scores of >400 were 86% and 96%, respectively. Conclusion: The Ca-slice#, a simple scoring method using chest CT scans, was in good agreement with the ECG-gated Agatston score.

Fully automated radiologic identification focusing on the sternal bone

A crucial task in forensic investigations is the identification of unknown deceased. In general, secure identification methods rely on a comparison of ante mortem (AM) with post mortem (PM) data. However, available morphologic approaches are often dependent on the expertise and experience of the examiner, and often lack standardisation and statistical evidence. The objective of this study was therefore to overcome the current challenges via developing a fully automated radiologic identification (autoRADid) method based on the sternal bone. An anonymised AM data set consisting of 91 chest computed tomography (CT) scans, as well as an anonymised PM data set of 42 chest CT scans were included in this work. Out of the 91 available AM CT data sets, 42 AM scans corresponded to the 42 PM CT scans. For the fully automated identification analysis, a custom-made python pipeline was developed, which automatically registers AM data to the PM data in question using a two-step registration method. To evaluate the registration procedure and subsequent identification success, the image similarity measures Jaccard Coefficient, Dice Coefficient, and Mutual Information were computed. The highest value for each metric was retrieved in order to analyse the correspondence between AM and PM data. For all three similarity measures, 38 out of the 42 cases were matched correctly. This corresponds to an accuracy of 91.2%. The four unsuccessful cases incorporated surgical interventions taking place between the AM and the PM CT acquisition or poor CT scan quality preventing robust registration results. To conclude, the presented autoRADid method seems to be a promising fully automated tool for a reliable and facile identification of unknown deceased. A final pipeline combining all three similarity measures is open source and publicly available for efficient future identifications of unknown deceased.

EPIPERICARDIAL FAT NECROSIS.

Epipericardial fat necrosis (EFN) is a relatively rare benign disease that causes acute chest pain. A woman in her 20s presented with acute left shoulder and epigastric pain. One day before presentation, she had visited a cardiologist and an acute coronary syndrome had been ruled out. The pain worsened with deep inspiration. Chest computed tomography (CT) showed a soft-tissue attenuation lesion containing a fatty centre located in the epipericardial fat at the left cardiophrenic angle. Hence, EFN was diagnosed and the pain was resolved with loxoprofen. The lesion had disappeared on a follow-up chest CT scan. EFN is a rare benign disease that causes acute chest pain. Approximately 70-90% of EFN cases are misdiagnosed by clinicians as other diseases. In patients with acute chest pain, the correct diagnosis of EFN avoids unnecessary invasive investigations and reassures patients. Patients with epipericardial fat necrosis typically present with acute pleural chest pain without any associated symptoms.Characteristic CT findings of the encapsulated fatty pericardial lesion with a surrounding inflammatory reaction are key for the diagnosis of epipericardial fat necrosis.The correct diagnosis of epipericardial fat necrosis in patients with acute chest pain reassures them and avoids unnecessary invasive investigation.