Chest Chest Computed Tomography Examinations Research Articles

Opportunistic Assessment of Hip Fracture Risk Based on Chest CT.

Hip fracture (HF) has been described as the "last fracture of life" in the elderly, so the assessment of HF risk is extremely important. Currently, few studies have examined the relationship between imaging data from chest computed tomography (CT) and HF. This study demonstrated that pectoral muscle index (PMI) and vertebral body attenuation values could predict HF, aiming to opportunistically assess the risk of HF in patients without bone mineral density (BMD) based on chest CT for other diseases. In the retrospective study, 800 participants who had both BMD and chest CT were enrolled from January 2021 to January 2024. After exclusion, 472 patients were finally enrolled, divided into the healthy control (HC) group and the HF group. Clinical data were collected, and differences between the two groups were compared. A predictive model was constructed based on the PMI and CT value of the fourth thoracic vertebra (T4HU) by logistic regression analysis, and the predictive effect of the model was analyzed by using the receiver operating characteristic (ROC) curve. Finally, the clinical utility of the model was analyzed using decision curve analysis (DCA) and clinical impact curves. Both PMI and T4HU were lower in the HF group than in the HC group (p < 0.05); low PMI and low T4HU were risk factors for HF. The predictive model incorporating PMI and T4HU on the basis of age and BMI had excellent diagnostic efficacy with an area under the curve (AUC) of 0.865 (95% confidence interval [CI]: 0.830-0.894, p < 0.01), sensitivity and specificity of 0.820 and 0.754, respectively. The clinical utility of the model was validated using calibration curves and DCA. The AUC of the predictive model incorporating BMD based on age and BMI was 0.865 (95% CI: 0.831-0.895, p < 0.01), with sensitivity and specificity of 0.698 and 0.711, respectively. There was no significant difference in diagnostic efficacy between the two models (p = 0.967). PMI and T4HU are predictors of HF in patients. In the absence of dual-energy x-ray absorptiometry (DXA), the risk of HF can be assessed by measuring the PMI and T4HU on chest CT examination due to other diseases, and further treatment can be provided in time to reduce the incidence of HF.

Comparison of the equivalent doses of the eye lenses, thyroid, and mammary gland among three pediatric and one adult anthropomorphic phantom during the chest CT examinations using a 40mm volume helical scan.

Equivalent doses for the eye lenses, thyroid, and mammary glands were measured and compared between one adult and three pediatric anthropomorphic phantoms during chest computed tomography (CT) using 40mm volume helical scan on the Aquilion ONE GENESIS Edition CT equipment. Placing an optically stimulated luminescence dosemeter (OSLD) on the eye lenses, thyroid, and mammary gland, we measured and compared the equivalent dose of OSLD among different phantoms during chest CT using a helical scan. Compared with adults, the equivalent doses to the eye lens, thyroid, and mammary glands were ~81%, 77%, and 63% lower in newborns, 1-year-olds, and 5-year-olds using comparable image noise during chest CT.

Subchronic particulate matter exposure underlying polyhexamethylene guanidine phosphate–induced lung injury: Quantitative and qualitative evaluation with chest computed tomography

Our study was to explore the effects of subchronic particulate matter (PM) exposure on lung injury induced by polyhexamethylene guanidine phosphate (PHMG-p) in a rat model. Specifically, we investigated pulmonary inflammation, fibrosis, and tumor formation using chest computed tomography (CT), and histopathologic examination. PHMG-p was administered intratracheally to 20 male rats. After an initial week of PHMG-p treatment, the experimental group (PM group) received intratracheal administration of PM suspension, while the control group received normal saline. This regimen was continued for 10 weeks to induce subchronic PM exposure. Chest CT scans were conducted on all rats, followed by the extraction of both lungs for histopathological analysis. All CT images underwent comprehensive quantitative and qualitative analyses. Pulmonary inflammation was markedly intensified in rats subjected to subchronic PM exposure in the PM group compared to those in the control. Similarly, lung fibrosis was more severe in the PM group as observed on both chest CT and histopathologic examination. Quantitative chest CT analysis revealed that the mean lesion volume was significantly greater in the PM group than in the control group. Although the incidence of bronchiolo-alveolar hyperplasia was higher in the PM group compared to the control group, this difference was not statistically significant. In summary, subchronic PM exposure exacerbated pulmonary inflammation and fibrosis underlying lung injury induced by PHMG-p.

Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading.

The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysema grades in routine chest computed tomography (CT). Furthermore, optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or moderate to more extensive visual emphysema grades were calculated. In a retrospective study of 298 consecutive patients who underwent routine chest CT and spirometry examinations, LAV% was quantified using an AI-based software with a threshold < -950 HU. The FEV1/FVC was derived from spirometry, with FEV1/FVC < 70% indicating airway obstruction. The mean time interval of CT from spirometry was 3.87 ± 4.78 days. Severity of emphysema was visually graded by an experienced chest radiologist using an established 5-grade ordinal scale (Fleischner Society classification system). Spearman correlation coefficient between LAV% and FEV1/FVC was calculated. Receiver operating characteristic determined the optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or a visual emphysema grade of moderate or higher (Fleischner grade 3-5). Significant correlation between LAV% and FEV1/FVC was found (ϱ = -0.477, P < 0.001). Increasing LAV% corresponded to higher visual emphysema grades. For patients with absent visual emphysema, mean LAV% was 2.98 ± 3.30, for patients with trace emphysema 3.22 ± 2.75, for patients with mild emphysema 3.90 ± 3.33, for patients with moderate emphysema 6.41 ± 3.46, for patients with confluent emphysema 9.02 ± 5.45, and for patients with destructive emphysema 16.90 ± 8.19. Optimal LAV% cutoff value for predicting a FEV1/FVC < 70 was 6.1 (area under the curve = 0.764, sensitivity = 0.773, specificity = 0.665), while for predicting a visual emphysema grade of moderate or higher, it was 4.7 (area under the curve = 0.802, sensitivity = 0.766, specificity = 0.742). Furthermore, correlation between visual emphysema grading and FEV1/FVC was found. In patients with FEV1/FVC < 70% a high proportion of subjects had emphysema grade 3 (moderate) or higher, whereas in patients with FEV1/FVC ≥ 70%, a larger proportion had emphysema grade 3 (moderate) or lower. The sensitivity for visual emphysema grading predicting a FEV1/FVC < 70% was 56.3% with an optimal cutoff point at a visual grade of 4 (confluent), demonstrating a lower sensitivity compared with LAV% (77.3%). A significant correlation between AI-based LAV% and FEV1/FVC as well as visual CT emphysema grades can be found in routine chest CT suggesting that AI-based LAV% measurement might be integrated as an add-on functional parameter in the evaluation of chest CT in the future.

Survey on chest CT findings in COVID-19 patients in Okinawa, Japan: differences between the delta and omicron variants

To investigate the frequency of pneumonia and chest computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) during the fifth Delta variant-predominant and sixth Omicron variant-predominant waves of the COVID-19 pandemic in Okinawa, Japan. A survey on chest CT examinations for patients with COVID-19 was conducted byhospitals with board-certified radiologists who provided treatment for COVID-19 pneumonia in Okinawa Prefecture. Data from 11 facilities were investigated. Indications for chest CT; number of COVID-19 patients undergoing chest CT; number of patients with late-onset pneumonia, tracheal intubation, and number of deaths; and COVID-19 Reporting and Data System classifications of initial chest CT scans were compared by the chi-squared test between the two pandemic waves (Delta vs. Omicron variants). A total of 1944 CT scans were performed during the fifth wave, and 1178 were performed during the sixth wave. CT implementation rates, which were the number of patients with COVID-19 undergoing CT examinations divided by the total number of COVID-19 cases in Okinawa Prefecture during the waves, were 7.1% for the fifth wave and 2.1% for the sixth wave. The rates of tracheal intubation and mortality were higher in the fifth wave. Differences between the distributions of the CO-RADS classifications were statistically significant for the fifth and sixth waves (p < 0.0001). In the fifth wave, CO-RADS 5 (typical of COVID-19) was most common (65%); in the sixth wave, CO-RADS 1 (no findings of pneumonia) was most common (50%). The finding of “typical for other infection but not COVID-19” was more frequent in the sixth than in the fifth wave (13.6% vs. 1.9%, respectively). The frequencies of pneumonia and typical CT findings were higher in the fifth Delta variant-predominant wave, and nontypical CT findings were more frequent in the sixth Omicron variant-predominant wave of the COVID-19 pandemic in Okinawa, Japan.

An improvement of the computational effective diameter measurement in thoracic computed tomography examinations

A method to calculate a corrected effective diameter (DMIL) to more accurately estimate the dose received by a patient in chest computed tomography (CT) examination had been previously proposed. However, the discrepancy between DMIL and water-equivalent diameter (Dw) is still relatively high (i.e. about 6%). Furthermore, the method is still performed manually, so it is laborious and time-consuming. This study aims to improve the corrected effective diameter with bone correction (Deffcorr) and to automatically calculate it. The automated Deffcorr was calculated as the square root of the product of these corrected AP and LAT diameters. The approach was implemented on 30 patients who had undergone chest CT examination with the standard protocol. The results show that the correlation between the Deffcorr and Dw is R2=0.93 with no statistical difference (p&amp;gt;0.05). The automated Deffcorr is 3.1% lower than Dw. While the DMIL is 10.5% higher than Dw and both are statistically different (p&amp;lt;0.05). In conclusion, the new Deffcorr was introduced and the result obtained was closer to Dw than DMIL. This method is simple enough to be used as an alternative method to accurately estimate Dw for radiation dose estimation in clinical chest CT scanning.

The value of non-contrast chest CT in the prediction of myocardial injury in patients with the COVID-19 Omicron variant

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant associated myocardial injury seriously affected the patient's health. Chest computed tomography (CT) is an essential imaging diagnostic tool for evaluating lung diseases in these patients, but its value in the diagnosis of myocardial injury remains unknown. The purpose of this study was to evaluate the lung lesions in patients with Omicron infection with or without myocardial injury, and to evaluate the predictive value of non-contrast chest CT in such patients with myocardial injury. We enrolled 122 consecutive hospitalized patients with laboratory-confirmed coronavirus disease 2019 (COVID-19) for non-contrast chest CT examination. These patients were divided into two groups according to whether myocardial injury occurred. Myocardial injury was defined as a Troponin I level above the 99th-percentile upper reference limit (0.04 ng/mL). The imaging manifestations of the patients’ lungs were evaluated. Myocardial CT value, left atrium (LA) size, long diameter of left ventricular (LV), and cardiothoracic ratio (CTR) were recorded. Multivariate logistic analysis was performed to identify the predictive factors associated with myocardial injury. Of 122 patients, 61 patients (50%) had myocardial injury. Compared with patients without myocardial injury, there was worse NYHA class, more critical patients, higher incidence of bronchial meteorology, larger area and percentage of lung lesions, diameters of LA, and lower myocardial CT value in the myocardial injury group (P < 0.05). Troponin I concentration in patients with myocardial injury group showed negative correlation with myocardial CT value (r = − 0.319, P = 0.012). The multivariable logistic regression analysis showed that disease severity status (OR 2.279; 95% CI 1.247–4.165, P = 0.007), myocardial CT value (OR 0.849; 95% CI 0.752–0.958, P = 0.008), neutrophil count (OR 1.330; 95% CI 1.114–1.587, P = 0.002) were independent predictors of myocardial injury. The discrimination of the model was good (C-statistic = 0.845, 95% CI 0.775–0.914) and well calibrated with a Hosmer–Lemeshow test for goodness of fit (P = 0.476). Patients infected with Omicron with myocardial injury had more severe lung disease than those without myocardial injury. Non-contrast chest CT can be a useful method of detection of myocardial injury in Omicron infection patients.

Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS).

In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this study, the breast dose of 50 adult female patients who underwent chest CT examinations was measured directly by TLDs. Then, the ANFIS model was developed with four inputs including dose length product (DLP), volumetric CT dose index (CTDIvol), total mAs, and size-specific dose estimate (SSDE), and one output (TLD dose). Additionally, multiple linear regression (MLR) as a traditional prediction model was used for linear modeling and its results were compared with the ANFIS. The TLD reader results showed that the breast dose value was 12.37 ± 2.46mGy. Performance indices of the ANFIS model, including root mean square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 0.93 for the testing dataset, respectively. Also, the ANFIS model had superior performance in predicting the breast dose than the MLR model (R = 0.805). This study demonstrates that the proposed ANFIS model is efficient for patient dose prediction in CT scans. Therefore, intelligence models such as ANFIS are suggested to estimate and optimize patient dose in CT examinations.

Clinical Efficacy of the Integrated Medical Care Model Combined with Psychological Intervention in Tuberculosis Patients with Lung Cancer

Objective: To explore the clinical efficacy of the integrated medical care model combined with psychological intervention in pulmonary tuberculosis patients with lung cancer. Methods: From January to December 2022, 60 pulmonary tuberculosis patients with lung cancer admitted to our hospital were selected as the research subjects. Using the random number table method, the patients were divided into two groups, a control group and a study group, with 30 cases in each group. The chest computed tomography (CT) examination results, mental state assessment (including depression scale and anxiety scale) scores, incidence of adverse reactions, treatment effect, and length of hospital stay were compared between the two groups. Results: The treatment effect of the patients in the study group was better than that of the patients in the control group (P &lt; 0.05); the duration of hospitalization, chest CT examination results, mental state assessment scores, and incidence of adverse reactions of the study group and the control group, were significantly different (P &lt; 0.05). Conclusion: The integrated medical care model combined with psychological intervention can effectively improve the treatment effect of pulmonary tuberculosis patients with lung cancer and prevent the occurrence of adverse reactions; thus, it should be promoted in clinical practice.

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.

Image quality assessment in low-dose COVID-19 chest CT examinations.

Low-dose thoracic protocols were developed massively during the COVID-19 outbreak. To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols. COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models. In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ. Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.

Diagnostic Efficacy of Chest Computed Tomography for Coronavirus Disease 2019.

A significant discrepancy between the results of previous studies is identified regarding the diagnostic efficacy of chest computed tomography (CT) for coronavirus disease 2019 (COVID-19). We aimed to evaluate the diagnostic efficacy of chest CT for COVID-19. Suspected cases of COVID-19 with fever, cough, dyspnea, and evidence of pneumonia on chest CT scan were enrolled in the study. The accuracy, sensitivity, and specificity of chest CT were determined according to real-time reverse transcriptase-polymerase chain reaction (RT-PCR) results as the gold standard method. The study population comprised 356 suspected cases of COVID-19 (174 men and 182 women; age range 3-96 years; mean age ± standard deviation, 55.21 ± 18.38 years). COVID-19 patients were diagnosed using chest CT with 89.8% sensitivity, 78.1% accuracy, 21.3% specificity, 84.7% positive predictive value, and 30.23% negative predictive value. The odds ratio was 2.39 (95% confidence interval, 1.16-4.91). Typical CT manifestations of COVID-19 were observed in 48 (13.5%) patients with negative RT-PCR results and 30 (8.4%) patients with confirmed positive RT-PCR results had no radiological manifestations. Kappa coefficient of chest CT for diagnosis of COVID-19 was 0.78. The results show that when RT-PCR results are negative, chest CT could be considered as a complementary diagnostic method for the diagnosis of COVID-19 patients. A more comprehensive diagnostic method could be established by combining the chest CT examination, clinical symptoms, and RT-PCR assay.

Trade-off between breast dose and image quality using composite bismuth shields in computed tomography: A phantom study

IntroductionMany researchers have suggested that bismuth composite shields (BCS) reduce breast dose remarkably; however, the level of this reduction and its impact on image quality has not been assessed. This study aimed to evaluate the efficiency of nano- and micro- BCS in reducing the dose and image quality during chest computed tomography (CT) scans. Materials and methodsBismuth shields composed of 15 weighting percentage (wt%) and 20 wt% bismuth oxide (Bi2O3) nano- and micro-particles mixed in silicon rubber polymer were constructed in 1 and 1.5 mm thicknesses. The physical properties of nanoparticles were assessed using a scanning electron microscope (SEM), X-ray diffraction (XRD), and energy-dispersive X-ray (EDX). Breast radiation doses were measured experimentally during chest CT using PMMA standard dosimetry phantom (body phantom, 76-419-4150, Fluke Biomedical) in the presence of the shields. The image quality was assessed by calculating signal and noise values in different regions. ResultsThe SEM images showed that the average size of Bi2O3 nano- and micro-particles was about 70 nm and 150 μm, respectively. The breast doses were reduced by increasing the shield thickness/bismuth weight percentage. The maximum dose reduction was related to the 20% weight of Bi2O3 nano-particles and a thickness of 1.5 mm. The minimum dose reduction was related to the 15% weight of Bi2O3 micro-particles with a thickness of 1 mm. The mean noise was higher in nano-particle bismuth shields than in micro-particles. ConclusionComposite shields containing bismuth nano- and micro-particles can reduce the breast dose during chest CT examinations while negatively impacting diagnostic image quality. Several critical factors, such as bismuth concentration, particle size, and shield thickness, directly affect the efficiency.

Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer.

To assess the prediction performance of preoperative chest computed tomography (CT) based radiomics features for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2+), and Ki-67 status of breast cancer. This study enrolled 108 breast cancer patients who received preoperative chest CT examinations in our institution from July 2018 to January 2020. Radiomics features were separately extracted from nonenhanced, arterial, and portal-venous phases CT images. The least absolute shrinkage and selection operator logistic regression was used for feature selection. Then the radiomics signatures for each phase and a combined model of 3 phases were built. Finally, the receiver operating characteristic curves and calibration curves were used to confirm the performance of the radiomics signatures and combined model. In addition, the decision curves were performed to estimate the clinical usefulness of the combined model. The 20 most predictive features were finally selected to build radiomics signatures for each phase. The combined model achieved the overall best performance than using either of the nonenhanced, arterial and portal-venous phases alone, achieving an area under the receiver operating characteristic curve of 0.870 for ER+ versus ER-, 0.797 for PR+ versus PR-, 0.881 for HER2+ versus HER2-, and 0.726 for Ki-67. The decision curve demonstrated that the CT-based radiomics features were clinically useful. This study indicated preopreative chest CT radiomics analysis might be able to assess ER, PR, HER2+, and Ki-67 status of breast cancer. The findings need further to be verified in future larger studies.

The impact of vertical off-centering on image noise and breast dose in chest CT with organ-based tube current modulation: A phantom study

PurposeTo determine the effects of patient vertical off-centering when using organ-based tube current modulation (OBTCM) in chest computed tomography (CT) with focus on breast dose. Materials and methodsAn anthropomorphic adult female phantom with two different breast attachment sizes was scanned on GE Revolution EVO and Siemens Definition Edge CT systems using clinical chest CT protocols and anterior-to-posterior scouts. Scans with and without OBTCM were performed at different table heights (GE: centered, ±6 cm, and ± 3 cm; Siemens: centered, −6 cm, and ± 3 cm). The dose effects were studied with metal-oxidesemiconductor field-effect transistor dosimeters with complementary Monte Carlo simulations to determine full dose maps. Changes in image noise were studied using standard deviations of subtraction images from repeated acquisitions without dosimeters. ResultsPatient off-centering affected both the behavior of the normal tube current modulation as well as the extent of the OBTCM. Generally, both OBTCM techniques provided a substantial decrease in the breast doses (up to 30% local decrease). Lateral breast regions may, however, in some cases receive higher doses when OBTCM is enabled. This effect becomes more prominent when the patient is centered too low in the CT gantry. Changes in noise roughly followed the expected inverse of the change in dose. ConclusionsPatient off-centering was shown to affect the outcome of OBTCM in chest CT examination, and on some occasions, resulting in higher exposure. The use of modern dose optimization tools such as OBTCM emphasizes the importance of proper centering when preparing patients to CT scans.

French Imaging Database Against Coronavirus (FIDAC): A large COVID-19 multi-center chest CT database

PurposeDuring the first wave of the COVID-19 pandemic, the French Society of Radiology and the French College of Radiology, in partnership with NEHS Digital, have set up a system to collect chest computed tomography (CT) examinations with clinical, virological and radiological metadata, from patients clinically suspected of COVID-19 pneumonia. This allowed the constitution of an anonymized multicenter database, named FIDAC (French Imaging Database Against Coronavirus). The aim of this report was to describe the content of this public database. Materials and methodsTwenty-two French radiology centers participated to the data collection. The data collected were chest CT examinations in DICOM format associated with the following metadata: patient age and sex, originating facility identifier, originating facility region, time from symptom onset to CT examination, indication for CT examination, reverse transcription-polymerase chain reaction (RT-PCR) results and normalized CT report performed by a senior radiologist. All the data were anonymized and sent through a NEHS Digital system to a centralized data center. ResultsA total of 5944 patients were included from the 22 centers aggregated into 8 regions with a mean number of patients of 743 ± 603.3 [SD] per region (range: 102–1577 patients). Reasons for CT examination and normalized CT reports were provided for all patients. RT-PCR results were provided in 5574 patients (93.77%) with a positive result of RT-PCR in 44.6% of patients. ConclusionThe FIDAC project allowed the creation of a large database of chest CT images and metadata available, under conditions, in open access through the CERF-SFR website.

Radiological Findings on Chest Computed Tomography in Patients With the Primary Diagnosed Chronic Lymphoproliferative Diseases.

IntroductionThe presence of concomitant respiratory pathology complicates the process of treatment and recovery of patients with chronic lymphoproliferative diseases (CLDs). Therefore, the diagnosis of lung injury is an important step in the management of such patients.ObjectivesThe aim of this study was to determine the prevalence, nature, extent, and location of changes diagnosed by high-resolution chest computed tomography (CT) in patients with CLDs at the initial examination.MethodsMedical records of inpatients who were hospitalized in 2018-2019 to the City Hematology Center of the Public Non-Profit Enterprise “City Clinical Hospital #4” of Dnipro City Council with a confirmed clinical diagnosis of CLDs were included in the retrospective study. The results of initial high-resolution chest CT were studied and analyzed.ResultsOut of 1,004 hospitalized patients with confirmed CLDs, 119 patients were primarily diagnosed. Among them, 81 patients underwent chest CT examination (68.1%) before the beginning of specific therapy. The average age was 65 (56;68) years, 46 (56.8%) were men. 23 (28.4%) patients were diagnosed with chronic lymphocytic leukemia, 28 (34.6%) patients with multiple myeloma, 24 (29.6%) patients with lymphoma, and six patients (7.4%) had other CLDs. It was found that both central and peripheral lymphadenopathy had about a third of the studied cohort of patients (33.3 and 29.6%, respectively), and these symptoms dominated in patients with chronic lymphocytic leukemia (43.5 and 50%, respectively), lymphoma (50 and 52.2%, respectively), and other CLDs (45.8 and 16.7%, respectively), in contrast to patients with multiple myeloma (7.1 and 0%, respectively). Lesions of the lung parenchyma were found in 45.7% of the studied cohort and were met more often in patients with multiple myeloma (67.9%). However, when comparing the prevalence of their categories, no statistically significant differences were found. Predictable bone-destructive changes were statistically significantly more common in patients with multiple myeloma than in other groups of patients with CLDs (P=0.0003).ConclusionsSigns of pulmonary diseases during initial chest CT were found almost in half of the patients with CLDs. It potentially may affect the frequency of treatment complications in such patients. Lymphadenopathy was the most common finding on chest CT, especially in patients with chronic lymphocytic leukemia and lymphoma. And enlarged intrathoracic lymph nodes possibly could lead to pulmonary functions disorders. Among the types of lung parenchyma lesions, pneumofibrosis and foci of consolidation in the lower lung lobes were the most often diagnosed. Chest CT is informative at the stage of the initial examination of patients with CLDs not only for clinical diagnosis but also for the diagnosis of respiratory comorbidities and prediction of the disease outcome and treatment complications.

The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia.

Objective:To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test.Methods:In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort.Results:A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or “COVID-19 without virus detection”, as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%.Conclusion:DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making.Advances in knowledge:DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.

Clinical Classification of SARS-CoV-2 Positive Patients; Evaluation of Laboratory and Radiological Findings

Introduction: The sudden emergence and rapid spread of the Coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has dangered global health. For this reason, there has been an urgent need to develop strategies to control the spread of the virus. In this study, we aimed to evaluate the clinical, laboratory and chest computed tomography (CT) findings of COVID-19 cases with positive results of SARS-CoV-2 reverse transcription quantitative polymerase chain reaction (RT-qPCR). Materials and Methods: A total of 278 patients aged 18 years and over who had positive SARS-CoV-2 RT-qPCR results and had access to laboratory and chest CT examinations between 15 April 2020 and 31 August 2020 were included in the study. Clinical classification of patients was performed as asymptomatic, mild and severe. Demographic information, laboratory tests and chest CT results of the patients were created with the data obtained from the hospital system and the relevant branch physicians. The data were analyzed statistically. Results: In the study, 150 (54%) of the patients were males and 128 (46%) were females. Forty-two (15%) of these patients were classified as asymptomatic, 200 (72%) as mild and 36 (13%) as severe cases. Mean age of the patients was found to be 45.5 ± 17.5 years and 72 (25.9%) patients were found to have at least one comorbidity. Findings consistent with COVID-19 pneumonia were observed in the chest CT examinations of 42.4% (118/278) of the patients. The rate of findings in chest CT of severe patients (91.7%) was found higher than mild (38.5%) and asymptomatic cases (19%) (p< 0.05). When we examined the laboratory findings;59.4% of the patients had elevated CRP, 42.1% lymphopenia, 41.1% elevated LDH, 40.6% elevated creatinine, 36% elevated AST, 20.1% leukopenia, 8% elevated ALT, 7.2% elevated leukocyte, 5.4% elevated troponin I and 3.3% elevated CK-MB were detected. In severe patients, lymphopenia (83.3%), neutrophil elevation (41.7%), CRP elevation (97.2%), LDH elevation (91.3%), troponin I elevation (21.4%) and AST elevation (63.9%) findings were observed at a higher rate than asymptomatic and mild cases (p< 0.05). Conclusion: In our study, it was shown that the findings of high age, hypertension, diabetes mellitus, dyspnea, lymphopenia and the increase in neutrophil, CRP, LDH, troponin I and AST values were associated with severe clinical presentation. These findings are thought to be helpful in understanding the differences between clinical classes in COVID-19 patients.

Predictive value of chest computed tomography for axillary lymph node metastasis in patients with breast cancer: A retrospective cohort study

Purpose: This study aimed to evaluate the predictive value of preoperative chest computed tomography (CT) for axillary lymph node (ALN) metastasis in patients with breast cancer.Methods: CT features of ALNs were retrospectively reviewed in 212 patients with breast cancer who underwent preoperative chest CT examination and ALN dissection. Primary tumor size and CT characteristics of ALNs (cortical thickness, cortical shape, the presence or absence of contrast enhancement of ALNs, and the presence or absence of perinodal infiltration) were recorded and analyzed. A nomogram was developed to visualize the associations between the predictors and each ALN status endpoint.Results: Of 212 patients, 95 (44.8%) had ALN metastasis. Primary tumor size and CT characteristics of ALNs were identified as predictors of ALN metastasis. The nomogram comprising primary tumor size and cortical shape was a good validation model for predicting ALN metastasis. The sensitivity, specificity, and accuracy of the nomogram for predicting ALN metastasis were 88.4%, 79.5%, and 83.5%, respectively.Conclusion: Using preoperative chest CT scans, a nomogram combining the cortical shape of ALNs with the primary tumor size showed good performance in predicting ALN metastasis.