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Some of the viruses may cause lung parenchyma and airway involvement. Usually, viral pneumonia causes ground-glass opacities, bilateral peripheral distribution, consolidation, vascular thickening, and reticular opacity. These features are common in COVID-19 rather than Non-Covid-19 viral pneumonia. However, in advanced cases, COVID-19 viral pneumonia may cause organising pneumonia and fibrosis of the lung. Atypical findings of Non-Covid-19 pneumonia have included central peripheral distribution, pleural effusion, lymphadenopathy, nodules, tree-in-bud opacities, and pneumothorax. Therefore, differentiating Non-Covid-19 pneumonia from COVID-19 pneumonia at chest computed tomography (CT) is necessary. In that case, CT scans of the thorax are one of the essential tools for early identification and future prognosis of viral pneumonia. We have proposed a Computer-Aided Diagnostic (CAD) system that can detect features of chest CT using a Deep Neural Network (DNN) with Long Short-Term Memory (LSTM). Transfer learning using pre-trained DNN models (ResNet50, VGG19, InceptionV3, Xception, DenseNet121, and VGG16) is applied to retain both high-level and low-level features effectively. The deep features are passed to the LSTM layer. The LSTM is utilised as a classifier and detects long short-term dependencies. The proposed method employs a hybrid DNN-LSTM network for automatic detection to take advantage of the uniqueness of the two models. The proposed models are trained with common and different features present in the chest CT of COVID-19 and Non-Covid-19 viral pneumonia. The 5-fold cross-validation (CV) method validated and tested the proposed model. The proposed DNN model's performance is quite improved with LSTM and CV. As a result, the proposed LM-DNN (VGG16+LSTM+CV) model has achieved the classification test accuracy of 91.58% and specificity of 93.86%, which offers superior performance with state-of-the-art. Also, the DenseNet121+LSTM+CV model has reached the classification test accuracy of 90.1% and sensitivity of 92%. [ABSTRACT FROM AUTHOR]

Purpose: To describe the first experience of patient dose optimization in developing AQD, SSDE and image quality-related DRLs for common CT examinations in the adult age group using the concept of AQD. Materials and methods: The recent published IQSC from 0 to 4 were used by radiologists for the assessment of image quality. The entire data were collected for five types (brain CT, chest CT, chest HRCT, abdomen KUB CT and abdomen + pelvic CT) CT investigations based on anatomic region (head, chest and abdomen + pelvic). The entire datasets of 264 patients were categorized into three groups based on their weights: group-1 (41–60 kg), group-2 (61–80 kg) and group-3 (81–100 kg). Only score-3 images were considered to assess median and 75th percentile values of CTDIvol and DLP to obtain AQDs and DRLs, respectively. Results: Following the practical training of four radiologists on image quality scoring criteria for CT images, 264 (92%) out of 288 patient images were clinically acceptable as per IQSC for the study. The AQD (median) values in terms of CTDIvol for the mentioned weight groups were 25.8, 2.7, and 30.6 mGy, while the median DLP values for these groups were 496, 510 and 557 mGycm, respectively, for brain CT. The 75th percentile values in terms of CTDIvol were 30.2, 35.3 and 36.2 mGy, while in terms of DLP, they were 583, 619 and 781 mGycm for brain CT, respectively. Similar results are presented for the above-mentioned procedures, as well as in terms of SSDE. Conclusion: The first ever experience in obtaining AQD, SSDE and DRLs values for specific CT procedures couples image quality with dose indices, showing comparable values with other relevant studies. Hence, it will provide a baseline for comparison within the facility for future studies and facilitate dose optimization for other facilities aiming for improvement. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung nodules detected by computed tomography (CT) often require invasive procedures for definitive diagnosis. With the increasing use of CT, incidental lung nodules have increased significantly. An adjunctive blood-based biomarker test that predicts lung cancer risk could reduce unnecessary interventions and focus diagnostic efforts on high-risk patients. This study introduces a blood-based biomarker test that predicts lung cancer risk in CT-detected nodules with a sensitivity of 78.4% (95% CI: 75.7–81.1) and a specificity of 93.1% (95% CI: 90.0–96.3). Background and Objectives: Lung nodules detected by chest computed tomography (CT) often require invasive biopsies for definitive diagnosis, leading to unnecessary procedures for benign lesions. A blood-based biomarker test that predicts lung cancer risk in CT-detected nodules could help stratify patients and direct invasive diagnostics toward high-risk individuals. Methods: In this multicenter, single-blinded clinical trial, we evaluated a test measuring plasma levels of p53, anti-p53 autoantibodies, CYFRA 21-1, and anti-CYFRA 21-1 autoantibodies in patients with CT-detected lung nodules. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, and subgroup analyses by gender, age, and smoking status were performed. A total of 1132 patients who had CT-detected lung nodules, including 885 lung cancer cases and 247 benign lesions, were enrolled from two academic hospitals in South Korea. Results: The test demonstrated a sensitivity of 78.4% (95% CI: 75.7–81.1) and specificity of 93.1% (95% CI: 90.0–96.3) in predicting lung cancer in CT-detected nodules. The PPV was 97.6%, and the NPV was 54.6%. Performance was consistent across gender (sensitivity 79.3% in men and 76.8% in women) and age groups, with a specificity of 93.4% in men and 92.7% in women. Stage I lung cancer was detected with a sensitivity of 80.6%. Conclusions: The Lung Cancer test based on 9G technology presented here offers a non-invasive method for stratifying lung cancer risk in patients with CT-detected nodules. Its integration into clinical practice could reduce unnecessary interventions and foster earlier detection. [ABSTRACT FROM AUTHOR]

To explore the predictive factors of axillary lymph node metastasis in breast cancer, and to provide a basis for clinical decision-making under the DIP payment mode of medical insurance. Methods A total of 715 patients with breast cancer were divided into the metastasis group (n=309) and the non-metastasis group (n=406) according to the postoperative paraffin pathological results. Data of age > 60 years old, menopausal status, body mass index (BMI) > 24 kg/m², hyperglycemia (GLU > 6.1 mmol/L), high triglycerides (TG > 1.7 mmol/L), maximum diameter of the tumor, the distance between the tumor and nipple and the quadrant where the tumor located were compared between the two groups. The expression levels of estrogen receptor (ER), progesterone receptor (PR), nuclear proliferation antigen (Ki-67) and human epidermal growth factor receptor-2 (Her-2) in breast cancer tissue samples were detected by histological grading and immunohistochemistry. The consistency, sensitivity and specificity of chest CT and breast ultrasound were examined, taken the pathological diagnosis as the gold standard. Results Compared with the non-metastatic group, the proportion of maximum diameter of tumor > 2 cm, histological grade III, high Ki-67 and high ER expression, tumor located in the outer upper quadrant, the distance > 3 cm between tumor and nipple were increased in the metastatic group, and the proportion of high level of TG was decreased in the metastatic group (P < 0.05). The consistency between chest CT and pathological diagnosis was better than that of breast ultrasound (Kappa was 0.493 and 0.353 respectively, P < 0.05). Logistic regression analysis showed that histological grade III, high expression of ER, maximum diameter of tumor > 2 cm, and chest CT diagnosis were risk factors for axillary lymph node metastasis (P < 0.05). Conclusion The combined application of the predictive factors of axillary lymph node metastasis of breast cancer could provide certain reference for clinical decision-making under the background of DIP payment mode of medical insurance. [ABSTRACT FROM AUTHOR]

Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no or mild TOR), ≥5 and <15 (moderate TOR), and ≥15 (severe TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p < 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27–7.44; p = 0.01). A moderate TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI 1.00–3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI 1.56–5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early intervention and management strategies. The clinical significance of TOR thresholds needs further evaluation in larger samples. [ABSTRACT FROM AUTHOR]

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Purpose: Opportunistic osteoporosis screening, conducted during routine medical examinations such as chest computed tomography (CT), presents a potential solution for early detection. This study aims to investigate the feasibility of utilizing radiomics technology based on chest CT images to screen for opportunistic osteoporosis. Methods: This Study is a Multicenter Retrospective Investigation. Relevant clinical data, including demographics and DXA results, would be collected for each participant. The radiomics analysis in this study focuses on the extraction of features from the 11th or 12th thoracic vertebral bodies from chest CT images. SVM machine learning models would be trained using these radiomic features, with DXA results as the ground truth for osteoporosis classification. Results: In the training group, Clinical models had an accuracy of 0.684 and an AUC of 0.744, Radiomics models had an accuracy of 0.828 and an AUC of 0.896, Nomogram models had an accuracy of 0.839 and an AUC of 0.901. In the internal validation group, Clinical models had an accuracy of 0.769 and an AUC of 0.829, Radiomics models had an accuracy of 0.832 and an AUC of 0.892, Nomogram models had an accuracy of 0.839 and an AUC of 0.918. In the external validation group, Clinical models had an accuracy of 0.715 and an AUC of 0.741, Radiomics models had an accuracy of 0.777 and an AUC of 0.796, Nomogram models had an accuracy of 0.785 and an AUC of 0.807. In all three datasets, the Nomogram model exhibited a statistically significant difference in screening effectiveness compared to the clinical models. Conclusion: Our research demonstrates that by leveraging radiomics features extracted from a single thoracic spine using chest CT, and incorporating these features with patient basic information, opportunistic screening for osteoporosis can be achieved. [ABSTRACT FROM AUTHOR]

Bronchogenic cysts, a rare congenital pulmonary disorder, typically affect young adults and are often managed conservatively. However, large cysts with recurrent infections require surgical intervention. This case study highlights the successful management of a large bronchogenic cyst. A 53-year-old female presented with a decade-long history of recurrent respiratory infections manifesting as cough, yellow purulent sputum, and shortness of breath. Chest computed tomography revealed a large bronchogenic cyst in the right middle lobe, causing cardiac compression. Despite conservative management, the recurrent symptoms persisted. After multidisciplinary consultation, a thoracoscopic right middle lobectomy was planned. Severe pleural adhesions and bleeding complicated the procedure; therefore, thoracotomy was performed. Postoperatively, the patient developed transient fever and elevated white blood cell count, both of which resolved with appropriate antibiotic therapy. The patient was discharged in stable condition, with no recurrence of symptoms at follow-up. Large, symptomatic bronchogenic cysts that cause recurrent infections require surgical resection. [ABSTRACT FROM AUTHOR]

Background: There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules. Methods: An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used. Results: Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68–0.84) v 0.81 (95% CI 0.71–0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00–1.07) and 1.10 (95% CI 1.07–1.13) versus physician judgement and clinical risk models alone, respectively. Conclusion: DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making. [ABSTRACT FROM AUTHOR]

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI) may help improve pulmonary embolism (PE) management with early CT pulmonary angiogram (CTPA) screening and accelerate PERT coordination. This study aimed to test the clinical validity of an FDA-approved PE AI algorithm. CTPA scan data of 200 patients referred due to automated AI detection of suspected PE were retrospectively reviewed. In our institution, all patients suspected of PE received a CTPA. The AI app was then used to analyze CTPA for the presence of PE and calculate the right-ventricle/left-ventricle (RV/LV) ratio. We compared the AI's output with the radiologists' report. Inclusion criteria included segmental PE with and without RV dysfunction and high-risk PE. The primary endpoint was false positive rate. Secondary end points included clinical outcomes according to the therapy selected, including catheter-directed interventions, systemic thrombolytics, and anticoagulation. Fifty-seven of 200 exams (28.5%) were correctly identified as positive for PE by the algorithm. A total of 143 exams (71.5%) were incorrectly reported as positive. In 8% of cases, PERT was consulted. Four patients (7%) received systemic thrombolytics without any complications. There were six patients (10.5%) who developed high-risk PE and underwent thrombectomy, one of whom died. Among 46 patients with acute PE without right heart strain, 44 (95%) survived. The false positive rate of our AI algorithm was 71.5%, higher than what was reported in the AI's prior clinical validity study (91% sensitivity, 100% specificity). A high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy. This can lead to increased workloads for PERT consultants, alarm/notification fatigue, and automation bias. The AI direct notification process to the PERT team did not improve PERT triage efficacy. [ABSTRACT FROM AUTHOR]

Purpose: Childhood and young adult cancer survivors exposed to chest radiotherapy are at increased risk of lung cancer. In other high-risk populations, lung cancer screening has been recommended. Data is lacking on prevalence of benign and malignant pulmonary parenchymal abnormalities in this population. Methods: We conducted a retrospective review of pulmonary parenchymal abnormalities in chest CTs performed more than 5 years post-cancer diagnosis in survivors of childhood, adolescent, and young adult cancer. We included survivors exposed to radiotherapy involving the lung field and followed at a high-risk survivorship clinic between November 2005 and May 2016. Treatment exposures and clinical outcomes were abstracted from medical records. Risk factors for chest CT–detected pulmonary nodule were assessed. Results: Five hundred and ninety survivors were included in this analysis: median age at diagnosis, 17.1 years (range, 0.4–39.8); and median time since diagnosis, 22.3 years (range, 1–58.6). At least one chest CT more than 5 years post-diagnosis was performed in 338 survivors (57%). Among these, 193 (57.1%) survivors had at least one pulmonary nodule detected on a total of 1057 chest CTs, resulting in 305 CTs with 448 unique nodules. Follow-up was available for 435 of these nodules; 19 (4.3%) were malignant. Risk factors for first pulmonary nodule were older age at time of CT, CT performed more recently, and splenectomy. Conclusions: Benign pulmonary nodules are very common among long-term survivors of childhood and young adult cancer. Implications for Cancer Survivors: High prevalence of benign pulmonary nodules in cancer survivors exposed to radiotherapy could inform future guidelines on lung cancer screening in this population. [ABSTRACT FROM AUTHOR]

Abstract We aimed to implement a fully automatic computed tomography (CT) image-detection programming algorithm as a pectus excavatum (PE) diagnostic tool, facilitating comprehensive chest wall deformity evaluation. We developed our algorithm using MATLAB, leveraging the Hounsfield unit threshold and region growing methods. The MATLAB graphical user interface enables the direct use of our program. We validated the model using CT images of anthropomorphic phantoms and one normal individual. The measurement values obtained by our algorithm demonstrated very small differences compared to the known anthropomorphic phantom model data and manual measurement. For algorithm testing, 17,214 chest CT scans obtained from 57 PE patients were processed by the algorithm and independently reviewed by a radiologist and a thoracic surgeon. The measurements of transverse, anteroposterior, and sternum-to-vertebral distance of the thoracic cavity, along with the calculated data of four indices, exhibited high positive correlations (0.94–0.99). The asymmetry index and maximum anteroposterior hemithorax distance exhibited moderate correlation (0.40–0.83). Our automatic PE diagnostic tool demonstrated high accuracy; four chest wall deformity indices were obtained simultaneously without any initial manual marking, correlating well with manual measurements.

Abstract Purpose Opportunistic osteoporosis screening, conducted during routine medical examinations such as chest computed tomography (CT), presents a potential solution for early detection. This study aims to investigate the feasibility of utilizing radiomics technology based on chest CT images to screen for opportunistic osteoporosis. Methods This Study is a Multicenter Retrospective Investigation. Relevant clinical data, including demographics and DXA results, would be collected for each participant. The radiomics analysis in this study focuses on the extraction of features from the 11th or 12th thoracic vertebral bodies from chest CT images. SVM machine learning models would be trained using these radiomic features, with DXA results as the ground truth for osteoporosis classification. Results In the training group, Clinical models had an accuracy of 0.684 and an AUC of 0.744, Radiomics models had an accuracy of 0.828 and an AUC of 0.896, Nomogram models had an accuracy of 0.839 and an AUC of 0.901. In the internal validation group, Clinical models had an accuracy of 0.769 and an AUC of 0.829, Radiomics models had an accuracy of 0.832 and an AUC of 0.892, Nomogram models had an accuracy of 0.839 and an AUC of 0.918. In the external validation group, Clinical models had an accuracy of 0.715 and an AUC of 0.741, Radiomics models had an accuracy of 0.777 and an AUC of 0.796, Nomogram models had an accuracy of 0.785 and an AUC of 0.807. In all three datasets, the Nomogram model exhibited a statistically significant difference in screening effectiveness compared to the clinical models. Conclusion Our research demonstrates that by leveraging radiomics features extracted from a single thoracic spine using chest CT, and incorporating these features with patient basic information, opportunistic screening for osteoporosis can be achieved.

Preserved ratio impaired spirometry (PRISm) is a common pulmonary functional impairment, considered a pre-chronic obstructive pulmonary disease stage and has received increased attention from the academic community in recent years. Despite a comprehensive review of the etiology, epidemiology, and risk factors of the PRISm population, there is a lack of systematic review for chest CT imaging. To gain a more comprehensive understanding of this population, this article summarizes the chest CT imaging features of the PRISm population using both visual and quantitative assessment methods, including the characteristic changes of airways, lung parenchyma, and vessels. The article indicates that the reference value of visual assessment of chest CT results for the PRISm population is limited, while quantitative assessment of chest CT, combined with pulmonary function testing, is advantageous in gaining a deeper understanding of the lung structural features of the PRISm population. Future studies are expected to employ a more systematic approach through prospective, large-sample, multi-center cohort studies to elucidate the characteristics of the PRISm population in chest imaging.

Atopobium minutum (A. minutum) has rarely been documented in human infections. However, this report describes a case involving a 52-year-old woman who developed empyema and lung collapse due to A. minutum. She initially presented to the emergency department with nausea, vomiting, diarrhea, and abdominal pain. Her condition quickly declined within the first day of arrival, leading to respiratory failure and requiring intubation and ICU-level care. Despite receiving intensive antibiotic treatment, the patient needed prolonged intubation and a tracheostomy. Initial cultures indicated Streptococcus intermedius and Lactobacillus minutus, but final culture results identified A. minutum as the cause. This case highlights the difficulty in diagnosing A. minutum infections, often necessitating advanced DNA sequencing, and raises concerns about potential multidrug resistance. It highlights the importance of prompt identification of the pathogen by laboratories to allow for effective treatment of these rare infections.

Histiocytic sarcoma is an extremely rare disease that's hard to diagnose and treat, often leading to a poor prognosis. Here, we present a case report detailing a rare occurrence of HS in a 37-year-old pregnant woman who first presented with left shoulder pain, palpitations, and a productive cough at 20 weeks of gestation. Her diagnostic evaluations were performed, including different imaging modalities such as chest X-rays, CT scans, and MRI. Imaging revealed a large mediastinal mass with extensive involvement of the adrenal glands, lungs, and lymph nodes. The definitive diagnosis of HS is based on pathological and morphological features, and the immunohistochemistry report plays a key role. In our case, the diagnosis of HS was confirmed through pathological evaluation and immunohistochemistry, with a positive CD68 result obtained from a supraclavicular lymph node biopsy. A hospital committee comprising medical specialists like hematologists-oncologists, pathologists, pulmonologists, and obstetricians was brought together to assess the case collectively. The patient received chemotherapy, which alleviated her symptoms and maintained her condition. Based on the committee's recommendations, despite a healthy fetus and normal obstetric sonograms, the decision was made to terminate the pregnancy with the consent of the patient and her family. Despite initial improvement postchemotherapy, the patient's condition worsened, necessitating intubation. Tragically, two months after the initial admission, the patient passed away due to severe complications. In this case report, we provide a literature review and review of the patient's imaging reports. Since the patient is pregnant and HS is uncommon, it's important to highlight that this case is unique and worth sharing.

Abstract Background Liver injuries such as metabolic-associated fatty liver disease, liver fibrosis, and steatosis are common in COVID-19 patients. Unenhanced CT can be used to diagnose the morphological traits of steatosis and cirrhosis. This study aims to provide a clear overview on the association between liver injuries and decreased hepatic CT attenuation values on chest CT images in patients with COVID-19. Main text Measuring HU values can be used as an additional method to diagnose liver injuries, even though HU values alone cannot definitively diagnose specific liver diseases. Chest CT is a common imaging procedure for diagnosing pneumonia, and during this CT examination, the upper abdomen, including the liver and spleen, is incidentally captured on the CT scan. Therefore, the assessment of liver injuries in chest CT of patients with COVID-19 can be performed by measuring the HU value of the liver and spleen. In this review, we summarize all the currently available CT findings in liver injuries associated with decreased hepatic CT attenuation values. Conclusion We found out that liver injuries such as hepatic steatosis and metabolic disease were more frequent in the COVID-19 patient, especially in severe and ICU patients. Compared to control group and COVID-19 patients with mild symptoms, the hepatic CT attenuation values and L/S ratios were lower in research group and severe COVID-19 patients.

Introduction: Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), presents a broad spectrum of clinical manifestations, from asymptomatic cases to severe pneumonia and acute respiratory distress syndrome (ARDS). Chest computed tomography (CT) has become a critical diagnostic tool, especially in regions with high disease prevalence. This study aims to assess the common CT findings, patterns of lung involvement, and severity of disease in RT-PCR positive COVID-19 patients to better understand and manage this pervasive disease. Materials and Methods: This observational cross-sectional study was conducted at Dr. Kamakshi Memorial Hospital, Chennai, from 2020 to 2021. The study included RT-PCR positive COVID-19 patients aged 18 to 95 years who presented to the fever clinic or COVID casualty and were referred to the radiology department for chest CT evaluation. Exclusion criteria included pregnancy, age under 18 years, and refusal to consent. Non-contrast chest CT scans were performed using a TOSHIBA Aquilion Lightening 16-slice CT machine. Scans were acquired in a single inspiratory breath-hold from the lung apex to the costophrenic angle. CT findings were analyzed and reported by two experienced radiologists, with discrepancies resolved through consensus. Results: Out of 349 patients, 213 (61%) were male and 136 (39%) were female, with a mean age of 47.7 years. The distribution of CT findings showed significant variability among the four groups. Group A had the highest percentage of normal CT scans (22%) and ground-glass opacities (52%). Group B exhibited a reduction in normal CT scans (9%) and an increase in ground-glass opacities (57%). Group C showed further decrease in normal CT scans (10.6%) with increased crazy paving (17.3%) and reticulation (14.6%). Group D had similar normal CT scans (10.8%) but significantly higher incidences of reticulation (24.3%) and ground-glass opacities (64%). Conclusion: This study highlights the critical role of chest CT in monitoring the progression of COVID-19 pneumonia. The findings demonstrate a clear temporal evolution of lung involvement, from ground-glass opacities in the early stages to more complex patterns such as crazy paving and reticulation in later stages.

The COVID-19 pandemic has spread worldwide for over two years. It was considered a significant threat to global health due to its transmissibility and high pathogenicity. The standard test for COVID-19, namely, reverse transcription polymerase chain reaction (RT–PCR), is somehow inaccurate and might have a high false-negative rate (FNR). As a result, an infected person with a negative test result may unknowingly continue to spread the virus, especially if they are infected with an undiscovered COVID-19 strain. Thus, a more accurate diagnostic technique is required. In this study, we propose 3Cs, which is a capsule neural network (CapsNet) used to classify computed tomography (CT) images as novel coronavirus pneumonia (NCP), common pneumonia (CP), or normal lungs. Using 6123 CT images of healthy patients’ lungs and those of patients with CP and NCP, the 3Cs method achieved an accuracy of around 98% and an FNR of about 2%, demonstrating CapNet’s ability to extract features from CT images that distinguish between healthy and infected lungs. This research confirmed that using CapsNet to detect COVID-19 from CT images results in a lower FNR compared to RT–PCR. Thus, it can be used in conjunction with RT–PCR to diagnose COVID-19 regardless of the variant.

Tinghua Hu,1,&ast; Shanshan Dai,2,&ast; Lan Yang,1 Bo Zhu1 1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710000, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Xi’an No. 9 Hospital, Xi’an, Shaanxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bo Zhu, Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, 277 Yanta West Road, Xi’an, Shaanxi, 710061, People’s Republic of China, Email zhubo685689@163.comBackground: COPD, combined with Osteoporosis, has a high incidence and potential for great harm. Choosing an optimal diagnostic method to achieve bone mineral density (BMD) screening is crucial for COPD patients. Studies on COPD patients with BMD reduction are lacking.Purpose: To identify the risk factors of BMD reduction and osteoporosis in COPD patients.Patients and Methods: We included a total of 81 patients with AECOPD, who were admitted to the hospital from July 1, 2019, to January 31, 2020. Patients were grouped into BMD normal group, BMD reduced group and OP group. The areas under ROC curve were used to explore the value of CT values in the diagnosis of bone abnormality, and clinical indicators were collected.Results: The CT value of the vertebral cancellous bone is highly correlated with the T value of BMD (R > 5.5, P < 0.0001). Using multivariate Logistic regression analysis, we showed that COPD duration, BMI, 25-hydroxyvitamin D3, and long-term inhaled glucocorticoid were independent factors affecting different BMD levels in COPD patients. No significant difference in bone formation indexes between groups. β-crossL was negatively correlated with serum IL-6 (r=− 0.254, P=0.022), and ALP was positively correlated with serum TNF-α (r=0.284, P=0.023).Conclusion: Thoracolumbar vertebral cancellous bone CT has potential value in the diagnosis of bone abnormality. COPD duration, BMI, 25-hydroxyvitamin D3, and long-term inhaled glucocorticoid may contribute to the BMD reduction in COPD patients, and serum IL-6 and TNF-α regulate bone metabolism in COPD patients.Keywords: chronic obstructive pulmonary disease, osteoporosis, bone mineral density, chest CT

Introduction: This study aimed to identify useful clinical indicators for predicting the relapse of interstitial lung disease (ILD) complicated with anti-aminoacyltRNA synthetase (ARS) antibodies (anti-ARS-ILD), being treated with prednisolone and calcineurin inhibitors. Methods: Fifty patients with anti-ARS-ILD were enrolled between October 2014 and August 2022. All patients were treated with prednisolone and calcineurin inhibitors as remission induction therapy and followed up for over a year with these combination therapies. We examined patients who experienced ILD relapse after immunosuppressive treatment. We explored the risk factors for predicting ILD relapse in these patients by comparing demographic, clinical, laboratory, and radiological findings and treatments between the relapsed and non-relapsed groups on admission. Results: Of the 50 patients, 19 (38%) relapsed during a median follow-up of 4.8 years. Univariate and multivariate Cox regression analyses identified the presence of acute/subacute (A/S)-ILD, higher serum aldolase (ALD) and surfactant protein-D (SP-D) levels, and lower %forced vital capacity (FVC) as risk factors for relapse in patients with anti-ARS-ILD. Using the receiver operating curve analysis, ALD =6.3 U/L, SP-D =207 ng/mL, and %FVC =76.8% were determined as the cut-off levels for indicating a poor prognosis. The 5-year relapse rate was significantly higher in patients with A/S-ILD, serum ALD=6.3 U/L, serum SP-D =207 ng/mL, or %FVC of =76.8% than in those without these parameters. (P=0.009, 0.0005, 0.0007, 0.0004, respectively) Serum ALD levels were significantly correlated with the disease activity indicators of anti-ARS-ILD. Conclusion: The presence of A/S-ILD, higher serum ALD and SP-D levels, and lower %FVC are useful indicators for predicting anti-ARS-ILD relapse. [ABSTRACT FROM AUTHOR]

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of −528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions. [ABSTRACT FROM AUTHOR]

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the Computed Tomography (CT) scan of a person to be in the training set, while other images of the same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets. [ABSTRACT FROM AUTHOR]

Breast density is an important marker for increased breast cancer risk, but the ideal marker would be more specific. Breast compactness, which reflects the focal density of collagen fibers, parallels breast cancer occurrence being highest in the upper outer quadrants of the breast. In addition, it peaks during the same time frame as breast cancer in women. Improved biomarkers for breast cancer risk could pave the way for patient-specific preventive strategies. [ABSTRACT FROM AUTHOR]

Background: Liver injuries such as metabolic-associated fatty liver disease, liver fibrosis, and steatosis are common in COVID-19 patients. Unenhanced CT can be used to diagnose the morphological traits of steatosis and cirrhosis. This study aims to provide a clear overview on the association between liver injuries and decreased hepatic CT attenuation values on chest CT images in patients with COVID-19. Main text: Measuring HU values can be used as an additional method to diagnose liver injuries, even though HU values alone cannot definitively diagnose specific liver diseases. Chest CT is a common imaging procedure for diagnosing pneumonia, and during this CT examination, the upper abdomen, including the liver and spleen, is incidentally captured on the CT scan. Therefore, the assessment of liver injuries in chest CT of patients with COVID-19 can be performed by measuring the HU value of the liver and spleen. In this review, we summarize all the currently available CT findings in liver injuries associated with decreased hepatic CT attenuation values. Conclusion: We found out that liver injuries such as hepatic steatosis and metabolic disease were more frequent in the COVID-19 patient, especially in severe and ICU patients. Compared to control group and COVID-19 patients with mild symptoms, the hepatic CT attenuation values and L/S ratios were lower in research group and severe COVID-19 patients. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has spread worldwide for over two years. It was considered a significant threat to global health due to its transmissibility and high pathogenicity. The standard test for COVID-19, namely, reverse transcription polymerase chain reaction (RT–PCR), is somehow inaccurate and might have a high false-negative rate (FNR). As a result, an infected person with a negative test result may unknowingly continue to spread the virus, especially if they are infected with an undiscovered COVID-19 strain. Thus, a more accurate diagnostic technique is required. In this study, we propose 3Cs, which is a capsule neural network (CapsNet) used to classify computed tomography (CT) images as novel coronavirus pneumonia (NCP), common pneumonia (CP), or normal lungs. Using 6123 CT images of healthy patients' lungs and those of patients with CP and NCP, the 3Cs method achieved an accuracy of around 98% and an FNR of about 2%, demonstrating CapNet's ability to extract features from CT images that distinguish between healthy and infected lungs. This research confirmed that using CapsNet to detect COVID-19 from CT images results in a lower FNR compared to RT–PCR. Thus, it can be used in conjunction with RT–PCR to diagnose COVID-19 regardless of the variant. [ABSTRACT FROM AUTHOR]

Objectives Pulmonary arterial hypertension associated with systemic sclerosis (PAH-SSc) sometimes accompanies pulmonary veno-occlusive disease (PVOD). We aimed to reveal the relationship between clinical signs of PVOD and severity of pulmonary vasculopathy in SSc. Methods This study included 52 consecutive SSc patients who had pulmonary haemodynamic abnormalities [mean pulmonary arterial pressure (mPAP) >20 mmHg, pulmonary vascular resistance >2 WU or pulmonary artery wedge pressure (PAWP) >15 mmHg]. A chest CT scan was evaluated in all patients. Patients were divided into two groups, the 0–1 group and the 2–3 group, according to the number of chest CT signs for PVOD, including mediastinal lymph node enlargement, thickened interlobular septal wall and ground glass opacity. Pulmonary haemodynamics, echocardiography and MRI-based cardiac function, pulmonary function and serum biomarkers were compared between the two groups. Results Mediastinal lymph node enlargement, thickened interlobular septal wall and ground glass opacity were observed in 11 (21%), 32 (62%) and 11 (21%) patients, respectively. The 2–3 group (n  = 15) had higher mPAP (P  = 0.02) but lower diffusing capacity of carbon monoxide (DLCO)/alveolar volume (P  = 0.02) compared with the 0–1 group (n  = 37). Other parameters, including PAWP, cardiac output, left ventricular ejection fraction, left atrial diameter, forced vital capacity, brain natriuretic peptide and Krebs von den Lunge-6 were not different between the two groups. Conclusions The CT signs for PVOD had a positive correlation with mPAP but a negative correlation with DLCO in SSc patients, indicating that PAH-SSc may reflect a spectrum of pulmonary vascular disease that ranges from the pulmonary artery to the vein. [ABSTRACT FROM AUTHOR]

PURPOSE: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. METHODS: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. RESULTS: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. CONCLUSIONS: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

Introduction. Adrenal incidentalomas (AI) are a current problem due to their potential hormonal activity. At the same time, their prevalence in the general population is not completely clear. Patients and Methods. We analyzed the chest CT scans of 307 patients treated as inpatients for COVID‑19 in order to identify patients with previously undiagnosed AI among them (study group). A control group was also selected from these 307 patients; it consisted of 27 patients without adrenal masses, similar in sex and age to the study group. Results. Out of 307 patients, 27 (8.7%) patients had AI detected for the first time. The majority of patients with AI were of older age group, predominantly women. The density of the detected masses was low, more than half of the AI were less than 2 cm in size. When comparing clinical, laboratory data and outcome of the study and control groups, no differences were found. Conclusion. The prevalence of AI in our study is higher compared to that in other sources due to targeted revision of the adrenal region on CT by radiology specialists. The absence of differences in the comparison of the two groups relieves clinical specialists of the necessity of additional adrenal examination of patients during inpatient treatment for extra-­adrenal disease, but in the posthospital period such examination may be important.

Background: Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19. Method: A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost. Findings: In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review. Conclusion: RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

Congyang Xue,1 Xiaopei Lu,2 Guangda Sun,1 Nan Wang,1 Gansheng He,1 Wenqiang Xu,1 Zhipeng Xi,1 Lin Xie1 1Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2Department of Traditional Chinese Medicine Surgery, Nanjing Hospital of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of ChinaCorrespondence: Lin Xie, Department of Spine Surgery, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, 100th. Shizi Street, Nanjing, Jiangsu Province, 210028, People’s Republic of China, Email xielin@njucm.edu.cnPurpose: To explore the validity of the thoracic spine Hounsfield Unit (HU) measured by chest computed tomography (CT) for opportunistic screening of diabetic osteoporosis. The current study attempted to establish a diagnostic threshold for thoracic spine HU in a type 2 diabetes mellitus (T2DM) population with osteoporosis.Patients and Methods: The current study retrospectively included 334 patients with T2DM. They underwent chest CT and Dual-energy X-ray (DXA) between August 2021 and January 2022 in our hospital. HU values were measured on the resulting chest CT images at thoracic spine 11 and 12 to construct regions of interest. All patients were grouped according to the lowest T-value of DXA examination: osteoporosis, osteopenia and normal bone density. HU values were compared with T-values in each group of patients, and receiver operating characteristics curves were plotted to calculate diagnostic thresholds as well as sensitivity and specificity.Results: There was a strong correlation between the HU values of chest CT and the T-values of DXA (p < 0.01). The sensitivity for osteoporosis was 88.7% for T11 attenuation≤ 98 HU and the specificity for osteoporosis was 87.5% for T12 attenuation ≤ 117HU; the specificity for normal BMD was 85.4% for T11 attenuation ≥ 147 HU and 82% for T12 attenuation ≥ 146 HU.Conclusion: Chest CT can be used to screen patients with T2DM for opportunistic osteoporosis and help determine if they need DXA screening. The current study suggests that when the HU threshold of T11 ≤ 98/T12 ≤ 117, patients may need further osteoporosis screening.Keywords: osteoporosis, T2DM, HU, thoracic vertebra, chest CT

Abstract Background To estimate the diagnostic utility of chest CT qualitative assessment and chest CT total severity score (TSS) to predict mortality in oncology patients with COVID-19 infection. Methods This retrospective study included 151 oncology patients with COVID-19 infection. 67, 84 were male and female, respectively. Their mean age (years) ± SD was 49.7 ± 14.9. Two radiologists individually reviewed the chest CT and scored the pulmonary abnormalities using TSS. Inter-observer agreement was determined using the Bland–Altman plot. Correlation between TSS and COVID-19 severity, complication, mortality, cancer status and effect in anticancer therapy plan was done. Results There was a statistically significant excellent agreement between the independent observers in quantitative pulmonary assessment using TSS with interclass correlation (ICC) > 0.9 (P

Jiawei Ye,1,&ast; Yingying Huang,2,&ast; Caiting Chu,3 Juan Li,1 Guoxiang Liu,1 Wenjie Li,1 Chengjin Gao1 1Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China; 2Dementia Research Centre, Faculty of Medicine, Health and Human Sciences, Macquarie University Sydney, Australia; 3Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Chengjin Gao; Wenjie Li, Email gaochengjin@xinhuamed.com.cn; kingnever@hotmail.comBackground: Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients.Methods: From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality.Results: Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001– 1.005), APACHE II score (OR = 1.387, 95% CI 1.216– 1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939– 2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947– 0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131– 2.068), urea nitrogen (OR = 1.587, 95% CI 1.222– 2.062), SOFA score(OR = 3.333, 95% CI 1.476– 7.522), qSOFA score(OR = 15.197, 95% CI 3.281– 70.384), PSI score(OR = 1.053, 95% CI 1.018– 1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615– 114,953.586) independently linked to COVID-19 patient severity.Keywords: COVID-19, chest CT, artificial intelligence, mortality, severity

Congenital lobar overinflation is a rare but well-recognized congenital cause of neonatal and infantile respiratory distress. At times, the condition can mimic other congenital or acquired diseases and have atypical distribution and imaging patterns. Lobectomy of the involved lobe(s) is curative. We present our experience with 3 surgically confirmed cases of congenital lobar overinflation. Referral papers, patient's charts, including operation notes, and radiographic records were reviewed. All of them were initially misdiagnosed or underdiagnosed based on the initial radiographic examination alone. All 3 were referred to our center with respiratory distress, and the first 2 were treated with antibiotics prior to the settlement of their diagnosis. Chest computed tomography was key in diagnosing all 3 cases. The first patient was a 10-day-old neonate diagnosed with bilateral congenital lobar overinflation. The second patient was a 2-month-old infant diagnosed with right middle lobe disease. In these 2 cases, the initial assessment of the vascularity was atypically excessive in the affected lobe(s). Eventually, correlation with typical concurrent imaging features and the clinical condition of the patients led to the correct diagnosis. The third case was a 4-month-old infant with left upper lobe congenital lobar overinflation. All cases underwent successful surgical treatment. Congenital lobar overinflation is a rare anomaly, and multiple-lobe involvement is even rarer. Vascularity within the affected lobes is a subjective assessment that can be overestimated, leading to confusion, and a feature that needs correlation with other common imaging features and the clinical course of patients.

OBJECTIVES To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone. METHODS We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans. A deep learning model performed aortic landmark detection and aorta segmentation to derive 8 vessel segments. Associated calcifications were detected and their volumes measured using a mean-based density thresholding. Algorithm parameters (calcium cluster size threshold, aortic mask dilatation) were varied to determine optimal performance for the upper ascending aorta that encompasses the aortic clamping zone. A binary visual rating served as a reference. Standard estimates of diagnostic accuracy and inter-rater agreement using Cohen's Kappa were calculated. RESULTS Thoracic aortic calcifications were observed in 74% of patients with a prevalence of 27–70% by aorta segment. Using different parameter combinations, the algorithm provided binary ratings for all scans and segments. The best performing parameter combination for the presence of calcifications in the aortic clamping zone yielded a sensitivity of 93% and a specificity of 82%, with an area under the receiver operating characteristic curve of 0.874. Using these parameters, the inter-rater agreement ranged from κ 0.66 to 0.92 per segment. CONCLUSIONS Fully automated segmental detection of thoracic aortic calcifications in chest CT performs with high accuracy. This includes the critical preoperative assessment of the aortic clamping zone. [ABSTRACT FROM AUTHOR]

Objective：To investigate the clinical value of small pulmonary vessels parameters measured by chest CT in evaluating the classification of different types of pulmonary hypertension（PH）and the severity grading of connective tissue disease s - related pulmonary hypertension（CTD-PH). Methods：A retrospective analysis included 170 PH patients, including 60 CTD-PH patients, 52 idiopathic PH（IPAH）patients, and 58 chronic obstructive pulmonary disease - related PH（COPD - PH）patients, with 120 healthy controls（HC）included as controls. The ratio of the sum of the cross-sectional area（CSA）of small pulmonary vessels with CSA ＜5 mm² （%CSA＜5）and between 5 to 10 mm²（ %CSA5- 10）to the total CSA of the lung measured by chest CT was compared among the four groups using one-way ANOVA or Kruskal-Wallis test, followed by pairwise comparisons. Receiver operating characteristic（ROC）curve analysis was used to evaluate the performance of %CSA for differentiating mild to moderate CTD-PH（CTD-LM-PH）from severe CTD-PH（CTD-S-PH）patients, and calculate the area under the curve（AUC), sensitivity and specificity. Results：The %CSA＜5 of the IPAH and COPD -PH groups were significantly lower compared to the HC group（P ＜ 0.001). Additionally, the %CSA5 - 10 of the COPD -PH group showed a significant decline compared to the HC group（P=0.038), whereas the %CSA5-10 of the CTD-PH and IPAH groups was significantly high compared to the HC group（both P ＜ 0.05). In comparisons between different types of PH groups, the %CSA＜5 and % CSA5 - 10 of the CTD - PH group were higher than those of the COPD - PH group（P ＜ 0.001). The % CSA5 - 10 of the IPAH group was significantly higher than that of the CTD -PH group（P=0.022), while there was no significant difference in the %CSA＜5 between the IPAH and COPD-PH groups（P=0.833). The %CSA＜5 of CTD-S-PH group was significantly lower than that of CTD-LM-PH group（P= 0.004). The ROC curve analysis showed that the optimal cutoff value for %CSA＜5 to predict CTD -S -PH was 0.804, with an AUC of 0.710（95%CI：0.573-0.847), sensitivity of 0.714 and specificity of 0.320. Conclusion：The quantitative parameter %CSA assessed by chest CT can distinguish different types of PH. The %CSA＜5 can serve as a reference for evaluating the severity of CTD-PH. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 2019 (COVID-19) pandemic, artificial intelligence (AI) based software on chest computed tomography (CT) imaging has proven to have a valuable role in accelerating diagnosis and screening. The proposed AI-based tools proved to be rapid and reproducible techniques to guide patient management and treatment protocols. Although no specific guidelines exist, CT-imaging and clinical features are used for patient staging. To shed light on the role of AI techniques that have been developed in fighting COVID-19, in this review, studies investigating the usage of commonly used AI models on chest CT imaging for disease quantification and prognostication are collected. [ABSTRACT FROM AUTHOR]

Background: Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19. Method: A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost. Findings: In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review. Conclusion: RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 comorbid with noncommunicable chronic diseases (NCDs) complicates the diagnosis, treatment, and prognosis, and increases the mortality rate. The aim is to evaluate the effects of a restricted diet on clinical/laboratory inflammation and metabolic profile, reactive oxygen species (ROS), and body composition in patients with COVID-19 comorbid with NCDs. We conducted a 6-week open, pilot prospective controlled clinical trial. The study included 70 adult patients with COVID-19 comorbid with type 2 diabetes (T2D), hypertension, or nonalcoholic steatohepatitis (NASH). Interventions: a restricted diet including calorie restriction, hot water drinking, walking, and sexual self-restraint. Primary endpoints: COVID-19 diagnosis by detecting SARS-CoV-2 genome by RT-PCR; weight loss in Main group; body temperature; C-reactive protein. Secondary endpoints: the number of white blood cells; erythrocyte sedimentation rate; adverse effects during treatment; fasting blood glucose, glycosylated hemoglobin A1c (HbA1c), systolic/diastolic blood pressure (BP); blood lipids; ALT/AST, chest CT-scan. In Main group, patients with overweight lost weight from baseline (− 12.4%; P < 0.0001); 2.9% in Main group and 7.2% in Controls were positive for COVID-19 (RR: 0.41, CI: 0.04–4.31; P = 0.22) on the 14th day of treatment. Body temperature and C-reactive protein decreased significantly in Main group compared to Controls on day 14th of treatment (P < 0.025). Systolic/diastolic BP normalized (P < 0.025), glucose/lipids metabolism (P < 0.025); ALT/AST normalized (P < 0.025), platelets increased from baseline (P < 0.025), chest CT (P < 0.025) in Main group at 14 day of treatment. The previous antidiabetic, antihypertensive, anti-inflammatory, hepatoprotective, and other symptomatic medications were adequately decreased to completely stop during the weight loss treatment. Thus, the fast weight loss treatment may be beneficial for the COVID-19 patients with comorbid T2D, hypertension, and NASH over traditional medical treatment because, it improved clinical and laboratory/instrumental data on inflammation; glucose/lipid metabolism, systolic/diastolic BPs, and NASH biochemical outcomes, reactive oxygen species; and allowed patients to stop taking medications. Trial Registration: ClinicalTrials.gov NCT05635539 (02/12/2022): https://clinicaltrials.gov/ct2/show/NCT05635539?term=NCT05635539&draw=2&rank=1. [ABSTRACT FROM AUTHOR]

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT. [ABSTRACT FROM AUTHOR]

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients' age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland–Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1–R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1–R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients. [ABSTRACT FROM AUTHOR]

Background: To estimate the diagnostic utility of chest CT qualitative assessment and chest CT total severity score (TSS) to predict mortality in oncology patients with COVID-19 infection. Methods: This retrospective study included 151 oncology patients with COVID-19 infection. 67, 84 were male and female, respectively. Their mean age (years) ± SD was 49.7 ± 14.9. Two radiologists individually reviewed the chest CT and scored the pulmonary abnormalities using TSS. Inter-observer agreement was determined using the Bland–Altman plot. Correlation between TSS and COVID-19 severity, complication, mortality, cancer status and effect in anticancer therapy plan was done. Results: There was a statistically significant excellent agreement between the independent observers in quantitative pulmonary assessment using TSS with interclass correlation (ICC) > 0.9 (P < 0.001). ROC curve analysis revealed that TSS was statistically significantly higher in non-survivors using an optimum cut-off value of 5 to predict in-hospital mortality. Univariate analysis showed that age, pulmonary predominant pattern, pleural effusion, tree-in-bud, ECOG PS, tumour stage 4 and post-COVID cancer status were a statistically significant predictor of mortality. Multivariate analysis reported that consolidation versus ground-glass opacity (GGO), crazy paving pattern versus GGO and progressive versus remittent cancer diseases were statistically significant independent predictors of mortality among those patients. Conclusions: TSS demonstrated excellent inter-observer agreement to assess COVID-19 in oncology patients with low cut-off value to predict in-hospital mortality, thus raising the attention to rapid proper care in this setting. There was a statistically significant positive correlation between TSS and delayed chemotherapeutic schedule. [ABSTRACT FROM AUTHOR]

Introduction: Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer‐assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. Methods: In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. Results: HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between −30 and 20. Lesions outside these ranges were mostly benign. Conclusion: Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image. [ABSTRACT FROM AUTHOR]

OBJECTIVES The Haller index (HI) is widely utilized as a quantitative indicator to assess the extent of the pectus excavatum (PE) deformity, which is the most common chest wall abnormality in children. Both preoperative correction planning and postoperative follow-up need to be based on the standard of normal thoracic growth and development. However, there is currently no established reference range for the HI in children. Consequently, the goal of this study was to conduct a preliminary investigation of normal HI values among children to understand thoracic developmental characteristics. METHODS Chest computed tomography images obtained from January 2012 to March 2022 were randomly selected from the imaging system of the Children's Hospital of Chongqing Medical University. We divided the images of children into a total of 19 groups: aged 0–3 months (1 group), 4–12 months (1 group) and 1 year to 17 years (17 groups), with 50 males and 50 females, totaling 100 children in each group. HI was measured in the plane where the lowest point of the anterior thoracic wall was located and statistically analysed using SPSS 26.0 software. RESULTS A total of 1900 patients were included in the study. Our results showed that HI, transverse diameter and anterior-posterior diameter were positively correlated with age (P  <   0.05). Using age as the independent variable and HI as the dependent variable, the best-fit regression equations were HI-male = 2.047 * Age0.054(R2 = 0.276, P<0.0001) and HI-female = 2.045 * Age0.067(R2 = 0.398, P<0.0001). Males had significantly larger thoracic diameters than females, and there was little difference in the HI between the 2 sexes. CONCLUSIONS The HI rapidly increases during the neonatal period, slowly increases during infancy and stops increasing during puberty, with no significant differences between the sexes. [ABSTRACT FROM AUTHOR]

BACKGROUND: The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE: In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. METHODS: First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. RESULTS: Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%. CONCLUSION: The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability. [ABSTRACT FROM AUTHOR]

BackgroundWhile sarcopenia has been found to be associated with increased risks of cardiovascular diseases (CVDs), evidence exploring sex-related differences remains insufficient. This study aimed to investigate the differences in how often sarcopenia occurs in each sex, as determined by skeletal muscle area (SMA) in chest CT images, and its association with CVD common risk factors.MethodsThis cross-sectional study involved 1,340 inpatients from the Department of Geriatrics of Renji Hospital, affiliated to Shanghai Jiaotong University School of Medicine. Data on age, sex, body mass index (BMI), smoking status, disease history, and clinical parameters were collected. Sarcopenia was defined using chest CT images with a cut-off value of T12-SMA/height2