Comparison of the Utility of PI-RADS 2.1, ADC Values, and Combined Use of Both, for the Diagnosis of Transition Zone Prostate Cancers.

OBJECTIVE: To assess the performance of apparent diffusion coefficient (ADC; values or category) alone, Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) scoring alone, and the two in combination, to diagnose transition zone prostate cancers (PCas). METHODS: This retrospective study included 222 patients who underwent multiparametric magnetic resonance imaging of the prostate between May 2020 and December 2022 and who had pathologically confirmed PCa or benign prostatic hyperplasia (BPH). Prostate Imaging Reporting and Data System version 2.1 and ADC (values or category) were used in the assessment of suspicious findings identified in the transition zone. The interobserver agreements for region-of-interest measurements were calculated by intraclass correlation coefficients. Logistic regression analyses were used to determine the performance of PI-RADS v2.1 alone and in combination with ADC (values or category) to diagnose PCa. Receiver operating characteristic curve and DeLong test were used to evaluate the diagnostic performance of the quantitative parameters. RESULTS: A total of 152 patients had BPH, and 70 patients had PCa. For BPH versus PCa, the ADC values of PCa (0.64 × 10 -3 ± 0.16 × 10 -3 mm 2 /s) were significantly lower than BPH (1.06 ± 0.18 × 10 -3 mm 2 /s; P < 0.001). The PI-RADS scores for PCa (5 [interquartile range, 5-5]) were significantly higher than BPH (2 [interquartile range, 2-3]; P < 0.001). For all patients who had PI-RADS 1-5, the combined use of ADC (values or category) together with PI-RADS v2.1 did not perform significantly better than the use of PI-RADS v2.1 alone. The receiver operating characteristic of ADC category in combination with PI-RADS v2.1 score, 0.756 (95% confidence interval, 0.646-0.846), was significantly higher than that for PI-RADS 2.1 alone, 0.631 (95% confidence interval, 0.514-0.738), in PI-RADS 3-4 lesions ( P = 0.047). CONCLUSION: The ADC category can help to improve the diagnostic performance of PI-RADS v2.1 category 3-4 lesions in diagnosing PCa.

Comparison of 3 CT Perfusion Software Packages in Estimation of Ischemic Lesions in Acute Ischemic Stroke Patients.

OBJECTIVE: The aim of this study was to compare 3 computed tomography perfusion (CTP) software packages in the estimation of infarct core volumes, hypoperfusion volumes, and mismatch volumes. METHODS: Forty-three patients with large vessel occlusion in the anterior circulation who underwent CTP imaging were postprocessed by 3 software packages: RAPID, advantage workstation (AW), and NovoStroke Kit (NSK). Infarct core volumes and hypoperfusion volumes were generated by RAPID with default settings. The AW and NSK threshold settings were the following: infarct core (cerebral blood flow [CBF] <8 mL/min/100 g, CBF <10 mL/min/100 g, CBF <12 mL/min/100 g, and cerebral blood volume [CBV] <1 mL/100 g) and hypoperfusion (T max >6 seconds). Mismatch volumes were then obtained for all the combinations of the settings. Bland-Altman, intraclass correlation coefficient (ICC), and Spearman ρ or Pearson correlation coefficient were applied for statistical analysis. RESULTS: In the estimation of infarct core volumes, good agreement was observed between AW and RAPID when CBV <1 mL/100 g (ICC, 0.767; P < 0.001). For hypoperfusion volumes, good agreement (ICC, 0.811; P < 0.001) and strong correlation ( r = 0.856; P < 0.001) were observed between NSK and RAPID. For mismatch volumes, the setting of CBF <10 mL/min/100 g combined with hypoperfusion with NSK resulted in moderate agreement (ICC, 0.699; P < 0.001) with RAPID, which was the best among all other settings. CONCLUSIONS: The estimation results varied among different software packages. Advantage workstation had the best agreement with RAPID in the estimation of infarct core volumes when CBV <1 mL/100 g. NovoStroke Kit had better agreement and correlation with RAPID in the estimation of hypoperfusion volumes. NovoStroke Kit also had moderate agreement with RAPID in estimating mismatch volumes.

Comparison of 68Ga-FAPI and 18F-FDG PET/CT for the diagnosis of primary and metastatic lesions in abdominal and pelvic malignancies: A systematic review and meta-analysis.

Purpose: The purpose of this study is to compare the application value of 68Ga-FAPI and 18F-FDG PET/CT in primary and metastatic lesions of abdominal and pelvic malignancies (APMs). Materials: The search, limited to the earliest available date of indexing through 31 July 2022, was performed on PubMed, Embase, and Cochrane Library databases using a data-specific Boolean logic search strategy. We calculated the detection rate (DR) of 68Ga-FAPI and 18F-FDG PET/CT in the primary staging and recurrence of APMs, and pooled sensitivities/specificities based on lymph nodes or distant metastases. Results: We analyzed 473 patients and 2775 lesions in the 13 studies. The DRs of 68Ga-FAPI and 18F-FDG PET/CT in evaluating the primary staging and recurrence of APMs were 0.98 (95% CI: 0.95-1.00), 0.76 (95% CI: 0.63-0.87), and 0.91(95% CI: 0.61-1.00), 0.56 (95% CI: 0.44-0.68), respectively. The DRs of 68Ga-FAPI and 18F-FDG PET/CT in primary gastric cancer and liver cancer were 0.99 (95% CI: 0.96-1.00), 0.97 (95% CI: 0.89-1.00) and 0.82 (95% CI: 0.59-0.97), 0.80 (95% CI: 0.52-0.98), respectively. The pooled sensitivities of 68Ga-FAPI and 18F-FDG PET/CT in lymph nodes or distant metastases were 0.717(95% CI: 0.698-0.735) and 0.525(95% CI: 0.505-0.546), and the pooled specificities were 0.891 (95% CI: 0.858-0.918) and 0.821(95% CI: 0.786-0.853), respectively. Conclusions: This meta-analysis concluded that 68Ga-FAPI and 18F-FDG PET/CT had a high overall diagnostic performance in detecting the primary staging and lymph nodes or distant metastases of APMs, but the detection ability of 68Ga-FAPI was significantly higher than that of 18F-FDG. However, the ability of 68Ga-FAPI to diagnose lymph node metastasis is not very satisfactory, and is significantly lower than that of distant metastasis. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022332700.

Automated ASPECTS in acute ischemic stroke: comparison of the overall scores and Hounsfield unit values of two software packages and radiologists with different levels of experience.

BACKGROUND: ASPECTS is a simple, rapid, and semi-quantitative method for detecting early ischemic changes (EIC). However, the agreement between software applications and neuroradiologists varies greatly. PURPOSE: To compare ASPECTS calculated by using automated software tools to neuroradiologists evaluation in patients with acute ischemic stroke (AIS). MATERIAL AND METHODS: Retrospectively, 61 patients with large vessel occlusion (LVO) who underwent multimodal stroke computed tomography (CT) were evaluated using two automated ASPECTS software tools (NSK and RAPID) and three neuroradiologists with different experiences (two senior neuroradiologists and one junior neuroradiologist). Four weeks later, the same three neuroradiologists re-evaluated the ASPECTS in consensus using the baseline CT and follow-up non-contrast CT (NCCT). Interclass correlation coefficients (ICCs) and Pearson correlation coefficients were applied for statistical analysis. RESULTS: The HU value exhibited the greatest correlation in the insular lobe (r = 0.81; P < 0.001) and the lowest correlation in the internal capsule (r = 0.65; P < 0.001) between NSK and RAPID. Software analysis and human readers showed excellent agreement with the consensus reading. Compared with the consensus reading, the correlation of the two senior radiologists (ICC = 0.975 and 0.969, respectively) were higher than that of junior radiologist (ICC = 0.869), and the consistency values of the NSK and RAPID software tools after 6 h of onset to imaging (ICC = 0.894 and 0.874, respectively) were greater than those within 6 h of onset (ICC = 0.746 and 0.828, respectively). CONCLUSION: For patients experiencing AIS due to LVO, the ASPECTS calculated with automated software agrees well with the predefined consensus score but is inferior to that of senior radiologists.

Detection rate of fluorine-18 prostate-specific membrane antigen-1007 PET/CT for prostate cancer in primary staging and biochemical recurrence with different serum PSA levels: A systematic review and meta-analysis.

Background: We performed a systematic review and meta-analysis to evaluate the detection rate (DR) of fluoro-prostate-specific membrane antigen (18F-PSMA-1007) PET/CT in patients with different serum prostate-specific antigen (PSA) levels in the setting of primary staging of prostate cancer (PCa) or biochemically recurring PCa. Methods: A comprehensive electronic literature search of the PubMed, Embase, and Cochrane Library databases was conducted in accordance with the PRISMA statement. This study was registered in the PROSPERO database (registration number: CRD42022331595). We calculated the DR of 18F-PSMA-1007 PET/CT in PCa. Results: The final analysis included 15 studies that described 1,022 patients and 2,034 lesions with 18F-PSMA-1007 PET/CT in PCa. The DR of 18F-PSMA-1007 PET/CT in patients with PCa in primary staging ranged from 90% to 100%, with a pooled estimate of 94% (95% CI: 92%-96%). The DR of 18F-PSMA-1007 PET/CT in patients with PCa in BCR ranged from 47% to 100%, with a pooled estimate of 86% (95% CI: 76%-95%). The DRs of PSA levels >2.0, 1.1-2.0, 0.51-1.0, and ≤0.5 ng/ml detected by 18F-PSMA-1007 PET/CT in a patient-based analysis were 97% (95% CI: 93%-99%), 95% (95% CI: 88%-99%), 79% (95% CI: 68%-88%), and 68% (95% CI: 58%-78%), respectively. Conclusion: This meta-analysis concluded that 18F-PSMA-1007 PET/CT had a high application value for prostate cancer, including primary tumors and biochemical recurrence. The DR of 18F-PSMA-1007 PET/CT was slightly higher in primary prostate tumors than in biochemical recurrence. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022331595.

Diagnostic accuracy of 18F-PSMA-1007 PET/CT for prostate cancer in primary staging and biochemical recurrence with different serum PSA levels: A systematic review and meta-analysis.

OBJECTIVE: We performed a systematic review and meta-analysis to evaluate the application value of fluorine-18-prostate specific membrane antigen (18F-PSMA-1007) positron emission tomography/computed tomography (PET/CT) in patients with different serum prostate specific antigen (PSA) levels and primary prostate cancer (PCa) or the biochemical recurrence of PCa. METHODS: A comprehensive electronic literature search of the PubMed, Embase and Cochrane Library databases was conducted in accordance with the PRISMA statement. We calculated the pooled sensitivity and specificity of 18F-PSMA-1007 PET/CT in PCa. A summary receiver operator characteristic (SROC) curve and the area under the curve (AUC) were used to assess the accuracy of 18F-PSMA-1007 PET/CT for PCa. RESULTS: The final analysis included 11 studies that described 799 patients and 4261 lesions with 18F-PSMA-1007 PET/CT in PCa. The pooled sensitivity and specificity of 18F-PSMA-1007 PET/CT in PCa were 0.836 and 0.946, respectively. The per-patient pooled sensitivity and specificity of 18F-PSMA-1007 PET/CT in PCa were 0.934 and 0.453, and the per-lesion values were 0.816 and 0.979, respectively. The pooled sensitivity and specificity of 18F-PSMA-1007 PET/CT in PCa with PSA>2ng/mL were 0.923 and 0.442 in a patient-based analysis and 0.799 and 0.961 in a lesion-based analysis, respectively. The pooled sensitivity and specificity of 18F-PSMA-1007 PET/CT in PCa with PSA≤2ng/mL were 0.832 and 0.277 in a patient-based analysis, respectively. CONCLUSION: This meta-analysis showed that 18F-PSMA-1007 PET/CT has a higher diagnostic value for prostate cancer in the setting of primary PCa and biochemical recurrence. As serum PSA levels increase, the diagnostic accuracy of 18F-PSMA-1007 PET/CT also improves.

PAM-DenseNet: A Deep Convolutional Neural Network for Computer-Aided COVID-19 Diagnosis.

Currently, several convolutional neural network (CNN)-based methods have been proposed for computer-aided COVID-19 diagnosis based on lung computed tomography (CT) scans. However, the lesions of pneumonia in CT scans have wide variations in appearances, sizes, and locations in the lung regions, and the manifestations of COVID-19 in CT scans are also similar to other types of viral pneumonia, which hinders the further improvement of CNN-based methods. Delineating infection regions manually is a solution to this issue, while excessive workload of physicians during the epidemic makes it difficult for manual delineation. In this article, we propose a CNN called dense connectivity network with parallel attention module (PAM-DenseNet), which can perform well on coarse labels without manually delineated infection regions. The parallel attention module automatically learns to strengthen informative features from both channelwise and spatialwise simultaneously, which can make the network pay more attention to the infection regions without any manual delineation. The dense connectivity structure performs feature maps reuse by introducing direct connections from previous layers to all subsequent layers, which can extract representative features from fewer CT slices. The proposed network is first trained on 3530 lung CT slices selected from 382 COVID-19 lung CT scans, 372 lung CT scans infected by other pneumonia, and 200 normal lung CT scans to obtain a pretrained model for slicewise prediction. We then apply this pretrained model to a CT scans dataset containing 94 COVID-19 CT scans, 93 other pneumonia CT scans, and 93 normal lung scans, and achieve patientwise prediction through a voting mechanism. The experimental results show that the proposed network achieves promising results with an accuracy of 94.29%, a precision of 93.75%, a sensitivity of 95.74%, and a specificity of 96.77%, which is comparable to the methods that are based on manually delineated infection regions.

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions.

Objective: To investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs). Materials and Methods: Patients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs. Results: Most of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs. Conclusions: DKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT.

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression,i.e.,from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, andF1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.

Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols. Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12-14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine-based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development. Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case. Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

MRI and 18F-FDG PET/CT findings of a giant cell tumor of the tendon sheath of the knee joint (pigmented villonodular synovitis): A case report and literature review.

We present a case of a 33-year-old female hospitalized with a 3-month history of right knee pain when squatting. Her physical examination showed no resting pain or local skin fever. Magnetic resonance imaging (MRI) showed multiple nodular long T1 and short T2 abnormal signal shadows in the popliteal fossa region. A patchy T2 high signal shadow was found in the soft tissue around the right knee.Fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) revealed multiple soft tissue density nodules around and within the right knee joint (largest 20x13mm) with a maximum standardized uptake value (SUVmax) of 10.5 and a delayed SUVmax of 12.0. The subsequent histopathologic examination confirmed the diagnosis of a diffuse giant cell tumor of the tendon sheath (GCTTS) and pigmented villonodular synovitis(PVNS).

Hippocampal subfields atrophy contribute more to cognitive impairment in middle-aged patients with type 2 diabetes rather than microvascular lesions.

AIMS: Neurodegeneration and microvascular lesions are related to cognitive impairment in type 2 diabetes mellitus (T2DM). We aimed to use the volume of hippocampal subfields and the burden of white matter hyperintensities (WMH) as neurodegeneration and microangiopathy markers, respectively, to investigate their potential associations with cognitive impairment in T2DM patients. METHODS: A total of 76 T2DM patients and 45 neurologically unimpaired normal controls were enrolled between February 2016 to August 2018. All participants underwent structural magnetic resonance imaging (MRI) and Montreal Cognitive Assessment (MoCA). The T2DM patients were divided into the T2DM without mild cognitive impairment (T2noMCI) group (n = 44) and the T2DM with mild cognitive impairment (T2MCI) group (n = 32) according to MoCA scores. We used automatic brain segmentation and quantitative technique to assess the volume of twelve hippocampal subfields and WMH on MRI. We used age, sex, education, and total intracranial volume as our covariates and the Bonferroni method for multiple comparison correction. RESULTS: Both the T2MCI group and T2noMCI group showed significant hippocampal subfields atrophy compared to the controls, which were mainly in the left hippocampal tail, left CA1, bilateral molecular layer, bilateral dentate gyrus, and bilateral CA4 (all p < 0.0042). No significant differences in the volume of total WMH, deep-WMH, and periventricular-WMH were found among the three groups. The HbA1c levels were significantly negatively correlated with hippocampal atrophy, and the MoCA scores were positively correlated with bilateral hippocampal volume in T2DM patients and all samples. Mediation analyses demonstrated that the association of HbA1c levels with cognitive function was mediated by hippocampal subfields volume. CONCLUSION: Widespread hippocampal atrophies across the subfields have been found in middle-aged T2DM patients, which was positively correlated with the MoCA scores and negatively correlated with the HbA1c levels. The association of HbA1c levels with cognitive function was mediated by some crucial hippocampal subfields volume. In middle-aged patients with T2DM, the neurodegeneration is more strongly associated with cognitive impairment than microvascular lesions. Trail Registeration This study was registered on Clinical-Trails.gov (NCT02738671).

CT imaging features of different clinical types of COVID-19 calculated by AI system: a Chinese multicenter study.

BACKGROUND: The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system. METHODS: A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed. RESULTS: It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups. CONCLUSIONS: Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.

CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19.

OBJECTIVE: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. RESULTS: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. CONCLUSION: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

CT imaging changes of corona virus disease 2019(COVID-19): a multi-center study in Southwest China.

BACKGROUND: Since the first case of a coronavirus disease 2019 (COVID-19) infection pneumonia was detected in Wuhan, China, a series of confirmed cases of the COVID-19 were found in Southwest China. The aim of this study was to describe the imaging manifestations of hospitalized patients with confirmed COVID-19 infection in southwest China. METHODS: In this retrospective study, data were collected from 131 patients with confirmed coronavirus disease 2019 (COVID-19) from 3 Chinese hospitals. Their common clinical manifestations, as well as characteristics and evolvement features of chest CT images, were analyzed. RESULTS: A total of 100 (76%) patients had a history of close contact with people living in Wuhan, Hubei. The clinical manifestations of COVID-19 included cough, fever. Most of the lesions identified in chest CT images were multiple lesions of bilateral lungs, lesions were more localized in the peripheral lung, 109 (83%) patients had more than two lobes involved, 20 (15%) patients presented with patchy ground glass opacities, patchy ground glass opacities and consolidation of lesions co-existing in 61 (47%) cases. Complications such as pleural thickening, hydrothorax, pericardial effusion, and enlarged mediastinal lymph nodes were detected but only in rare cases. For the follow-up chest CT examinations (91 cases), We found 66 (73%) cases changed very quickly, with an average of 3.5 days, 25 cases (27%) presented absorbed lesions, progression was observed in 41 cases (46%), 25 (27%) cases showed no significant changes. CONCLUSION: Chest CT plays an important role in diagnosing COVID-19. The imaging pattern of multifocal peripheral ground glass or mixed consolidation is highly suspicious of COVID-19, that can quickly change over a short period of time.

Coronavirus disease 2019: initial chest CT findings.

OBJECTIVES: To systematically analyze CT findings during the early and progressive stages of natural course of coronavirus disease 2019 and also to explore possible changes in pulmonary parenchymal abnormalities during these two stages. METHODS: We retrospectively reviewed the initial chest CT data of 62 confirmed coronavirus disease 2019 patients (34 men, 28 women; age range 20-91 years old) who did not receive any antiviral treatment between January 21 and February 4, 2020, in Chongqing, China. Patients were assigned to the early-stage group (onset of symptoms within 4 days) or progressive-stage group (onset of symptoms within 4-7 days) for analysis. CT characteristics and the distribution, size, and CT score of pulmonary parenchymal abnormalities were assessed. RESULTS: In our study, the major characteristic of coronavirus disease 2019 was ground-glass opacity (61.3%), followed by ground-glass opacity with consolidation (35.5%), rounded opacities (25.8%), a crazy-paving pattern (25.8%), and an air bronchogram (22.6%). No patient presented cavitation, a reticular pattern, or bronchial wall thickening. The CT scores of the progressive-stage group were significantly greater than those of the early-stage group (p = 0.004). CONCLUSIONS: Multiple ground-glass opacities with consolidations in the periphery of the lungs were the primary CT characteristic of coronavirus disease 2019. CT score can be used to evaluate the severity of the disease. If these typical alterations are found, then the differential diagnosis of coronavirus disease 2019 must be considered. KEY POINTS: • Multiple GGOs with consolidations in the periphery of the lungs were the primary CT characteristic of COVID-19. • The halo sign may be a special CT feature in the early-stage COVID-19 patients. • Significantly increased CT score may indicate the aggravation of COVID-19 in the progressive stage.

The Value of Whole-Brain Perfusion Parameters Combined with Multiphase Computed Tomography Angiography in Predicting Hemorrhagic Transformation in Ischemic Stroke.

OBJECTIVE: To explore the value of whole-brain perfusion parameters combined with multiphase computed tomography angiography (MP-CTA) in predicting the hemorrhagic transformation (HT) of ischemic stroke. METHODS: A total of 64 patients with ischemic stroke who underwent noncontrast computed tomography, computed tomography perfusion imaging, and computed tomography angiography before treatment from August 2017 to June 2019 were included retrospectively. The perfusion parameters cerebral blood volume (CBV), cerebral blood flow (CBF), time to peak (TTP), mean transit time (MTT), time to maximum (Tmax), and permeability surface (PS) were measured by postprocessing software (Advantage Workstation 4.7 (Revolution, GE Healthcare, USA)), and their ratios between the healthy and affect side relative CBV, relative CBF, relative time to peak (rTTP), relative mean transit time (rMTT), relative Tmax, and relative permeability surface (rPS) were calculated. The differences in perfusion parameters between the HT group and the non-HT group were evaluated. The collateral circulation scores and HT rates were assessed by MP-CTA. Receiver operating characteristic curves were drawn to analyze the diagnostic efficiency of valuable parameters and their correlations with HT. The rate of HT in different treatments were compared. RESULTS: The CBV values in the HT group were lower than those in the non-HT group (P < .05), while the TTP, MTT, Tmax, PS, rTTP, rMTT, and rPS values in the HT group were higher than those in the non-HT group (P < .05). PS (r = .63, area under curve = .881) and rPS (r = .52, area under curve = .814) were significantly correlated with HT. The combination of perfusion parameters and the MP-CTA scores can improve the diagnostic efficiency (area under curve = .891). The HT rate in the group with poor collateral (64.29%) was higher than that in the group with good collateral (11.11%). CONCLUSIONS: Whole-brain perfusion parameters and MP-CTA scores have important application value in assessing the HT risk of ischemic stroke patients before treatment.

Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features.

OBJECTIVES: The aim of this study was to investigate the chest computed tomography (CT) findings in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate its relationship with clinical features. MATERIALS AND METHODS: Study sample consisted of 80 patients diagnosed as COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, and the relationship between them was analyzed. RESULTS: Totally, 80 patients diagnosed with COVID-19 were included. With regards to the clinical manifestations, 58 (73%) of the 80 patients had cough, and 61 (76%) of the 80 patients had high temperature levels. The most frequent CT abnormalities observed were ground glass opacity (73/80 cases, 91%), consolidation (50/80 cases, 63%), and interlobular septal thickening (47/80, 59%). Most of the lesions were multiple, with an average of 12 ± 6 lung segments involved. The most common involved lung segments were the dorsal segment of the right lower lobe (69/80, 86%), the posterior basal segment of the right lower lobe (68/80, 85%), the lateral basal segment of the right lower lobe (64/80, 80%), the dorsal segment of the left lower lobe (61/80, 76%), and the posterior basal segment of the left lower lobe (65/80, 81%). The average pulmonary inflammation index value was (34% ± 20%) for all the patients. Correlation analysis showed that the pulmonary inflammation index value was significantly correlated with the values of lymphocyte count, monocyte count, C-reactive protein, procalcitonin, days from illness onset, and body temperature (P < 0.05). CONCLUSIONS: The common chest CT findings of COVID-19 are multiple ground glass opacity, consolidation, and interlobular septal thickening in both lungs, which are mostly distributed under the pleura. There are significant correlations between the degree of pulmonary inflammation and the main clinical symptoms and laboratory results. Computed tomography plays an important role in the diagnosis and evaluation of this emerging global health emergency.

Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19.

PURPOSE: To evaluate the value of chest CT severity score (CT-SS) in differentiating clinical forms of coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A total of 102 patients with COVID-19 confirmed by a positive result from real-time reverse transcription polymerase chain reaction on throat swabs who underwent chest CT (53 men and 49 women, 15-79 years old, 84 cases with mild and 18 cases with severe disease) were included in the study. The CT-SS was defined by summing up individual scores from 20 lung regions; scores of 0, 1, and 2 were respectively assigned for each region if parenchymal opacification involved 0%, less than 50%, or equal to or more than 50% of each region (theoretic range of CT-SS from 0 to 40). The clinical and laboratory data were collected, and patients were clinically subdivided according to disease severity according to the Chinese National Health Commission guidelines. RESULTS: The posterior segment of upper lobe (left, 68 of 102; right, 68 of 102), superior segment of lower lobe (left, 79 of 102; right, 79 of 102), lateral basal segment (left, 79 of 102; right, 70 of 102), and posterior basal segment of lower lobe (left, 81 of 102; right, 83 of 102) were the most frequently involved sites in COVID-19. Lung opacification mainly involved the lower lobes, in comparison with middle-upper lobes. No significant differences in distribution of the disease were seen between right and left lungs. The individual scores in each lung and the total CT-SS were higher in severe COVID-19 when compared with mild cases (P < .05). The optimal CT-SS threshold for identifying severe COVID-19 was 19.5 (area under curve = 0.892), with 83.3% sensitivity and 94% specificity. CONCLUSION: The CT-SS could be used to evaluate the severity of pulmonary involvement quickly and objectively in patients with COVID-19.© RSNA, 2020.