METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.
Methods: Infectivity, pathogenicity, and real-time dissemination of aerosolized NiV in Syrian hamsters was evaluated using NiV-Malaysia (NiV-M) and/or its recombinant expressing firefly luciferase (rNiV-FlucNP).
Results: Both viruses had an equivalent pathogenicity in hamsters, which developed respiratory and neurological symptoms of disease, similar to using intranasal route, with no direct correlations to the dose. We showed that virus replication was predominantly initiated in the lower respiratory tract and, although delayed, also intensely in the oronasal cavity and possibly the brain, with gradual increase of signal in these regions until at least day 5-6 postinfection.
Conclusion: Hamsters infected with small-particle aerosolized NiV undergo similar clinical manifestations of the disease as previously described using liquid inoculum, and exhibit histopathological lesions consistent with NiV patient reports. NiV droplets could therefore play a role in transmission by close contact.
METHODS: EMPOWER-Lung 1 was a multicentre, open-label, randomised, phase 3 trial. We enrolled patients (aged ≥18 years) with histologically confirmed squamous or non-squamous advanced non-small-cell lung cancer with PD-L1 tumour expression of 50% or more. We randomly assigned (1:1) patients to intravenous cemiplimab 350 mg every 3 weeks for up to 108 weeks, or until disease progression, or investigator's choice of chemotherapy. Central randomisation scheme generated by an interactive web response system governed the randomisation process that was stratified by histology and geographical region. Primary endpoints were overall survival and progression free survival, as assessed by a blinded independent central review (BICR) per Response Evaluation Criteria in Solid Tumours version 1.1. Patients with disease progression on cemiplimab could continue cemiplimab with the addition of up to four cycles of chemotherapy. We assessed response in these patients by BICR against a new baseline, defined as the last scan before chemotherapy initiation. The primary endpoints were assessed in all randomly assigned participants (ie, intention-to-treat population) and in those with a PD-L1 expression of at least 50%. We assessed adverse events in all patients who received at least one dose of their assigned treatment. This trial is registered with ClinicalTrials.gov, NCT03088540.
FINDINGS: Between May 29, 2017, and March 4, 2020, we recruited 712 patients (607 [85%] were male and 105 [15%] were female). We randomly assigned 357 (50%) to cemiplimab and 355 (50%) to chemotherapy. 284 (50%) patients assigned to cemiplimab and 281 (50%) assigned to chemotherapy had verified PD-L1 expression of at least 50%. At 35 months' follow-up, among those with a verified PD-L1 expression of at least 50% median overall survival in the cemiplimab group was 26·1 months (95% CI 22·1-31·8; 149 [52%] of 284 died) versus 13·3 months (10·5-16·2; 188 [67%] of 281 died) in the chemotherapy group (hazard ratio [HR] 0·57, 95% CI 0·46-0·71; p<0·0001), median progression-free survival was 8·1 months (95% CI 6·2-8·8; 214 events occurred) in the cemiplimab group versus 5·3 months (4·3-6·1; 236 events occurred) in the chemotherapy group (HR 0·51, 95% CI 0·42-0·62; p<0·0001). Continued cemiplimab plus chemotherapy as second-line therapy (n=64) resulted in a median progression-free survival of 6·6 months (6·1-9·3) and overall survival of 15·1 months (11·3-18·7). The most common grade 3-4 treatment-emergent adverse events were anaemia (15 [4%] of 356 patients in the cemiplimab group vs 60 [17%] of 343 in the control group), neutropenia (three [1%] vs 35 [10%]), and pneumonia (18 [5%] vs 13 [4%]). Treatment-related deaths occurred in ten (3%) of 356 patients treated with cemiplimab (due to autoimmune myocarditis, cardiac failure, cardio-respiratory arrest, cardiopulmonary failure, septic shock, tumour hyperprogression, nephritis, respiratory failure, [n=1 each] and general disorders or unknown [n=2]) and in seven (2%) of 343 patients treated with chemotherapy (due to pneumonia and pulmonary embolism [n=2 each], and cardiac arrest, lung abscess, and myocardial infarction [n=1 each]). The safety profile of cemiplimab at 35 months, and of continued cemiplimab plus chemotherapy, was generally consistent with that previously observed for these treatments, with no new safety signals INTERPRETATION: At 35 months' follow-up, the survival benefit of cemiplimab for patients with advanced non-small-cell lung cancer was at least as pronounced as at 1 year, affirming its use as first-line monotherapy for this population. Adding chemotherapy to cemiplimab at progression might provide a new second-line treatment for patients with advanced non-small-cell lung cancer.
FUNDING: Regeneron Pharmaceuticals and Sanofi.
MATERIALS AND METHODS: We retrospectively enrolled patients who underwent the procedure from January 2018 to April 2022. Under real time ultrasound (Hitachi Medical ProSound F37), thoracic lesions adjacent to the chest wall were sampled with a full-core biopsy needle (CT Core Single Action Biopsy Device, 18G × 15 cm, Vigeo, Italy). Chest x-ray was performed 30 minutes post procedure ruling out pneumothorax. Patients were discharged home 1-2 hours post biopsy. Data was analysed using Microsoft Excel 2010 and Statistical Package for Social Science (SPSS) Version 26.
RESULTS: A total of 18 patients (14 males, 4 females) underwent USLB for lung tumours. Biopsies were histologically deemed adequate with an overall diagnostic yield of 77.8% (14/18). A total of 57% were positive for thoracic malignancy (21% squamous cell carcinoma, 21% adenocarcinoma, 15% small cell carcinoma) and another 43% were positive for extra thoracic malignancy (1 hepatocellular carcinoma, 2 DLBCL, 1 Hodgkin's lymphoma, 1 seminoma, 1 thymoma). Four patients had inconclusive results but managed to get positive results from surgical or lymph node biopsy (thymoma and adenocarcinoma). Statistical analysis showed more than two passes are needed to achieve a positive HPE yield (p value<0.05). There were nil complications to all the cases done.
CONCLUSIONS: USLB can safely and effectively be performed by trained pulmonologists with excellent accuracy and low complication rate in outpatients.
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.