LitCovid-sentences  

PMC:7160614 / 25992-30476 JSONTXT 14 Projects

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Id Subject Object Predicate Lexical cue
T287 0-10 Sentence denotes Discussion
T288 11-176 Sentence denotes In this multi-center study, statistical analysis was performed in comparing imaging and clinical manifestations between pneumonia patients with and without COVID-19.
T289 177-329 Sentence denotes Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different between the two groups (p < 0.05).
T290 330-411 Sentence denotes Three models for COVID-19 diagnosis were developed based on the refined features.
T291 412-518 Sentence denotes The models were validated in the both primary and validation cohorts and achieved an AUC as high as 0.986.
T292 519-710 Sentence denotes These models will play an essential role for early and easy-to-access diagnosis, especially when there are not enough RT-PCT kits or experimental platforms to test for the COVID-19 infection.
T293 711-812 Sentence denotes A total of 1745 lesions were evaluated for the qualitative feature, location, and size in this study.
T294 813-1006 Sentence denotes Consistent with the previous studies, the ground-glass opacities and consolidation in the lung periphery were considered to be the imaging hallmark in patients with COVID-19 infection [11, 25].
T295 1007-1150 Sentence denotes However, when we subdivided the GGO into pure GGO and mixed GGO, we found that the distribution pattern is different between these two lesions.
T296 1151-1313 Sentence denotes Pure GGO show differences between groups in every location of the lungs, whereas mixed GGO only have significant differences between groups in the lung periphery.
T297 1314-1386 Sentence denotes Recent studies defined four stages of lung involvement in COVID-19 [26].
T298 1387-1463 Sentence denotes Therefore, a follow-up analysis of these distributions would be significant.
T299 1464-1552 Sentence denotes The lesion size in patients with COVID-19 infection was another interesting observation.
T300 1553-1696 Sentence denotes Most lesions were between 1 and 3 cm, with few lesions larger than half of the lung segment, which was similar to the finding in MERS_CoV [22].
T301 1697-1881 Sentence denotes Other features similar to MERS_CoV and SARS_CoV were observed in the laboratory abnormalities, such as lymphopenia, which may be associated with the cellular immune deficiency [3, 27].
T302 1882-1998 Sentence denotes However, our results showed no significant difference in lymphopenia between the COVID-19 and non-COVID-19 patients.
T303 1999-2130 Sentence denotes To our knowledge, no diagnostic model based on imaging and clinical features alone has been proposed for the diagnosis of COVID-19.
T304 2131-2436 Sentence denotes Our clinical and radiological semantic (CR) models consisted of the following features: total number of GGO with consolidation in the peripheral area, tree-in-bud, offending vessel augmentation in lesions, temperature, heart ratio, respiration, cough and fatigue, WBC count, and lymphocyte count category.
T305 2437-2508 Sentence denotes The CR model outperformed the individual clinical and radiologic model.
T306 2509-2708 Sentence denotes This result was in accordance with that in previous study in breast cancer, in which the model based on the combination of radiomics features and clinical features achieved a higher performance [24].
T307 2709-2902 Sentence denotes Compared with the radiomics-based model, the extraction of radiological semantic features can overcome the image discrepancy caused by different scanning parameters and/or different CT vendors.
T308 2903-3099 Sentence denotes A previous study [28] also indicated that models based on semantic features determined by an experienced thoracic radiologist slightly outperformed models based on computed texture features alone.
T309 3100-3142 Sentence denotes There are a few limitations in this study.
T310 3143-3301 Sentence denotes First, the sample size is relatively small because this is a retrospective analysis of a new disease and most of the cases outside of Wuhan City are imported.
T311 3302-3619 Sentence denotes Second, with the multi-center retrospective design, there is a potential bias of patient selection [29], since there may be some deviations in marking semantic features among readers, though we have taken the effort to reduce this by creating pictorial examples and setting feature criteria (Supplementary Materials).
T312 3620-3667 Sentence denotes Third, longitudinal CT study was not performed.
T313 3668-3807 Sentence denotes Whether or not this model can be used to evaluate the follow-ups and help to guide therapy remains an open question to be further explored.
T314 3808-3991 Sentence denotes Moreover, the rich high-order features of the CT image combined with radiomics or deep learning have not been studied, which may be another way to identify the patients with COVID-19.
T315 3992-4127 Sentence denotes Besides, one can also focus on the role of radiological features in disease monitoring, treatment evaluation, and prognosis prediction.
T316 4128-4239 Sentence denotes In conclusion, 1745 lesions and 67 features were compared between pneumonia patients with and without COVID-19.
T317 4240-4313 Sentence denotes Thirty-five features were significantly different between the two groups.
T318 4314-4484 Sentence denotes A diagnostic model with AUC as high as 0.986 was developed and validated both in the primary and in the validation cohorts, which may help improve the COVID-19 diagnosis.