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10.1177/0284185121994695 http://scihub22266oqcxt.onion/10.1177/0284185121994695 33601893!ä!33601893 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33601893&cmd=llinks): Failed to open stream: HTTP request failed! 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Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19) |pdf=|usr=}}{{33601893}} gab.com Text Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19) JO: Acta Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33601893 #FOAvip BACKGROUND: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses. Twit Text FOAVip Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19) ????Acta Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33601893 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33601893 #FOAvip English Wikipedia <ref>{{Cite pmid|33601893}}</ref> Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19) #MMPMID33601893 Ke Z; Li L; Wang L; Liu H; Lu X; Zeng F; Zha Y Acta Radiol 2022[Mar]; 63 (3): 319-327 PMID33601893show ga BACKGROUND: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses. |*Inpatients[MESH] |*Nomograms[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnostic imaging/*mortality[MESH] |Confidence Intervals[MESH] |Datasets as Topic[MESH] |Humans[MESH] |Proportional Hazards Models[MESH] |Retrospective Studies[MESH]Radiomics analysis enables fatal outcome prediction for hospitalized p(epmc).pdf DeepDyve Pubget Overpricing