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10.1186/s12967-020-02692-3 http://scihub22266oqcxt.onion/10.1186/s12967-020-02692-3 33413480!7790050!33413480     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Transl+Med 2021 ; 19 (1): 29 Nephropedia Template TP {{tp|p=33413480|t=2021. CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS |pdf=|usr=}}{{33413480}} gab.com Text CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS JO: J Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=33413480 #FOAvip BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection. Twit Text FOAVip CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS ????J Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=33413480 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33413480 #FOAvip English Wikipedia <ref>{{Cite pmid|33413480}}</ref> CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS #MMPMID33413480 Liu H; Ren H; Wu Z; Xu H; Zhang S; Li J; Hou L; Chi R; Zheng H; Chen Y; Duan S; Li H; Xie Z; Wang D J Transl Med 2021[Jan]; 19 (1): 29 PMID33413480show ga BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection. |*SARS-CoV-2[MESH] |Adult[MESH] |Aged[MESH] |COVID-19 Testing/*methods/statistics & numerical data[MESH] |COVID-19/*diagnosis/*diagnostic imaging/epidemiology[MESH] |China/epidemiology[MESH] |Female[MESH] |High-Throughput Screening Assays/methods/statistics & numerical data[MESH] |Humans[MESH] |Machine Learning[MESH] |Male[MESH] |Middle Aged[MESH] |Models, Statistical[MESH] |Nomograms[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/*diagnostic imaging/epidemiology[MESH] |Radiographic Image Interpretation, Computer-Assisted/methods/statistics & numerical data[MESH] |Retrospective Studies[MESH] |Sensitivity and Specificity[MESH] |Tomography, X-Ray Computed/*methods/statistics & numerical data[MESH]CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia(epmc).pdf DeepDyve Pubget Overpricing