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10.1016/j.compbiomed.2021.104304 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104304 33691201!7925235!33691201     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33691201&cmd=llinks): Failed to open stream: HTTP request failed! 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Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients |pdf=|usr=}}{{33691201}} gab.com Text Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33691201 #FOAvip OBJECTIVE: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. METHODS: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. RESULTS: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 +/- 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 +/- 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 +/- 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 +/- 0.07 (95% CI = 0.87-0.9)). CONCLUSION: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. Twit Text FOAVip Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33691201 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33691201 #FOAvip English Wikipedia <ref>{{Cite pmid|33691201}}</ref> Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients #MMPMID33691201 Shiri I; Sorouri M; Geramifar P; Nazari M; Abdollahi M; Salimi Y; Khosravi B; Askari D; Aghaghazvini L; Hajianfar G; Kasaeian A; Abdollahi H; Arabi H; Rahmim A; Radmard AR; Zaidi H Comput Biol Med 2021[May]; 132 (ä): 104304 PMID33691201show ga OBJECTIVE: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. METHODS: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. RESULTS: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 +/- 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 +/- 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 +/- 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 +/- 0.07 (95% CI = 0.87-0.9)). CONCLUSION: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. |*COVID-19[MESH] |Humans[MESH] |Machine Learning[MESH] |Prognosis[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Machine learning-based prognostic modeling using clinical data and qua(epmc).pdf DeepDyve Pubget Overpricing