Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

[21] Luitse MJ(2018)Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement Eur Radiol 28 4389-98

[22] Rinkel GJ(2020)Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model Eur Radiol 30 87-15

[23] van der Tweel I(2019)Radiomics for predicting hematoma expansion in patients with hypertensive intraparenchymal hematomas Eur J Radiol 115 10-163

[24] Algra A(2016)A guideline of selecting and reporting intraclass correlation coefficients for reliability research J Chiropr Med 15 155-845

[25] Klijn CJ(1988)Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach Biometrics 44 837-574

[26] Brouwers HB(2006)Decision curve analysis: a novel method for evaluating prediction models Med Decis Making 26 565-1144

[27] Chang Y(2011)Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies BMC Med Inform Decis Mak 11 45-348

[28] Falcone GJ(2018)Accuracy of spot sign in predicting hematoma expansion and clinical outcome: A meta-analysis Medicine (Baltimore) 97 e11945-undefined

[29] Cai X(2013)Quantitative CT densitometry for predicting intracerebral hemorrhage growth AJNR Am J Neuroradiol 34 1139-undefined

[30] Ayres AM(2013)Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82 342-undefined

[21] Luitse MJ(2018)Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement Eur Radiol 28 4389-98

[22] Rinkel GJ(2020)Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model Eur Radiol 30 87-15

[23] van der Tweel I(2019)Radiomics for predicting hematoma expansion in patients with hypertensive intraparenchymal hematomas Eur J Radiol 115 10-163

[24] Algra A(2016)A guideline of selecting and reporting intraclass correlation coefficients for reliability research J Chiropr Med 15 155-845

[25] Klijn CJ(1988)Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach Biometrics 44 837-574

[26] Brouwers HB(2006)Decision curve analysis: a novel method for evaluating prediction models Med Decis Making 26 565-1144

[27] Chang Y(2011)Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies BMC Med Inform Decis Mak 11 45-348

[28] Falcone GJ(2018)Accuracy of spot sign in predicting hematoma expansion and clinical outcome: A meta-analysis Medicine (Baltimore) 97 e11945-undefined

[29] Cai X(2013)Quantitative CT densitometry for predicting intracerebral hemorrhage growth AJNR Am J Neuroradiol 34 1139-undefined

[30] Ayres AM(2013)Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82 342-undefined