A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage

<div>Texture feature selection using the least absolute shrinkage and selection operator (LASSO) method. (a) The optimal tuning parameter (<i>λ</i>) was selected using 10-fold cross-validation in the LASSO regression model; (b) LASSO regression coefficient distribution; (c) optimal radiomics feature combination and its correlation coefficient.</div>

Computational Intelligence and Neuroscience

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Figure 3: A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage 